The reflexive response from the IT industry to events like this is to recommend more software purchases. Observability layers. Real-time dependency graphs. Automated remediation engines. Each of those tools has merit. None of them is the actual lesson. The Canvas event is not fundamentally a problem to be solved with another platform. It is a problem revealed by leadership practice, communication discipline, and contractual rigor, or the absence of those things, on the campuses that experienced it.

It is also reportedly not an isolated event. Reporting suggests this attack is part of a broader pattern affecting other education software providers. Higher education and K-12 SaaS platforms appear to be a target. That pattern, if it holds, matters more than the identity of any single vendor, and it shapes the lessons leaders should draw.

The Three Phases of Any Incident

Every incident, whether technological or otherwise, moves through three phases. The first is to protect: contain the exposure and prevent further harm. The second is to resume: restore business operations cleanly enough for the institution to continue functioning. The third is to assess and learn: conduct a real after-action review, identify what failed structurally, and update the practices that led to the failure.

In a cloud-hosted environment, the protect and resume phases largely fall to the vendor. Instructure had to pull its platform offline, revoke privileged credentials, rotate keys, remediate the underlying issue, and restore service. The institution’s role during those phases is narrower but no less. Communicate with the community. Stand up continuity arrangements where they exist. Coordinate with peers. Hold the academic enterprise together while the technical work happens out of view.

The third phase is where institutional learning either happens or doesn’t. It is the work that distinguishes campuses that emerge stronger from each disruption from those that quietly normalize the most recent one. Most universities are starting that phase now. Some will treat the after-action seriously. Others will let the moment pass.

What Technical Work the Campus Still Owes

Of course, there is real technical work on campus, even when the breach itself happened in the cloud. Identity and integration are the seams where vendor compromise becomes institutional compromise, and those seams sit on campus.

Service account passwords, especially those without multifactor authentication, should be rotated following any vendor incident of this scale. Connections between Canvas and identity providers, the SIS, grading workflows, and downstream analytics should be examined and, where appropriate, revoked and reissued. On-campus authentication and SSO logs should be reviewed for unusual access patterns, unfamiliar source addresses, or newly created administrative accounts that suggest harvested credentials are being used for lateral movement. The systems integrated with Canvas are part of the blast radius, even if they were not breached directly.

There is also the human-facing dimension. Stolen internal data allows attackers to construct phishing campaigns that reference real work, real relationships, and real internal context. These attempts can be difficult to distinguish from legitimate emails. Communities need to be warned, and IT teams should anticipate a sustained period of targeted phishing attacks rather than a single, quickly passing wave.

This work is necessary. It is not, by itself, sufficient. A campus that completes the technical checklist but fails to communicate clearly with its community, or fails to update its continuity plans and its contracts, has remediated the incident without learning from it. Technical work is part of the response. The leadership practice around it determines whether the response actually adds capability for next time.

Not a Product Problem, and Not a SaaS Problem

Within the University System of Georgia, only one institution uses Canvas as its core LMS. The others use a different platform. That detail matters less than it might seem. Anything connected to the Internet carries cyber risk, and a sophisticated threat actor moving systematically will find its way to whichever platform a campus has chosen. Treating this as a Canvas problem misreads the structural condition. The next breach may well be at a different vendor. Vendor selection is not the lesson.

The deeper version of that misreading is the argument that begins to circulate after every SaaS breach: that institutions should pull software back into their own data centers where it can be controlled. That is a false comfort. Vendors at scale invest in security, continuously monitor their platforms, respond to threats with dedicated teams, and absorb the cost of remediation across thousands of customers. A campus running its LMS on local infrastructure would face the same threat surface with a fraction of the resources and a longer recovery curve. Outsourcing risk through contracts remains prudent. In the Internet-connected age, all software is vulnerable; the question is which arrangement makes that vulnerability most manageable.

There is, however, a related question worth asking of every SaaS vendor going forward. What new capability has been added to the platform in the past twelve months, and how have the integration points and data pathways changed as a result? Feature velocity is not free. New ways into a system are also new ways out, if attackers get in. That is a question for the vendor relationship, not a reason to abandon SaaS.

What the After-Action Should Actually Produce

Three questions deserve serious attention from every campus that runs Canvas, and from every campus that operates any mission-critical technology services.

The first is continuity. What was the plan for an LMS outage during finals week? Most institutions discovered during this event that their business continuity plans assumed the LMS would be available. When both the gradebook and the exam-delivery mechanism go offline at the end of a semester, the academic enterprise has very few viable options. Faculty improvise. Deans extend deadlines. Registrars absorb the chaos. None of that is a plan. A real continuity plan names the alternative workflows, identifies the people authorized to invoke them, and is exercised before it is needed. After-action work should produce continuity plans that account for losing the LMS at the worst possible moment, because that is when it will be lost.

The second is contractual. What do the institution’s data processing addenda with Instructure actually require? Specific questions matter here, and general counsel should be in the room when they are explored. Is the vendor obligated to share root cause findings promptly? Does the contract require the vendor to bear the costs of community notification and credit monitoring when notification is legally required? Does it commit the vendor to disclose its remediation actions and its plan to prevent recurrence? Does the institution have any contractual authority to shape how the vendor notifies students and faculty in response? For most campuses, the honest answer is that these terms are weaker than they should be, because the contract was negotiated for price and feature parity rather than for incident response. The Canvas event is the moment to read those agreements closely and to use what is learned to shape future negotiations across every cloud platform on campus.

The third is observational, and it applies whether or not a campus runs Canvas. Watch Instructure's response carefully. As attack vectors and root causes are identified, use those findings as a lens to red-team every technology service the institution operates or depends on. Apply the lessons regardless of whether the campus was directly affected. The cost of this exercise is small. The benefit is that an institution arrives at its next vendor incident having already understood the structural conditions that produced this one. The campuses that treat someone else's published root cause as a preview of their own next event are the ones that learn from it most cheaply.

Communication Is the Actual Incident Response

In a SaaS world, the most important IT incident response discipline is not technical. It is communication with the broader user community. The vendor controls the platform. The campus controls the message. Letting the community know what is happening, how it affects them, and what continuity arrangements are available is the single most consequential thing an IT organization does during an event of this kind.

That communication has a legal dimension as well as a cultural one. In many jurisdictions, an institution’s notification obligations can begin once it has reason to believe a breach has occurred, often before a vendor’s formal confirmation. Working with general counsel on those obligations is part of the early response, not an after-the-fact courtesy. Beyond the legal floor, communication has to be clear, repeated often, and willing to say “we don’t know” when that is the truth. Vague reassurances erode trust faster than bad news. Communities can absorb difficult information delivered honestly. They cannot absorb silence or spin. Campuses that communicated well during the Canvas event, in plain language, with regular updates, with realistic expectations about timing, will have built credibility that outlasts the moment. Campuses that communicated poorly will discover the cost in the next disruption.

The final word

A breach like this one tells leaders quickly whether their teams have built the habit of sweating the small details together. A strong response is rarely a single decisive act. It is a collection of many small things, done in sequence, by people who trust each other enough to coordinate without ceremony. Service accounts rotated. Logs reviewed honestly. Communication that is timely and plain. Continuity arrangements that have been exercised. Data processing addenda read with the next breach in mind. Lessons from other vendors’ incidents absorbed before the next one arrives. After-action reviews that produce real changes, not slides for the next leadership meeting.

No observability platform builds that culture. No automated remediation engine substitutes for it. The technology will keep evolving. The threat actors will keep working through the sector. What separates the campuses that handle the next event well from those that don’t will be the slow, unflashy work of leadership. That is the lesson the Canvas event offers, and it is the only lesson worth taking from it.

Long before social media, the media industry discovered that outrage outperforms deliberation. Cable news and the confrontational programming of the 1990s built audiences not on conversation but on conflict, performed loudly and repeatedly. The formula worked across the political spectrum. And when the Internet arrived, it did not invent this dynamic. It inherited it, scaled it, and put it in everyone’s pocket.

What the Internet added was not a new idea but a new scale. It took the outrage model that cable and talk radio had proven out, stripped away the last remaining gatekeepers, and handed the formula to every individual with a connection. In today’s Dispatch, I unpack how that shift perfected zero-sum posturing and slowly eroded our capacity for relational capital, leaving everyone more anxious, more lonely, and less able to find common ground with people who see the world differently.

The big picture

We live in a zero-sum world. The failure of compromise is observable everywhere; policy advocates cannot seek consensus because consensus has become synonymous with betrayal. This public breakdown is paralleled by a deep private crisis. Research confirms soaring rates of anxiety and loneliness, especially among younger adults. These are not coincidental conditions. The loneliness and the polarization are not separate problems. They are the same problem expressed at different scales, and they share a single structural root: the collapse of relational competence.

Negotiation is the deliberate practice of managing constraints and seeking alignment with people whose interests differ from your own. It is not a business skill or a diplomatic technique reserved for formal settings. It is a foundational human competence, the mechanism through which individuals, institutions, and communities resolve conflict without resorting to force or withdrawal.

That competence is eroding. The zero-sum posture that the media industry has normalized now too often governs the relationship between employees and the managers who lead them, and between organizations and the communities around them. Maximalist demands have replaced opening positions. Extreme anchors are no longer strategic bids. They have become sincere expressions of what people believe they deserve. This is not primarily a failure of character. It is a failure of culture, driven by structural forces that most people experience without being able to name.

The Three Shifts that Broke the Bargain

The technology did not install new human tendencies. It allowed existing ones to scale in ways they never could before, in three distinct waves.

The first came in 1993. The early Internet offered direct connection, bypassing the institutions that had set the norms for conflict and debate. Newspapers, civic groups, academic institutions, and professional bodies were imperfect mediating structures, and not always trusted ones, but they enforced shared scripts for acceptable disagreement. When they became optional, accountability and shared social context disappeared, replaced by speed, volume, and the ability to find an audience that always agreed with you. The cost of extreme anchors dropped to nearly zero.

The second shift came in 2007. The smartphone put the Internet in our pocket and made digital confrontation continuous. It replaced face-to-face engagement with compulsive scrolling and reacting, rewarding emotional reactivity, and the instant counter-assertion. The deliberate pause that good negotiation requires, the moment of sitting with another person’s position before responding to it, became not just inconvenient but actively punished by social networks that measure silence as disengagement. Every interaction became a potential public performance, and every concession became visible to an audience trained to read it as weakness.

The third shift came in 2022. Machine learning models personalized the noise. Algorithms now determine what people see, hear, and read, reinforcing existing beliefs and reducing complex realities into oppositional camps. What these three shifts produced together was not simply a coarser public culture. They changed the conditions under which people form beliefs, and by doing so, they changed what people bring to every potential collaboration before the first word is spoken.

The Problem of Digital Certainty

The most consequential product of these three shifts is digital certainty: the manufactured sense of being correct, delivered not through earned authority or shared deliberation, but through algorithmic reinforcement. The algorithm is not confirming your position because it is true. It is confirming it because your confirmation is profitable. The result is not genuine conviction. It is a simulation of certainty, continuously renewed, and optimized for engagement rather than accuracy.

Digital certainty has a behavioral signature that senior leaders will recognize immediately in any consequential conversation. It is the refusal to acknowledge shared constraints. It is the attribution of bad faith to any counterpart who introduces limits. It is the experience of compromise as personal defeat rather than problem-solving. And it is the belief, held with complete sincerity, that any outcome short of total victory represents either incompetence or betrayal on someone’s part.

The deeper problem is that the algorithm does not simply give people information. It substitutes for judgment. When the social environment tells you what to believe and how firmly to hold it, extreme opening positions stop being tactics. They become authentic expressions of what the person believes the situation requires. That distinction matters enormously for how a skilled leader should respond. A tactical extreme anchor can be countered with a counter-tactic. A sincere one requires something different: shared reality must be restored before any positional movement is possible. No negotiation technique resolves that impasse until the parties can first acknowledge they are operating within the same set of constraints.

The Erosion of Relational Capital

Effective negotiation depends on relational capital: the accrued trust, patience, and willingness to confirm understanding that develops between parties over time. Digital certainty undermines it, and Chris Voss’s framework makes clear precisely how.

Voss built his method on tactical empathy: slowing the conversation down, labeling what the other party is feeling, mirroring their language back to them, and confirming their perspective before moving toward resolution. These are not techniques for being agreeable. They are tools for restoring enough shared reality to enable productive movement. They work because they signal to the other party that they have been genuinely heard, which is the precondition for any willingness to move.

The architecture of digital culture makes this extraordinarily difficult to sustain. The pause that tactical empathy requires, the deliberate silence after a label lands, feels dangerous to anyone whose sense of correctness comes from continuous digital certainty rather than internal judgment. Silence produces no confirming feedback. It reads as falling behind. The person who has learned to engage by watching social media cannot sit quietly across a table, because quiet in that context means losing ground in a contest the other party is still running. Voss’s method asks the negotiator to do the thing the digital environment has made most psychologically costly: create space, absorb pressure, and resist the impulse to immediately respond with certainty.

This is why relational capital is so difficult to build and so easy to destroy in the age of social networks and mobile devices. A disagreement where one party treats any concession as defeat, a relationship that hardens into posturing rather than dialogue, a negotiation where the opening offer is entirely disconnected from reality: in each case, the underlying dynamic is the same. The same patterns that dominate social media are now creeping into public hearings, association meetings, and workplace conversations. Digital certainty has replaced shared reality, and no positional progress is possible until someone in the room decides to slow down and restore it.

What the Skilled Leader Can Still Do

The structural forces described above are real and durable. Social networks and digital certainty are not going away anytime soon. But individuals with experience, skill, and developed judgment can consciously narrow the gap between what the technology environment produces and what deliberate practice makes possible. The house still has an advantage. That does not mean the skilled player is without options.

The first requirement is diagnostic. Before choosing a response to an extreme anchor, the effective leader must determine whether they are facing a tactical gambit or a sincere belief. Voss’s calibrated questions are the right tool. “How am I supposed to do that?” and “What would it take to make this work?” do not concede anything. They require the other party to engage with constraints rather than simply restate demands. If the extreme anchor is tactical, that engagement will produce movement. If it is sincere, the response will reveal what shared reality needs to be rebuilt before movement is possible. The diagnostic step is not optional. Applying empathy to a tactical gambit can inadvertently legitimize it. Applying counter-tactics to sincere belief will harden it. The skilled leader distinguishes the two before choosing.

The second requirement is deliberate engagement with human friction. The specific setting matters less than the pattern: working across constituencies with competing interests, navigating ambiguous authority relationships, staying in rooms where agreement is not guaranteed, and exit is tempting. These experiences, accumulated over time, are what develop emotional intelligence and relational capacity. Reading what another person actually needs beneath what they are demanding, managing your own reactions under pressure, restoring trust after it has frayed: none of that is learned in a classroom. It is learned by staying in difficult situations long enough to develop judgment about them. The leader who has built that capacity carries a competency no algorithm can replicate, and no shortcut reliably produces. These skills can be developed anywhere and are honed anytime they are practiced.

These competencies are not the exclusive property of any particular career path. They are available to anyone willing to engage seriously with human complexity, absorb accountability when things go wrong, and resist the easier path of performing certainty rather than building understanding. But senior leaders carry a disproportionate responsibility regardless of background. They set the conditions in which others either develop these skills or abandon them. How a leader behaves in a difficult room matters more than any policy they write or process they design.

The final word

The extreme posturing that digital culture rewards is not a character defect. It is a rational adaptation to an environment that has made maximalism feel safe and restraint feel dangerous. Understanding it structurally is what allows a skilled leader to respond to it effectively rather than simply reacting to it. The leader who can accurately diagnose whether they are facing sincere belief or tactical posturing, choose the appropriate response, and remain patient enough to let the other party move, is not simply demonstrating virtue. They are doing something important for the organizations they lead and the people who depend on them. In an era when digital certainty is the default, that capacity is not a soft skill. It is the core competency that separates leaders who build lasting alignment from those who accumulate positional victories at the cost of relationships. That is true whether the room is a boardroom, a council chamber, an association meeting, or a campus senate.

Yet, behind those service windows sit some of the most intelligent and dedicated people I know, loyal staff who care deeply about students, faculty, and one another. This creates a jarring paradox. How can an institution full of caring professionals produce such a frustrating experience? Max Weber might have argued that this friction is simply the iron cage of legal-rational institutions working exactly as designed, inescapable and self-reinforcing. I am not quite that pessimistic. In today's Dispatch, I want to argue that the conditions inside that cage, the risk calculus, the accountability architecture, the politics of who gets to jump the line, are things leaders can actually change. But, only if they resist the urge to first blame individuals and focus their energies on re-examining the structure those people are responding to.

The big picture

There is a fundamental misdiagnosis at the heart of most complaints about central service organizations in higher education. When senior leaders encounter a “Department of No,” they typically see a failure of competence or leadership. The assumption is that staff are obstructionist, resistant to change, or simply not suited to the demands of a modern institution. This framing is tempting because it suggests a clean solution: find better people, reset expectations, rebuild the culture.

The reality is more uncomfortable. What often looks like team underperformance is a rational adaptation to structural conditions that make speed costly and caution rewarded. This is not an argument that staff are never the problem. They sometimes are, and a leader who cannot distinguish structural adaptation from genuine incapacity will struggle to address either. The most reliable signal is behavioral: when performance improves as incentives change, the problem was structural; when it persists despite changed conditions, the cause lies elsewhere. But a leader who reaches for individual explanations before examining structural conditions will almost always diagnose incorrectly, and the remedy prescribed can make things much worse.

What deserves defense here is not bureaucracy as a system. The bureaucracy, with its queues, its procedural armor, and its friction, more often than not produces outcomes that conflict with the institution’s actual work. What deserves defense, most of the time, is the individual who operates inside that system and has adapted, rationally and understandably, to the incentive structures the institution itself has created.

The One-Way Risk of Moving Too Fast

To understand why knowledge workers move slowly, one must first understand the risk environment they inhabit. In IT, HR, Finance, Legal, and Procurement, the risk calculus is one-way: speed benefits the requester, and error costs the staff member.

Consider a procurement officer asked to expedite a vendor contract. If they accelerate the process by relaxing a review step, the benefit accrues entirely to the requester. The faculty member launches their project. The procurement officer receives no formal recognition and likely no informal acknowledgment either. But if that shortcut produces an audit finding, a data exposure, or a compliance violation, the consequences fall entirely on the staff member, not the person who made the request.

This imbalance creates a rigorous behavioral logic. When saying yes carries undefined risk and following the policy offers more guaranteed safety, the rational knowledge worker follows the policy. This is not obstruction. It is professional self-preservation in an environment where the penalty for error falls almost entirely on those knowledge workers with the least organizational protection. Until leaders explicitly and credibly alter this dynamic, expecting speed from a workforce incentivized for caution is not a management challenge. It is an institutional design failure.

The queue itself performs a parallel function. Universities possess effectively infinite appetite for administrative support, generating grants, IT support, hiring proposals, and compliance reviews at a rate that far outpaces the budgeted capacity to handle them. In this environment, complexity and delay serve as informal demand throttles. Required forms, committee approvals, and mandatory wait times are not merely inefficiency. They are rationing mechanisms that prevent the system from collapsing under load. Eliminating friction without expanding capacity (or reducing non-value-added activities or handoffs) does not improve service. It accelerates defective work.

When Charismatic Authority Meets Legal-Rational Process

Universities rest on what Weber called legal-rational authority: the premise that rules apply consistently to everyone. The procurement officer and the provost are, in formal terms, subject to the same institutional processes. This is not merely an abstraction. It is the foundation on which the staff member’s playbook has any operational meaning.

When a president, provost, or influential dean bypasses procedure to expedite a personal priority, they are exercising a competing form of authority, one grounded in personal status and institutional prestige rather than in rules. The operational disruption this creates is real: staff must drop current work, context-switch, and rush the VIP request through, pushing other work further into the queue. This results in a cycle of underperformance and overcommitment. Staff service the queue jumper precisely because they cannot afford to be seen as unresponsive to senior leadership. The work displaced by that choice does not disappear; it falls back into the queue, extending wait times for everyone else. Those longer waits also generate complaints.

The cascading effect is predictable. Displaced users escalate to their own powerful advocates, who apply pressure from above, generating more exceptions, which displace more work, which generates more complaints. The bureaucracy responds by tightening its procedures, because rigid rules are the only available defense against arbitrary demands. What leaders read as increasing resistance is the institution's own rational response to a pattern of disruption it was never designed to absorb.

How the Misdiagnosis Becomes Self-Reinforcing

Senior leaders, particularly those who are goal-oriented and impatient with friction, tend to read slow service as an individual failure. The staff member is not customer-focused. The team lacks urgency. The leadership or culture needs to change. This reading is understandable; friction is visible while its structural causes are not. But it produces a prescription that more often makes the underlying problem worse.

When a leader responds to slow service by increasing pressure on individual workers, the risk still runs in one direction. The staff member now faces the original structural conditions plus heightened performance scrutiny. The rational response is more caution, not less. More procedural documentation, not fewer steps. More queue, less discretion. The harder a leader pushes without changing who bears the costs of mistakes and how that accountability is enforced, the more the system stiffens.

This feedback loop explains why the dysfunction often persists through leadership transitions. Each new leader inherits a workforce conditioned, through years of accumulated experience, to understand that moving fast increases the risk of error, and error attracts consequences. A directive to be more agile, issued without a corresponding commitment to simplify the work or absorb the cost of mistakes, is not a culture reset. It is simply another signal that nothing has fundamentally changed.

The final word

The prescriptions that follow are structural. First, accountability needs to run in both directions. A leader who demands speed and then punishes the resulting error should face consequences for that contradiction, not just the staff member who moved quickly. But that only works if someone above can see the pattern. Right now, the procurement officer has no way to surface it. Designing that visibility, whether through governance, peer accountability, or use of performance data, is itself the hard institutional work this reform actually requires. Second, the expedite lane should be formalized as a form of white-glove service rather than a favor for persistent or powerful individuals. This quarantines the disruption without pretending to eliminate it. Third, institutions must make genuine choices about what they will formally decline to do. An ever-lengthening queue is not a service model. It is what happens when an institution lacks the will to say no or the ability to simplify work. Moral persuasion alone will not solve a demand-capacity mismatch. Structural choices will.

None of this is easy. Two-way accountability requires leaders to constrain their own behavior first. Formal expedite policies require acknowledging, publicly, what has been operating informally. Declining categories of work, or eliminating non-value-added steps, can be politically challenging in ways that indefinite queuing is not.

Institutions unwilling to make these structural changes will continue producing the behaviors they complain about most. The staff member following the policy to the letter is not failing the institution. The institution has failed the staff member, and through that failure, every person waiting in line. The path to faster, more responsive service does not run through hiring decisions or culture statements. It runs through a fundamentally different structure, one that makes speed safe before demanding it.

It was 2003, and I was the “star quarterback” coder in the central IT organization at Texas A&M. My bosses, Steve Williams and Tom Putnam, called on me for the highest-priority projects: web-based admissions, e-commerce for tuition payments, class registration, SEVIS compliance. I had a knack for designing APIs on the mainframe that were cleanly callable from web applications, and I did my best work moving fast, often alone, supervising a small team but rarely slowing down to involve them.

Then I read a news story while attending a meeting in Washington. The University of Texas had experienced a significant data breach. An ambitious programmer had written a simple form that accepted a nine-digit number, and if it matched a Social Security number, it returned a full user profile. The API was publicly exposed to the Internet. A hacker had called it hundreds of thousands of times, guessing numbers at random, and walked away with tens of thousands of identities before anyone noticed.

I had built something nearly identical. And yes, it was also exposed to the Internet.

The call to my boss was one of the hardest ones I have made. Within the hour, I was on the phone with Steve, his boss Tom, the network security team, and the VP/IT Pierce Cantrell. They examined the logs for evidence of a similar intrusion. The next twenty-four hours were the longest of my career. When the security team reported back that the API had not been abused, I exhaled and absorbed a lesson I have never forgotten: my instinct to work alone, to move fast and stay efficient, had stripped away the collaboration that might have caught the problem before it became one.

I have tried to carry that lesson into how I lead. Mistakes are often the best teachers, and the right response to them is clarity and accountability, not punishment. But the arrival of GenAI and what practitioners now call vibe-coding, producing functional software quickly through AI-assisted code generation, has brought that 2002 moment back to mind more than once. A motivated staff member working in isolation today has capabilities I could not have imagined then. The speed is higher. The surface area for unintended exposure is larger. And the institutional stakes are the same.

In today's Dispatch, I want to share a framework for thinking about where AI-assisted software development fits within a research university, where it adds genuine value, and where it introduces risks that move faster than the governance structures designed to contain them. That framework draws on guiding principles recently released to University of Georgia leaders and IT professionals, developed through the two-readings approach I rely on when building new IT policy or guidance.

The big picture

Higher education institutions are built on distributed authority. Colleges and departments exist to pursue distinct academic missions, and the administrative structures around those missions tend to reflect that diversity. This is not inefficiency; it is design. A research university is a federation, not a hierarchy, and any IT strategy that fails to account for that structure will eventually break against it.

That federated reality is precisely why IT governance in research universities requires clarity about where decisions belong. The question is never simply whether something can be built. It is whether the decision to build it is being made by the right people, with the right information, with appropriate accountability for what comes next. For most of the last two decades, the answer to that question has been shaped by a deliberate shift: away from staff-coded software applications and toward vendor-supported platforms with the scale, specialization, and continuity to sustain them. That shift was not a loss of creativity. It was a hard-won institutional lesson.

GenAI is now putting pressure on that lesson. The pressure is not new in kind, but it is new in magnitude. When any staff member with reasonable technical curiosity can produce functional code in an afternoon, the conditions that historically slowed the spread of custom development no longer exist. The guardrails that once came from difficulty have disappeared. What remains is judgment, and it’s unevenly distributed.

The Edge is Real, and It Matters

The Edge-Leverage-Trust framework offers a useful way to think about this moment. It begins with the recognition that not all IT work belongs in the same governance layer. Some functions should scale: identity management, enterprise systems, data infrastructure, cybersecurity. These belong in the Leverage layer because standardization produces reliability, security, and cost efficiency that no unit could achieve independently. Other functions should not scale. Departments experiment. Research centers build tools for their specific scholarly needs. Professional schools configure platforms to fit their workflows. This edge activity is not a workaround. It is what a healthy, federated R1-type institution looks like from the inside.

GenAI coding fits productively at the edge. A staff member using an AI coding tool to automate a local workflow, build a batch data transformation script, or produce a unit-level reporting dashboard that draws only on data the unit already controls is doing exactly what the edge is designed to accommodate. That work is the unit’s business. The appropriate response from central IT is not oversight; it is encouragement.

The line is crossed when edge tools begin to act like central systems: when they integrate with enterprise data warehouses, authenticate through shared identity systems, connect to core ERP systems, or expand to serve audiences beyond the unit that built them. At that point, the tool has taken on institutional responsibilities that local governance is not designed to manage. Speed of construction is no longer the relevant variable. Durability, security, integration integrity, and continuity are.

What Generative AI Actually Changes, and What it Does Not

The critical misunderstanding about AI-assisted software development is the assumption that faster creation means lower risk. It does not. Code produced by a generative AI tool carries the same structural properties as any other custom code. It requires the same ongoing maintenance. It introduces the same integration challenges. It carries the same information security obligations. The generation of the functional code is faster; the support obligation does not shrink to match.

There is evidence that the risk runs in the other direction. Code written quickly and reviewed lightly is more likely to surface problems under real conditions. Several significant cloud infrastructure failures in recent years have been attributed in part to AI-generated code deployed without sufficient human review. Generative AI tools shift software developers from writing code to reviewing code, and when that review function is not performed rigorously, the productivity gain comes at a corresponding increase in risk. Units across higher education are discovering this in real time.

The deeper structural problem is what Gartner analyst Andy Kyte has described as the Gordian Knot: the dense, self-reinforcing tangle of fragile systems, undocumented workarounds, and accumulated technical debt that builds up in institutions over time. Generative AI does not cut through that knot. It tightens it. Every ungoverned custom application adds another strand. Every tool that works well enough in its local context but was never designed for institutional durability becomes a future liability. And when the staff member who built it moves on, the system does not move with them. The 2 a.m. call lands on the central IT organization that had no part in the decision.

The Human Dynamic That Makes this Worse

There is a recurring institutional pattern that compounds the structural risk. It begins when a motivated, technically capable staff member, sometimes working entirely alone, builds something that solves a real problem. The solution works. Colleagues are impressed. A senior leader takes notice and encourages broader deployment. What started as a local productivity tool is now being treated as a platform, without the review, the governance, or the support infrastructure that a platform requires.

This is called “dopamine-fueled IT.” The individual is genuinely talented. The initial work may be genuinely useful. But the institutional conditions around that work have not caught up to the expectations being placed on it. No one has asked whether the tool integrates safely with enterprise systems. No one has thought through what happens when the developer leaves. No one has assessed whether the data being used has been properly authorized for this new application context. The executive who endorsed the expansion wanted visible results and got them. The governance structures that exist to protect the institution from exactly this kind of deferred risk were bypassed, not maliciously, but because they were inconvenient in the moment.

The challenge for CIOs and senior IT leaders is to interrupt that pattern without discouraging the underlying initiative. That requires being clear-eyed about what makes edge innovation valuable, which is precisely that it is local, bounded, and reversible, and what makes Leverage-layer decisions consequential, which is precisely that they are not. The distinction is not about capability or intent. It is about accountability, and accountability is a structural question, not a personal one.

The final word

Higher education institutions have spent roughly two decades learning, often through painful experience, that the history of building software for themselves is littered with expensive, high-profile failures. The successes have almost always come from leveraging platforms built by companies whose entire business is building and sustaining them at scale. Generative AI is a genuinely powerful tool. It does not rewrite that lesson. What it does is lower the barrier to repeating the mistakes that produced it. The institutions that navigate this moment well will be the ones whose leaders understand where the edge ends and where institutional accountability begins, and who hold that line not as a constraint on innovation, but as the condition that makes real innovation sustainable within research-centric institutions.

Some time before that hearing, I had experienced the results of that philosophy firsthand. I joined a Teams call from a remote location over a Starlink connection. No lag. No buffering. It felt like I was sitting in my office. That kind of satellite network performance does not emerge from a bureaucracy. It comes from an organization that has stripped away work and process complexity and built around first principles.

Isaacman comes from that world. NASA does not, at least not yet. Last week, a NASA crew was aboard Orion and completed a flight around the far side of the Moon using the Space Launch System rocket, the first humans to travel that far from Earth since Apollo 17. The mission flew on time. The rocket performed. That is not nothing. It is, in fact, the result of an institution choosing to rationalize a legacy program rather than abandon it, which is harder and rarer than building something new from scratch.

In today’s Dispatch, I contrast the discipline of work and process simplification and lean innovation that made SpaceX what it is with the accumulated bureaucracy and complexity that NASA is now working hard to shed under Jared Isaacman, and ask what either story means for institutions trying to simplify and transform from within.

The big picture

NASA and SpaceX make for a useful contrast in how institutions approach complexity, cost, and change. The contrast is not clean. Jared Isaacman, now confirmed as NASA’s fifteenth administrator, came to the job shaped by his SpaceX experience and the conviction that large institutions can move faster by removing what does not matter. He inherited a 322-foot rocket that embodied nearly everything he had criticized and chose, deliberately, to standardize and fly it to the moon rather than abandon it. Meanwhile, Congress passed NASA a budget exceeding $27 billion for fiscal year 2026, the largest in nearly three decades in real terms. These are not the conditions of a failing institution; they are signs of political capital.

The more useful question is not whether SpaceX’s model defeats NASA’s. It is whether an institution can internalize the discipline of simplification after years of complexity accumulation, and whether political will can sustain that effort long enough to matter. That question is harder and rarer than building a company from scratch. It is also more relevant to higher education, where no one gets to start over.

Max Weber wrote about ideal types not as descriptions of reality, but as analytical tools for understanding the forces that shape political, social, and economic behavior. No organization is purely legal-rational or purely traditional. But comparing institutions to ideal types reveals what motivates both their actions and decisions. SpaceX and NASA remain useful ideal types, not because one succeeded and the other failed, but because they represent two fundamentally different institutional responses to the same problem: how to get work done when complexity accumulates.

Work Simplification at SpaceX

Work simplification is not a slogan. It is a discipline. It forces an institution to examine every step in a process and ask whether it is necessary. Most organizations never do this, either because of internal politics or because they are afraid of what they might find. SpaceX built a culture around it. The result is visible in five lessons that remain instructive regardless of how any particular rocket program turns out.

1. Every requirement must have an owner. SpaceX attaches a name to every requirement. If no one can defend it, it disappears. This creates clarity and accountability in equal measure. The institutional default, visible in large procurement programs across both government and higher education, is to carry requirements forward without ever asking who still needs them or why. Untraceable requirements are not neutral; they are dead weight.

2. Delete before you optimize. SpaceX removes steps before improving them. They eliminated hydraulic systems in favor of simpler electric controls. They dropped the complex fairing-catching system after realizing the same results came from simply letting the fairings splash down and retrieving them. The institutional default is to solve problems by adding new layers, which seems like progress. Deletion requires confidence that the original requirement was not worth keeping.

3. Simplification makes systems more reliable. SpaceX reduces failure by reducing components. The choice of stainless steel over carbon fiber for Starship removed both the heat shield requirement and its intricate manufacturing steps. Reliability through complexity is a contradiction. Every additional component is a new way for a system to fail. Organizations that rely on oversight and controls to ensure quality are managing the consequences of complexity that they did not eliminate.

4. Vertical integration eliminates friction. SpaceX builds most parts in-house. Teams talk directly. Decisions move fast. The fragmented supply chain model, common in both aerospace and enterprise technology, adds negotiation, delay, and coordination costs at every process step. Those costs are easy to justify individually and hard to see in aggregate until the system stops moving.

Work simplification is a cultural choice, not a technical one. This is the lesson that matters most for institutional leaders. SpaceX built a culture where unnecessary work is treated as a problem to be eliminated. The institutional default is a culture where unnecessary work is invisible and quiet, absorbed into the budget and the calendar by habit and without comment. The difference is not engineering talent or organizational size. It is a decision about what the organization pays attention to.

Work simplification is a way of seeing. It forces an institution to confront what it does and why. That clarity is the precondition for lean innovation to take hold.

Lean Innovation and the Discipline of Constraint

Lean innovation comes from scarcity, but not only from scarcity. More precisely, it comes from the discipline of treating resources as a constraint rather than a permission slip, regardless of how much funding is available. NASA’s current budget offers a useful illustration of why this distinction matters. Congress approved more than $27 billion for fiscal year 2026. That is not scarcity. But if Isaacman allows that abundance to dissolve the pressure for simplification, the same drift that produced a $23 billion rocket program with a ten-year delay will reassert itself quickly.

SpaceX grew up under financial pressure where failure had direct consequences for the company’s survival. That pressure sharpened judgment about what to invest in and what to cut. The lesson for institutions operating in relative abundance is not that they should manufacture artificial scarcity. It is that they need a substitute discipline: the deliberate choice to treat every investment as if the capital were limited, and to measure returns with the same scrutiny that austere conditions would demand.

Four additional lessons follow from the SpaceX example.

• Controlled failure accelerates progress. SpaceX flew prototypes, knowing some would explode. Each failure revealed something useful and cheaply. The institutional alternative is to avoid failure at all costs, which typically means designing for perfection from the start, delaying until certainty is within reach, and producing systems that are late, expensive, and still imperfect. When failure becomes organizationally unacceptable, learning slows and costs escalate.

• Iteration outruns optimization. SpaceX improves through constant cycles of build, test, and refine. The institutional preference for comprehensive design before implementation is not inherently wrong; it reflects legitimate risk management in high-stakes environments. But it tends to produce systems optimized for the requirements of three years ago, delivered into a changed environment. The discipline of iteration is not about moving recklessly. It is about shortening the distance between assumption and evidence.

• Architectural honesty is non-negotiable. SpaceX chose reusable engines because expendable designs were financially unsustainable at scale. The organization confronted that reality and designed around it. Institutional architecture that avoids honest reckoning with cost and sustainability does not make the problem disappear; it defers and compounds it. Higher education is full of architectural decisions that were reasonable when made and have never been revisited.

• Innovation flows from constraints, not from comfort. SpaceX made hard tradeoffs. Steel instead of carbon fiber. Simpler systems instead of more capable ones. Lower cost through deletion rather than through engineering. The institutional instinct is to innovate by adding. SpaceX demonstrated that the more durable advantage comes from subtracting what does not add value.

Lean innovation is the discipline of doing less, better. It is not frugality for its own sake. It is strategic austerity that channels effort toward what matters.

What NASA Is Learning, and Why That Matters More

The story that deserves attention is not SpaceX’s continued success. It is what NASA under Isaacman is attempting to do with a legacy program that was already built.

Isaacman standardized the SLS design to enable a more predictable cadence of flights. That is Lesson Two in practice: delete non-value-added activities and handoffs before you optimize. He publicly named the failure modes in the Starliner program and held the institution accountable. That is Lesson One: every requirement, and every decision, must have an owner. He is pursuing a multi-vendor launch strategy that introduces competition into a procurement culture that had operated without it. That is a structural intervention in the economic conditions that produce drift.

None of this is as elegant as starting from scratch. Legacy architecture does not submit gracefully to simplification. The requirements that have accumulated over decades each have defenders, and those defenders have political relationships. Progress in this environment depends on negotiation, credibility, and accrued trust that lowers the temperature enough for people to let go of what no longer serves them. That is a different kind of leadership challenge than building a rocket company.

This is the more useful lesson for higher education leaders, because it is the situation they actually occupy. No project ever starts with a clean slate. ERP projects, infrastructure investments, academic technology decisions, and organizational redesigns all begin with inherited constraints, established stakeholders, and requirements that no one can quite explain but everyone seems to depend on. The discipline that SpaceX practices from inception has to be applied in higher education retrospectively and incrementally, against resistance, in an environment where failure is organizationally unacceptable and consensus is required for any change.

That is harder than what SpaceX does. It is also what the job requires.

The final word

The SpaceX model offers durable lessons about work simplification and four more about lean innovation. Those lessons apply to any institution attempting change, including universities undertaking ERP modernization, technology consolidation, or organizational redesign. But the harder and more instructive example may now be NASA itself: a legacy institution with an inherited program, a new leader shaped by a different model, and the deliberate choice to simplify rather than abandon what exists.

The real barrier in higher education is not technical. It is political. Internal politics can overwhelm even the best-designed transformation. Progress depends on negotiation, relationships, and the slow accumulation of trust that makes it possible for people to release requirements they have protected for years. Those things matter little in a private rocket company. They are everything in a public institution. The leaders who understand both the discipline of simplification and the patience that institutional change requires are the ones most likely to get somewhere worth going.

The scale of these ideas was impressive, and the ambition was real. But as I listened, I kept thinking about the tradeoffs. A colleague from another southern university and I compared notes. Neither of us could make the numbers work with the resources available to us. More importantly, we were not convinced that such large financial resources were necessary to deliver meaningful AI capabilities to our community.

That skepticism extended to the tools themselves. I was skeptical of Microsoft Copilot as the primary option for everyday use. The context windows and file upload features were too limited, the personalization features were absent, and the tool felt built for corporate environments rather than academic ones. It was an enterprise-grade infrastructure in search of an academic use case. I still mentioned it to peers for what it was: a solid, privacy-compliant option bundled into existing software, but not something that could anchor a comprehensive Everyday AI strategy.

That assessment is changing. Microsoft's recent announcements have shifted the calculus enough that a second look is warranted, and most US higher education institutions have a data privacy agreement in place with Microsoft that makes that second look more than theoretical. In today's Dispatch, I want to lay out how an ambitious Everyday AI strategy can proceed with disciplined, modest investment, and why the market has moved in ways that actually strengthen the case for restraint.

The big picture

The core challenge for institutional leaders is not whether to adopt AI. That question is largely settled. The challenge is deciding how much to spend, on what, and in service of which outcomes. Those decisions are being made right now, often under competitive pressure, and the consequences will compound greatly over time.

The right framework distinguishes between two fundamentally different categories of AI investment. The first is Everyday AI: affordable, accessible tools that improve individual productivity for students and employees. Writing assistance, meeting transcription, document drafting, and basic research synthesis. These tools are embedded in platforms that institutions have licensed, and their value is real and immediate. The second is Game-changing AI: high-stakes, institution-level spend intended to transform research capacity or administrative operations at scale. High-performance computing, GPU clusters for faculty research, and agentic AI embedded in modernized ERP systems. These require serious capital and serious governance.

The strategic error most organizations risk making is funding Everyday AI at Game-changing AI levels. The two categories are not on the same investment curve, and conflating them risks wasting resources on subscriptions with little long-term ROI.

The Necessity of Restraint: Guarding Credibility and Capital

The temptation to overspend is structural, not individual. Every major technology transition produces a period in which visible, large-scale investments function as signals of seriousness. Organizations that spend excessively are assumed to be ahead. Those who move cautiously are assumed to be behind. This dynamic has a name during peak hype cycles: dopamine-fueled IT. It substitutes visible action for strategic planning, and it consistently leaves organizations overextended before the cycle turns.

The AI investment environment today has several features that make restraint particularly important. Model capabilities are improving faster than institutional deployment cycles. What costs $300,000 per month to build on a custom basis today will be available as a commodity feature in a standard enterprise license before too long. Custom solutions built on current-generation models carry the same risk that custom ERP bolt-ons carried in the early 2000s: they lock institutions into architectures that will be obsolete before they are fully adopted and provide ROI.

Architectural honesty requires acknowledging this. Institutions do not need to customize their own large language model for community use. No version of that strategy provides a reasonable ROI. A proprietary in-house model for everyday use will not be competitive with frontier commercial models, will not travel with students after graduation, and will depreciate toward zero as the market matures. The same logic applies to single-vendor enterprise licenses that commit significant institutional capital to one platform before the market has settled on clear winners.

The better discipline is to maximize the value of what is already available, reserve capital for investments with genuine transformative potential, and maintain the institutional credibility that comes from not having overcommitted during the boom.

What the Microsoft Development Actually Means

For CIOs who have been watching the Copilot trajectory, the announcements of the last several weeks are worth taking very seriously. Microsoft has moved Copilot from a single-model assistant to a multi-model GenAI platform. The Researcher agent now includes a Critique feature that uses OpenAI’s GPT to generate research responses and Anthropic’s Claude to independently review them for accuracy, completeness, and citation quality before delivery. Copilot Cowork, now in early access through Microsoft’s Frontier program, embeds Claude’s agentic capabilities directly into Microsoft 365 Copilot Studio workflows for long-running, multi-step tasks. Claude Sonnet is available directly in standard Copilot Chat alongside OpenAI’s models.

This matters for three reasons.

First, it changes the meaning of the single-vendor licensing concern. A professional (premium) Copilot license is no longer equivalent to betting on Microsoft alone. The platform is becoming a container for multiple frontier models, with the institution retaining control over which models are enabled and under what conditions. That is a structurally different proposition than it was just twelve months ago.

Second, it validates the pluralistic argument. The case for exposing students to multiple AI tools rather than standardizing on one has always rested on the unsettled nature of the market. Employers use different tools, and fluency across platforms is a genuine workforce competency. Microsoft’s multi-model architecture effectively embeds that pluralism in the enterprise platform. Students working in a Copilot environment are now working across multiple models, not just within one platform.

Third, the adoption data is informative. As of early 2026, only about 3.3 percent of Microsoft’s commercial Microsoft 365 users were paying for Copilot. Microsoft’s multi-model move is, in part, an attempt to solve a persistent adoption problem by demonstrating value that a single-model assistant was not delivering. The lesson for institutions is not that Copilot has solved the adoption challenge. The lesson is that the market itself is acknowledging that single-model approaches are not sufficient.

For those of us in higher education institutions with existing Microsoft enterprise agreements and data processing addenda already negotiated, the calculus has shifted. The compliance infrastructure is in place. The tool is improving meaningfully. Leveraging what is already available before committing new capital to other vendors is now a more defensible strategy than it was a year ago, not because the platform is perfect, but because it is no longer the weakest of the major foundational models.

Execution: AI as a Workforce Development Platform

An institution’s long-term technology strategic plan and its workforce development strategy are the same conversation in an AI-infused university. The question is not what AI can do for students, faculty, and staff. It is what their human contribution looks like when AI handles the rote, mundane work that used to require junior labor.

New data from Goldman Sachs and Morgan Stanley confirms what many of us have suspected: the impact of AI on employment is not sudden displacement. It is a gradual, structural reclassification of roles. Goldman Sachs scored occupations by AI exposure, separating roles that can be fully substituted by AI from those where AI complements human work, and found that AI has raised overall unemployment by just 0.1 percentage point so far. The jobs that are contracting are built on routine, repetition, and narrow specialization. The jobs that are growing in both number and compensation are ones where human judgment, interpersonal accountability, and contextual reasoning cannot be automated away. The radiologist is the instructive case: ten years ago, Geoffrey Hinton predicted deep learning would make the profession obsolete within five years. Instead, radiologists adopted AI, their numbers grew, and their pay increased. Augmentation, not substitution, is the dominant pattern, and it is the pattern institutions should be building toward.

This has direct implications for college curricula. Institutions that continue preparing students primarily for executor-type roles are preparing them for work that is contracting. Institutions that develop judgment, adaptability, critical thinking, and orchestration capacity are preparing them for work that will expand. AI fluency should not be confined to electives or specialty courses; it is a horizontal capability that belongs across the curriculum, embedded in the expectations of every discipline. Capstone experiences, in particular, should be redesigned around synthesis and human judgment, not just the demonstration of technical knowledge. The question every program should be asking is whether its graduates can direct, evaluate, and improve AI-assisted work, not just handle tasks that agentic AI can easily perform.

Beyond curriculum, adoption at scale requires investment in people and process. Communities of practice, where faculty, staff, and students share what is working and what is not, accelerate competency development more reliably than top-down training mandates. Faculty who are curious and willing to experiment are the real adoption infrastructure; supporting them is more valuable than licensing more AI tools. IT teams that want to be strategic partners need to be known for effective collaboration, not just technical execution. Trust is not a soft consideration in AI adoption. It is the condition that determines whether institutional guidance is actually followed.

The final word

The path to becoming an AI-infused University is not determined by the size of the AI investment, but by the quality of the judgment applied to that investment. The organizations best positioned when the current AI hype cycle exhausts itself are the ones that resisted the pressure to signal ambition through large, visible, and premature commitments to any of the GenAI foundational models currently available.

Everyday AI is already available cost-effectively and on a large scale. It’s increasingly integrated into platforms that institutions have already licensed, and it’s improving without requiring any additional capital investment. Game-changing AI, the kind that genuinely reshapes research infrastructure or transforms administrative operations at scale, requires serious investment and serious governance, and that investment will be more available to institutions that did not overcommit during the AI boom.

The market is settling toward integration, not novelty. The organizations that recognized that early will be the ones with both the credibility and the capital to act when the genuinely transformative opportunities arrive, once the boom ends.

Flight instructors use a phrase that has stayed with me: either “you fly the plane” or “the plane flies you.” Experienced pilots develop an instinct, built from hours in the cockpit, that picks up on subtle shifts in wind and aircraft behavior before those shifts become problems. New pilots spend their time reacting. Each correction comes a beat too late and becomes an overcorrection, which creates a new problem, which requires another correction. Only experience teaches you which kind of pilot you are.

The same dynamic holds for CIOs and their senior leadership teams. Unless a team is continuously doing the quiet, disciplined work of anticipating and preventing problems, disruptions will come. As someone who is naturally assertive, my instinct is to lean hard on prevention, to keep teams focused, to treat every near-miss as a signal that something needs addressing. That instinct is not wrong. But flying this plane well is not a solo act; it is a team effort requiring the best of three distinct styles.

In today’s Dispatch, I want to revisit the negotiation styles discussed in earlier commentary and look at them through the lens of IT service disruptions. The three styles: Assertives, Accommodators, and Analysts, read these IT service disruptions quite differently. Each sees something real. Each misses something their counterparts will catch. It is their collective effort at scrutinizing the smallest details together that puts the team in genuine control of the plane they are flying.

The big picture

Major service disruptions are not just operational events. They are interpretive ones. When something breaks, whether by human error or machine failure, the disruption does more than reveal a gap in process or infrastructure. It surfaces the assumptions each team member carries about what strong team performance actually looks like.

Most post-incident reviews proceed as if everyone in the room read the same event. In practice, they did not. The same incident that registers as a performance failure to one person looks like a demonstration of team resilience to another, and a predictable consequence of a system carrying too much load to a third. These interpretations are not random. They are structural. They follow directly from the negotiation styles explored and discussed previously: the Assertive, the Accommodator, and the Analyst.

Understanding why this happens, and what it costs when overlooked, is the central challenge of building teams that improve steadily over time rather than cycling between disruption and recovery. Mutual self-awareness is key to strong teams.

The Assertive’s Read: Weight as Prevention

For the Assertive, a major service disruption is evidence of drift. Standards have slipped. Somewhere attention was lost, a detail fell through, and both the leadership and the organization paid for it. Their instinct is to bring weight to the moment: name what happened, assign responsibility, and make clear that tolerating service disruptions is not acceptable. They believe, not without reason, that a team that never feels the full weight of disruptions will stop working hard to prevent them.

The Assertive is not wrong. Teams do drift in the absence of accountability. The problem is the conflation of two distinct questions. Did the team respond well? And did the team prevent this? For the Assertive, a strong recovery does not redeem a failure to prevent the major disruption in the first place. That distinction is important, but applied without calibration, it produces a team culture that equates every disruption with poor performance rather than using the incident as a diagnostic.

The structural risk of too much Assertive energy is a tension-filled room. Accommodators, who thrive on relational trust and shared recognition, disengage or fade into the background when the environment feels punitive. Analysts, who need psychological safety to raise structural concerns without being accused of deflection, go quiet. The very voices that could improve the system long-term are driven out by the short-term pressure to raise the bar. An all-Assertive culture can hold standards, but it can put too much weight on those best situated to address underlying issues.

The Accommodator’s Read: Response as Sufficient Evidence

The Accommodator measures the performance of the team much differently. They do not evaluate the incident at the moment of major disruption alone; they evaluate it across the full arc. Detection, communication, coordination, recovery. A team that held together under pressure, communicated well, and resolved the issue with collective effort has demonstrated something real and tangible. That is not nothing. Operational resilience is built over time through exactly these moments.

The Accommodator is also not wrong. Teams that carry weight and function under pressure have built a kind of institutional muscle. The risk is a different conflation: “Did we handle this well?” and “Is the IT service stable and healthy?” are separate questions. A team can respond superbly to a chronic condition while remaining entirely blind to the fact that a chronic condition is worsening. Over time, the Accommodator’s instinct to recognize and affirm can become a tolerance for drift, normalizing recurring incidents by reframing each one as evidence of team strength.

The structural risk of too much Accommodator influence is the gradual acceptance of a quiet, degrading baseline. Prevention is deprioritized. Root cause analysis feels unfair. The team gets very good at incident response and focuses less on incident prevention. What looks like a resilient team culture is sometimes a comfortable one.

The Analyst’s Read: Structure Produces Defects

The Analyst does not read a disruption as evidence of individual lapse or cultural softness. They read it as output. Given the volume of work in progress, the number of competing priorities, and the cognitive load on the people doing the work, a defect at this moment was a near certainty. The framework here is familiar to readers of prior commentary: high WIP correlates with high defect rates. Lower the load, reduce the errors. Set better priorities, say no more often, and the system produces better results.

The Analyst’s structural argument is well-grounded. The research on WIP and defect rates is consistent, and the dynamics of overloaded service queues are predictable. But the Analyst’s lens, applied too narrowly, mistakes the map for the territory. Not every defect is a WIP problem. Some disruptions trace to training gaps, design flaws, or errors or omissions in judgment. Structural explanations, however accurate, can become a way of avoiding the accountability questions the Assertive is right to raise.

The more serious risk is behavioral. Analysts shut down under sustained assertive pressure. When the post-incident debrief turns into an accountability session with a high emotional temperature, the Analyst withdraws. They stop offering structural critique precisely when it is most needed. A team that loses the Analyst’s voice at critical moments loses its most reliable corrective mechanism. The pressure cooker the Assertive builds is most damaging not because it generates weight on the team, but because it deters the people best equipped to apply structural fixes to the system.

Why Strong Teams Need All Three

The Assertive, Accommodator, and Analyst are not competing for the correct interpretation of what happened. They are each surfacing a distinct response mode that the others are structurally inclined to miss. A team without Assertive energy drifts. A team without Accommodator instinct buckles under stress. A team without Analyst discipline accumulates invisible structural risk until something breaks hard.

The ideal-type version of each style, pursued without the correction of the other two, produces a team that is recognizably broken. The pure Assertive culture stresses out its best people. The pure Accommodator culture gradually accepts chronic low-level underperformance as normal and expected. The pure Analyst culture retreats into analytical frameworks and goes quiet at the moment the room needs a clear voice.

Bob Iger’s phrase, “sweating the details together,” captures what the alternative looks like. Not one interpretive style imposed on the whole team, but shared scrutiny of the same details from different angles. Excellence in operations is not a single perspective held consistently. It is the accumulation of small corrections, made by people who see the same playing field differently and trust each other enough to say what they think.

The leader’s job is to hold those three perspectives in productive tension. In practice, that means managing the Assertive’s post-incident pressure so it does not silence the Analyst, creating space for the Accommodator’s recognition so it reads as earned rather than defensive, and treating structural critique as required input rather than a deflection from accountability. All feedback should always be welcome. None of these adjustments is easy in the moment of a response or debrief. All of them are necessary.

The final word

A disruption reveals more than what IT service broke. It reveals how the team understands the relationship between performance, pressure, and trust. The Assertive, the Accommodator, and the Analyst will never read that moment the same way. That is not a problem to resolve. It is the architecture of a team that gets better over time. Success is not a single moment of incident response and resolution. It is the accumulation of small details, scrutinized honestly by people who see the world a bit differently and trust each other enough to say the things that need to be said.

Two moments have stayed with me. In one, a senior consulting partner came to my office to inform me that the University Registrar had continued to change the scope of the transcript implementation, and that the accumulated bill had reached $400,000. That was a time-and-materials engagement. In another, a workstream leader flagged that the consulting vendor had spent little time studying a complex business process, and that their process maps still carried another institution’s name in the document header. That was a fixed-price engagement, and the vendor had every incentive to move quickly and less incentive to dive deep. Both situations are more common than most leaders would like to admit. In today’s Dispatch, I want to bring my decades of ERP experience to bear on that common problem: how to best manage consultants working on a fixed-price agreement and those working on a time-and-materials basis. How you manage each one is different, and the cost of getting it wrong is the same: a project that runs out of money, time, or both before it crosses the finish line.

The big picture

Most CIOs and project directors do not choose the contract model governing the consulting work on their ERP implementation. That decision typically occurs upstream, negotiated by procurement officers and CFOs who are optimizing for budget certainty rather than daily project management. By the time the consulting team is assembled, the contract is signed and the operating conditions are fixed.

This creates a practical problem. The management discipline required to run a fixed-price engagement is fundamentally different from what a time-and-materials agreement demands. The incentive structures are different; the failure modes are different; and the skills that matter most are different. A project director who manages a time-and-materials engagement incorrectly will watch the budget erode one unbilled decision at a time. One who manages a fixed-price engagement incorrectly will find themselves buried in change orders and contractual friction.

Neither model is inherently superior. Institutions have succeeded and failed under both. What determines the outcome is not the contract type, but whether the project director understands the constraints they are working under and manages accordingly.

What Fixed-Price Actually Means on the Ground

Fixed-price contracts are attractive to CFOs and governing boards because they appear to convert a sprawling, uncertain transformation into a bounded financial commitment. The number fits cleanly into a capital budget. For institutions that are risk-averse or lack experienced internal project staff, the logic is entirely reasonable.

However, the fixed-price architecture carries a structural tension that every project director needs to understand from day one. When a vendor assumes financial risk for overages, they protect their margin by limiting scope, resisting depth of process discovery, and substituting lower-cost resources as the project matures. This is not bad faith; it is a rational response to the incentive structure both parties agreed to. Senior architects who are highly visible during the early phases will be replaced by junior analysts once unit testing begins. The partner is implementing a static scope document while the institution is still discovering its own preferences in real time.

Higher education compounds the fixed-price problem in a specific way. Business processes are often exception-driven, poorly documented, and understood only by the people who have run them for years. Because a fixed-price vendor is incentivized to move quickly through the early discovery and configuration phases, there is a structural pressure to keep requirements workshops shallow and move toward build. The project director's most important job in a fixed-price engagement is to resist that pressure. The vendor must invest real time in process discovery, and the institution's configuration teams must be pushed to go deep during workshops, surfacing edge cases, exceptions, and dependencies before the design is locked. Requirements that are not discovered early do not disappear; they surface during testing, when the cost of addressing them is highest and the institution's leverage is lowest. By that point, every gap in the original specification becomes a formal change order, often priced at a premium, and negotiated at the moment the institution can least afford to walk away.

The single most important tool in a fixed-price engagement is a pre-negotiated rate card, agreed to before the contract is signed. This is a comprehensive schedule of billable rates for every consultant tier the vendor might deploy, and it remains binding for the life of the implementation. When scope inevitably expands, the negotiation shifts from price to volume. Instead of arguing over what additional work should cost, the project director is managing how many hours a task consumes. This converts change orders into a more manageable administrative process. A fixed-price agreement without a pre-negotiated rate card is a structural failure waiting to happen.

What Time-and-Materials Actually Means on the Ground

Time-and-materials agreements rest on a different premise. They assume the institution and the implementation partner are aligned in pursuing the best possible outcome, and that the flexibility to respond to what gets discovered during the project is worth more than the predictability and comfort of a fixed budget ceiling. The model is well-suited to complex environments where detailed requirements genuinely cannot be fully known in advance, which describes most university ERP implementations.

The structural weakness of T&M is the absence of any financial incentive for the vendor to say no. If a functional lead wants a customized workflow built for a process that serves twelve people a year, the consultant will bill the hours to build it. This happens not because the vendor is acting improperly, but because the contract rewards hours worked rather than value delivered. Scope expansion in a T&M engagement is quiet and cumulative. It shows up in the burn rate, month after month, until someone does the math and realizes the budget will not reach the go-live date.

A T&M engagement requires the project director to function as an investment manager rather than a contract auditor. The most effective tool for maintaining that discipline is a miniature scope and cost agreement for every major deliverable, negotiated before billable work begins. This converts the engagement into a series of small, bite-sized fixed-price commitments, providing cost predictability while retaining T&M flexibility overall. The core evaluative question never changes: does this activity move us toward go-live, or does it merely satisfy someone's preference for how the system ideally works? Decision velocity matters here as well; in T&M, slow institutional decisions are expensive in a direct and visible way, and the project director has to build that speed into the operating rhythm of the project.

T&M also offers one significant advantage that fixed-price does not, which is resource sovereignty. Because the institution is paying for specific talent rather than a guaranteed outcome, the project director can more easily remove a consultant who is not performing and request a replacement without proving a contractual breach. The difference between a senior consultant who understands higher education processes and one who does not is measured in months of rework and unexpected cost overruns.

What Both Models Share

The most common failure pattern in ERP implementations, regardless of contract type, is slow institutional decision-making. In a fixed-price environment, indecision triggers delay and disruption complaints from the consulting partner. In a T&M environment, indecision quietly burns the remaining budget. The contract cannot solve this problem; only leadership attention can. The project director needs standing authority to bring decisions to the right senior executives quickly, and those leaders need to understand that deferral and queue-time carries a real project cost.

Both models also require the project director to manage internal stakeholders as actively as they manage the vendor. The consulting partner is a visible risk; internal stakeholders are a quieter, often more systemic risk. The functional lead who keeps expanding requirements, the department head who will not release staff for testing, the executive who changes priorities midstream: these are project realities that no contract structure can contain. Managing them requires credibility, immediate executive-level access, and transparent decisions from above the project leader.

The final word

The contract with the consulting implementation partner is the operating system of the implementation, but it does not run the project, nor does it guarantee outcomes. What runs the project is the judgment and discipline of the people managing it daily.

Fixed-price rewards boundary control: the project director’s credibility depends on holding the line between what was scoped and what gets delivered, with a pre-negotiated rate card as the mechanism that keeps change orders grounded and controlled. T&M rewards investment thinking: credibility depends on demonstrating that every dollar spent moves the institution toward go-live, and on cutting off the quiet accumulation of non-value-added time before it becomes a budget crisis. The management disciplines are different, but the underlying standard is the same: the project director has to know which game they are playing and manage accordingly.

Ultimately, both models demand the same underlying capacity: the project director must separate what the institution genuinely needs from what various stakeholders sometimes want, and keep the project moving toward the former without losing the trust of the latter. That capacity does not come from the contract. Institutional leaders who understand this stop asking which contract type is right and start asking whether their project director truly understands the model they are operating inside. That is the right question, and it is almost never the one that gets asked until it’s too late.

Six months before launch, the server team recommended buying an Amdahl mainframe-class server for the web layer. We tested one. It was massive, expensive, and clearly built for a world the web was already moving past. It represented a philosophy of abundance and brute force. My team favored small, inexpensive Compaq servers running Windows. We chose the Compaq servers because of budget constraints, not their raw horsepower. That choice worked.

Today, the AI industry equates progress with brute force, assuming larger GPUs are the answer to every problem. History suggests otherwise. The long-term winners in computing have rarely been the vendors with the most horsepower. They have been the ones who integrated tightly, drove marginal costs toward zero, and let technology recede into the background. In today’s Dispatch, I use that lens to examine why Apple and Google are better positioned for what comes next, and why the current alliance between OpenAI, Microsoft, and Nvidia is more architecturally fragile than it appears.

The big picture: training vs inference

The current discourse around artificial intelligence is defined by a frantic, speculative energy. Institutions are watching a massive capital expenditure cycle where corporations rush to secure Nvidia GPUs as if they were vital commodities. This behavior reflects a classic gold rush mentality, characterized by announcements made before contracts are signed and commitments that carry no binding obligation.

To understand why this matters, you have to distinguish between the two fundamental modes of AI operation: training and inference. The industry conflates them, but they demand entirely different architectural and economic models.

• Training is the act of building the model. It requires massive, centralized compute power. This is where Nvidia currently extracts its profits, selling units that cost tens of thousands of dollars to organizations desperate to participate in the boom. This phase is capital-intensive and centralized.

• Inference is the act of using that model to do work. It is the moment a faculty member summarizes a PDF or a student asks for a schedule adjustment. Industry analysis, including work from firms like Sequoia Capital, suggests that by 2030 inference will account for the vast majority of AI compute demand. The existing architectural model for inference does not scale financially. A future where every routine interaction incurs a toll paid to a third-party GPU provider is not a sustainable institutional architecture. Relying on high-wattage, high-cost server hardware for everyday tasks is the Amdahl mistake repeated at scale.

The future belongs to those who can drive the cost of inference toward zero. Two companies have built the architecture to do that. One has built it at the edge. The other has built it at the backend. Both have done it without depending on Nvidia.

Apple’s Zero Marginal Cost Gambit

Apple’s strategy is a masterclass in leverage. While competitors scramble to build larger data centers, Apple has spent years optimizing its own silicon. The Neural Engine embedded in its M-series and A-series chips moves the primary AI workload from the data center to the device itself.

This is a pure architecture play. When a user engages with Apple Intelligence to rewrite an email, sort notifications, or summarize a document, Apple pays zero in cloud costs. The energy and compute are drawn from the device the user already purchased. That approach creates a powerful economic position. It allows for “Everyday AI” that is financially sustainable because it is local.

For tasks that do require cloud processing, Apple has introduced Private Cloud Compute, a system that redefines the relationship between server and user. Apple has constructed a server architecture where user data is cryptographically inaccessible even to Apple’s own administrators. The system deletes user data immediately after the request is fulfilled, and the hardware stack is verified by third-party auditors. This turns privacy from a compliance burden into a core infrastructure layer, offering a path to adoption that does not require compromising data sovereignty.

A nuance worth acknowledging: Apple’s advanced Siri capabilities are being partially powered by Google’s Gemini models. Some observers have read this as evidence that Apple is falling behind. The more accurate reading is the opposite. Apple is treating frontier language models as a commodity input, something to be sourced from the most capable provider at the lowest cost, while maintaining control of the user experience, the privacy architecture, and the device layer. That is exactly the behavior you would expect from a company that understands the infrastructure maturity thesis. You do not build the generator; you control the grid.

The iPhone 17 cycle has financially validated the on-device approach. Apple reported record holiday quarter revenue in January 2026, driven substantially by the AI-enabled hardware upgrade cycle. Consumers are buying new devices specifically to access on-device intelligence. That revenue pattern is structurally different from a subscription model tied to cloud inference costs. Apple’s margins do not erode with usage.

Google’s Vertical Integration as a Strategic Fortress

While Apple has secured the edge, Google has quietly secured the backend. Unlike its competitors, which are dependent on Nvidia’s pricing power and supply constraints, Google spent the last decade designing its own Tensor Processing Units (TPUs). That foresight, which began as early as 2015, has allowed the company to build a compute infrastructure largely immune to the market volatility affecting others.

Google’s TPU ecosystem offers a significant cost-performance advantage over Nvidia-based equivalents for inference tasks. The TPU is not a general-purpose device; it is a workhorse designed for systolic efficiency, moving data less and calculating more. Because Google controls the entire vertical stack, from chip design to model to end-user application, it is insulated from supply chain volatility. They do not pay the NVIDIA or Azure/AWS tax on their own compute infrastructure needs.

The broader market is beginning to recognize this position. Anthropic, one of the most significant AI labs operating today, has placed large orders for TPU capacity. OpenAI, seeking to reduce its Nvidia dependence, has signed agreements with Broadcom, which manufactures custom AI chips that include Google’s TPU architecture. When your most prominent competitors are routing compute through your infrastructure, you are no longer just a search company with an AI capability. You are becoming the grid operator for the next generation of AI applications.

In a 2035 world where AI is utility infrastructure, Google’s ability to control its own input costs will allow it to price aggressively. The company does not owe a margin to a hardware vendor for every transaction. That is a durable structural advantage.

The Fracture That Was Not Theoretical

The current alliance between OpenAI, Microsoft, and Nvidia appears formidable from a distance. It relies on a complex supply chain where incentives are frequently misaligned, and every layer of the stack demands a margin. That fragility is no longer a prediction. It has become a documented outcome.

In September 2025, Nvidia CEO Jensen Huang and OpenAI CEO Sam Altman stood together to announce what was described as a $100 billion strategic partnership to deploy 10 gigawatts of Nvidia infrastructure for OpenAI. The announcement generated enormous market attention. Five months later, no contract had been signed, no money had changed hands, and Nvidia’s own CFO confirmed publicly that the deal remained “a letter of intent” with no assurance that a definitive agreement would be completed. Huang had privately questioned OpenAI’s business discipline and expressed concern about the company’s competitive position against Google and Anthropic. By early March 2026, Huang stated publicly that the original $100 billion deal was “probably not in the cards.” The revised arrangement, reportedly a $30 billion equity stake with no chip-purchase obligations, represents roughly 30 cents on the dollar from the original headline.

This outcome illustrates something important about modular stacks. When your critical infrastructure provider is simultaneously financing your direct competitors, and when the terms of your supply relationship are non-binding announcements rather than executed contracts, you do not have a utility. You have a dependency. Dependencies are renegotiated when the leverage shifts.

The circular financing concern raised by market observers has also proven legitimate. Nvidia committed $10 billion to Anthropic while simultaneously being OpenAI’s primary hardware supplier. OpenAI, in turn, signed a separate binding agreement with AMD, Nvidia’s largest GPU competitor, for 6 gigawatts of hardware. Each company in this stack is hedging against the others. That is not the behavior of aligned partners. It is the behavior of organizations managing dependency risk.

Apple and Google face neither of these problems. Google does not negotiate a memorandum of understanding to access TPUs; it allocates them internally. Apple does not require a third-party GPU vendor’s approval to deploy intelligence features on 2.5 billion active devices. Their supply chains are self-directed.

What This Means for CIOs and Higher Education Leaders

History teaches us that in mature computing markets, vertical integration tends to prevail over modularity at the user-facing layer. This is not a universal rule; the Wintel era demonstrated that modular architectures can dominate for extended periods under the right conditions. But the conditions that sustained Wintel, standardized hardware, stable software interfaces, and low switching costs, do not fully describe today’s AI environment. AI capability is still volatile. Trust and privacy are active stakes for institutional leaders. The switching costs embedded in on-device AI architectures are significant. These conditions favor integrated platforms.

For a CIO charged with stewardship and long-term architectural honesty, the practical consequence is this: vendor commitments built on non-binding letters of intent are not infrastructure. They are options. When you anchor your institution’s AI capability to a vendor whose supply chain is itself dependent on a third party that is simultaneously financing your vendor’s competitors, you have not secured a capability. You have created a dependency on a negotiation you cannot control.

The “Nvidia tax” is the relevant concept here. Any AI service that routes every inference request through high-cost GPU infrastructure passes that cost along, either in direct pricing or in the financial fragility of the vendor providing it. For institutions managing tight operating budgets across multi-year horizons, that cost structure is not sustainable at scale. Everyday AI, the kind that handles scheduling, summarization, advising support, and administrative workflow, cannot be priced like frontier model research. The architecture has to support the economics.

This does not mean institutions should avoid OpenAI’s or Microsoft’s platforms. Many of those tools deliver genuine value. The question is architectural positioning over a five-to-ten-year horizon. Which vendor relationships are building toward lower marginal costs and greater institutional control, and which ones are building toward deeper dependency on supply chains the institution cannot see or influence?

Huang’s reported criticism of OpenAI’s business discipline was not merely industry gossip. It was a signal about how hardware providers evaluate their customers. Discipline, in this context, means the capacity to make binding commitments, manage capital responsibly, and build toward sustainable unit economics. Those are the same standards a CIO should apply to vendor evaluation. If the leading AI infrastructure provider is questioning whether its largest customer has the discipline to execute, that question deserves space in your own vendor risk assessment.

The final word

The temptation for leadership today is to chase the loudest innovations. There is pressure to deploy whatever generates the most excitement and to sign contracts with vendors dominating the headlines. The role of the CIO is to look past the spectacle.

The real Internet was not built during the boom. It was built in the years after, when scarcity forced architectural honesty and every dollar had to address a real constraint. Apple and Google are not winning the AI race because they are spending the most. They are better positioned because they built infrastructure they actually control, optimized for the economics of inference rather than training, and avoided the circular dependencies that are now visibly straining the OpenAI-Nvidia relationship.

The strongest institutional architectures are not built in abundance. They are built when resources are tight and choices are clear. That is the work of stewardship. It is also, not coincidentally, the work that Apple and Google have been doing quietly while the rest of the industry announced deals that were never signed.

But I also see the weight carried by the people already in the building. The team that has kept the lights on, absorbed the ambiguity of transition, and quietly wondered whether the new leader would take the time to understand what they have actually built. A leadership change is disorienting for everyone, not just the person walking in.

I have been in that position three times in my career. In all three environments, change was needed, and I braced myself for difficult work. Yet once I looked past the inevitable friction of any transition, what I found was remarkable. The teams I inherited were exceptional. They did not require upgrades. They needed clarity, transparency, and someone willing to “sweat the details” of hard work with them.

In today’s Dispatch, I want to pull back the curtain on organizational dynamics in the midst of change and explore why the real leadership is not found in trying to hire your way to a new culture, but in the patient work of leading the team you actually have.

The big picture

Higher education leaders are inundated with management literature preaching the gospel of “talent density,” a philosophy popularized by companies like Amazon and SpaceX, which suggests that the only path to high performance is to pay top salaries and continuously cull the bottom of the organization. For higher education and established enterprises, this model is not just impractical; it is a dangerous distraction that blinds leaders to the talent that their institution actually contains.

Most universities operate with salary bands that cap well below the starting rate at outside firms. When leaders fixate on a labor model designed for venture-backed hyper-growth, they fail to optimize for the conditions they actually face. The result is that capable people, working inside incoherent systems, are mistakenly read as the problem when the structure around them is the real constraint. Good leadership, in this context, is not waiting for a better team to arrive. It is building the structural coherence that allows the people already present to do exceptional work.

What the Team Already Knows

Before any new leader assesses their team, the team has already assessed them. They have watched previous transitions. They know which new arrivals listened and which ones performed. They have seen the reorgs, the rebranding exercises, and the new strategies that went nowhere. Their skepticism, if it exists, is earned and reasonable.

This is worth naming directly. The staff member who seems disengaged may have offered ideas that were ignored for years. The one who pushes back may be protecting something that is really important. The institutional knowledge that looks like resistance often turns out to be the most accurate map of the terrain available. Leading the team you have means starting there, with curiosity rather than a verdict.

The proof of this approach is not abstract, but is found in the continuity it produces. CIOs who invest deliberately in the people already present often find that their own succession comes from within. That outcome is not accidental. It is the direct result of treating the inherited team as an asset to develop rather than a problem to replace.

The “Upgrade” Trap

The danger for incoming leaders is a particular kind of magical thinking: the belief that transformation is primarily a hiring problem. They undervalue the people in the room by comparing them to a theoretical candidate who doesn’t exist, and in doing so, they neglect the development of people who are staying for the long term.

As argued in Star Quarterbacks and Shadow IT, this logic leads leaders to chase the confident, fast-moving builder who promises quick results. That “star” often bypasses collaboration, ignores enterprise architecture, and leaves behind technical debt for others. In a university, a genuinely high-performing technologist is not someone who writes code ten times faster. It is someone who stays ten years longer. Institutional memory and loyalty are force multipliers that individual heroics rarely provide.

The lesson often comes early for leaders who have been on the other side of this dynamic, who were themselves passed over or underestimated before someone took a chance on potential rather than credentials. Most teams contain people like that, individuals who have been overlooked because they did not fit someone’s mental image of the ideal hire. When leaders look for competencies rather than credentials, they often find that the quiet contributor was simply waiting for the right conditions.

Coherence is the Leadership Lever

When leaders accept that they cannot restructure every role and cannot recruit from the top of the technology labor market, the one variable they can actually control is coherence. A group of capable people moving in deliberate alignment will always outperform a group of “star quarterbacks” pulling in their preferred directions.

The mechanism for this is straightforward. The discipline of the weekly team meeting, practiced relentlessly, is one of the most effective coherence tools available. The format is simple: review what was just done, identify what comes next, and ask for feedback. It is not exciting, but it’s relentless. And it works. Steve Williams, a retired Army Colonel who led technology teams with this approach, demonstrated that military-grade meeting discipline translates directly into institutional settings. The consistency of the gathering matters as much as what happens inside it.

The weekly meeting is not micromanagement; it is the calibration mechanism that creates coherence. By gathering regularly to track progress, solve complex problems, and navigate stakeholders together, the team’s capacity rises. When shared standards are enforced and the details are scrutinized together, the gaps in individual capability are filled by the strength of the process. Excellence becomes a shared habit rather than an exception. Indiana’s football success is the newest example of this in action.

The Leader as Shock Absorber

Most teams underperform not because of skill deficits but because of distraction: conflicting priorities, political noise, and the anxiety that accumulates when leaders are vague. As discussed in Resilience in the Fog of Uncertainty, uncertainty creates shadows, and it is the leader’s primary job to turn the lights on and make things clear.

When leaders absorb the institutional ambiguity that would otherwise cascade down to the team, the group’s available capacity increases immediately. When the “VIP queue jumpers” are managed at the leadership level rather than handed off to the team, the people doing operational work can focus. Protecting the team from noise is not a secondary function of leadership. It is often the most consequential one.

Cultures built on individual heroism, on one “star quarterback” keeping fragile systems functional through personal effort, are structurally brittle and will eventually fail. Real operational quality comes from processes designed so that the departure of any single person does not create a crisis. Cross-training and documentation are not administrative overhead. They are the architecture of a strong and resilient team.

Building the Skills You Cannot Buy

When ready-made capabilities cannot be purchased from the open market at scale, leaders must turn to developing them internally. As argued in Why Your IT Hiring Stalls and How to Fix It, the best approach is to hire for competencies like curiosity, initiative, and accountability, then train for the skills that the work requires. This demands real budget commitment; at a minimum, four percent of the personnel budget directed toward training and development annually is a reasonable standard.

Growth happens when people are given responsibility just beyond what they believe they can handle, and when the work itself becomes the development ground. There is a dignity in the long-tenured employee that the technology sector systematically undervalues. These staff members know where complexity lives, how the ERP system actually functions, and how to navigate institutional culture in ways that no document can capture. The goal for leaders is not to bypass that knowledge. It is to unlock it.

A Note on the Exceptions

Every leadership transition includes genuinely difficult cases: the actively resistant employee, the non-performer who operates in the gaps of weak oversight. These situations are real and should be addressed quickly. The process is less about confrontation than about applying consistent organizational standards, clarifying expectations, and raising the bar uniformly. When that structural pressure is applied, the situation usually resolves itself. The person either rises or self-selects out.

But these cases represent a small fraction of any team. The error is allowing them to become the dominant lens for how a new leader reads the entire organization. When all attention goes to the few who are leaving, the many who are staying go unled.

The final word

The belief that a better team is waiting somewhere off the org chart, that transformation is primarily a matter of acquiring different people, is a way of avoiding the harder discipline of actually leading. The team already present is, in most cases, more capable than the circumstances have allowed them to demonstrate.

What they need is a leader willing to do three things consistently: provide clarity about what matters and why, protect them from the organizational noise that fragments attention, and invest in their development over time. When those conditions exist, the performance of an average-tenured, stable workforce can be genuinely surprising. The missing variable is rarely talent. It is coherence. And coherence is something a leader builds, not something they hire.

That experience shapes how I view the current AI boom. The technology is real. Much of the hype is not. In today’s Dispatch, I want to apply the lessons of the Internet’s boom and bust to the present moment by projecting forward to 2035 and asking a quieter, more durable question: what remains of AI after its boom and bust cycle ends. The aim is not to slow adoption, but to help leaders adopt AI now in ways that will still hold when capital tightens, expectations reset, and the real work begins.

The big picture

From the vantage point of 2035, the AI boom of the mid-2020s no longer feels novel or unpredictable. Capital was abundant, and anxiety was widespread. Every CEO wanted an AI strategy, quickly. Leaders spoke in absolutes. Vendors promised transformation at scale. Institutions moved fast, often faster than their data, culture, and governance could support. Speed became proof of seriousness. Caution was framed as resistance.

This was not new.

The dot-com era followed the same arc. Vision surged ahead of capacity. Charisma outran discipline. The technology did not fail. The economics did. When capital tightened, the gap between promise and readiness became impossible to ignore. AI followed that same path. The boom ended not in collapse, but in clarification.

The thesis is straightforward. The bust did not kill AI. It revealed what it was actually for. Real innovation did not emerge during the period of abundance. It emerged after it. By 2035, AI is no longer discussed as a breakthrough. It is infrastructure.

The noise faded. What remained was useful.

What the Boom Looked Like While We Were in It

The atmosphere of the mid-2020s was defined less by the technology itself than by the social pressure surrounding it. Organizations stood up AI transformation offices. Job descriptions shifted faster than job content. Employees worried quietly about replacement while leaders felt pressure to move before fully understanding what they were buying. Acting quickly became a proxy for seriousness. Caution was reframed as resistance. The only legitimate question was whether you would lead or fall behind.

That pressure had a structural cause. Capital was abundant, and abundance removes the friction that disciplines decision-making. When money is available and the competition is moving, the cost of waiting feels higher than the cost of a mistake. Vendors understood this and priced their proposals accordingly. Contracts grew large and long-term. GPU costs were treated as inconveniences rather than constraints. Custom models were launched without a clear path to maintenance or return.

The data wall arrived quietly. Institutions discovered that generic models built hastily on top of large language models were too prone to error and too disconnected from core institutional data to be trusted for real decisions. The unified, governed data required to make AI agents reliable and predictable simply did not exist in most organizations. Pilot projects proliferated but were rarely evaluated with rigor. Productivity claims multiplied faster than evidence. The gap between demonstration and production-grade performance was wider than the rhetoric had suggested.

What changed was not belief in the technology. It was the recognition that the conditions enabling rapid adoption, abundant capital, diffuse accountability, and deferred governance, had masked that gap between hype and reality rather than closed it. When those conditions shifted, the gap became visible and expensive.

What Actually Survived

Once capital tightened and enthusiasm cooled, a smaller, sturdier set of patterns remained. These are the changes that defined AI’s long-term impact.

AI Became a Base Layer, Not a Headline

By 2035, drafting, summarizing, and first-pass analysis, the early cognitive work of surfacing patterns and framing problems, are no longer treated as skills. They are utilities, as ordinary as spellcheck or search. AI did not replace most professionals. It compressed layers of low-complexity work. Tasks that once absorbed hours became instantaneous. One person, properly supported, could now do what several once did.

The constraint shifted. Judgment became the differentiator. The people who advanced were not the best prompt engineers, but those who could frame problems clearly, assess outputs critically, and accept responsibility for decisions. That shift exposed a simple truth: the use of AI amplifies judgment, it does not replace it. AI reshaped how work was done, but it did not remove the need for thoughtfulness and accountability.

Standalone Tools Disappeared into Systems

The boom years were defined by portals. Log in here. Ask the model there. Leaders told staff to “use AI” without changing how work actually flowed. The assumption was that exposure alone would increase efficiency. It did not. These tools lived at the edges of work rather than inside it, disconnected from the systems where decisions were made and value was created. Adoption stalled as complexity eclipsed curiosity.

By the early 2030s, AI had moved into the core. It was embedded inside ERP, CRM, finance, HR, and operational platforms, handling reconciliation, anomaly detection, routing, and forecasting quietly. We stopped treating these systems as software and started treating them as “digital workers,” entities with defined roles, access privileges, and performance metrics, formally integrated into the org chart alongside their human counterparts. No one talked about “using AI” anymore. They talked about closing the month faster, reducing errors, and handling more volume with the same staff. AI became plumbing. That shift mattered because it aligned technology with measurable outcomes instead of novelty, and once aligned, it finally endured.

The cycles of 'log in to this AI portal' faded. The lasting change wasn't just embedded AI inside software, but the rise of autonomous agents working across that software. By 2035, you don't ask a chatbot to summarize a customer issue. An agent notices the issue, crosses silos to investigate shipping and billing data, processes the return, and updates the CRM; only alerting a human if it hits a roadblock. AI stopped being a dutiful assistant waiting for a prompt and became an active participant in operations.

Governance Stopped being Aspirational

During the boom, AI governance was largely rhetorical. Organizations published principles and ethical statements, but enforcement was thin, and controls were immature. Responsibility was diffused. Risk was treated as theoretical. That posture did not survive sustained use, public scrutiny, or the first real failures.

By 2035, model risk is managed the way cybersecurity risk was a decade earlier. Data lineage is tracked. Outputs are audited. Boundaries are explicit. Some decisions remain automated. Others are deliberately human. This shift was not driven by virtue. It was driven by lawsuits and liability. Institutions relearned a familiar lesson from the Internet era. Trust does not emerge from intention. It requires structure and process.

At the same time, human accountability became a much more critical resource. Demand surged for those with the discernment to review outputs for nuance, the courage to make strategic decisions under uncertainty, and the emotional intelligence to negotiate complex human dynamics. The primary function of the knowledge worker shifted profoundly from being the engine of execution to becoming the supervisor of autonomous agents. Their value is defined by their ability to manage the critical exceptions where models failed, to rigorously define the ethical and operational guardrails before the work began, and to step in specifically for those high-stakes, sensitive interactions that still demanded genuine human empathy.

Implications for AI-Infused Universities

In higher education, the period after the boom clarified that becoming an AI-infused university was not about strategy or choosing a tool. It was about aligning different forms of AI to different kinds of work. The institutions that adapted best understood that not all AI was the same, and not all use cases carried the same risk, cost, or payoff. Over time, four distinct domains emerged, each requiring a different posture.

1. Research and discovery: game-changing AI, inward-focused. This was where large-scale, high-cost AI investments worked. High-performance computing and specialized models accelerated research in areas where scale and complexity mattered. The payoff was not automation, but amplification. Literature review, data exploration, and hypothesis testing compressed dramatically, allowing researchers to spend more time on theory, design, and interpretation. The universities that succeeded treated this infrastructure as shared and mission-critical, not as a collection of bespoke labs. Governance mattered here, but so did patience. These investments paid off over time, not in headlines.

2. Administrative operations, process, and ERP: game-changing AI, customer-focused. This domain delivered some of the most visible gains for students, faculty, and staff. Embedded inside ERP, CRM, advising, finance, and HR systems, AI reduced friction that had long defined the university experience. Routing improved. Reconciliation became faster and more accurate. Forecasting became more reliable. Importantly, these gains came from deep integration into core systems. Universities that treated AI as part of process redesign, rather than an overlay, finally saw durable improvements in service and efficiency.

3. Personal productivity for employees: everyday AI, inward-focused. Most gains came not from licensing tools, but from disciplined use of AI already embedded in productivity platforms. Drafting, summarizing, analysis, and preparation became faster and more consistent. The real investment was in training. Those institutions that focused on AI literacy helped employees move from execution to orchestration, enabling more critical and skeptical thinking rather than replacing it. Everyday AI raised the cognitive bar for staff work rather than lowering it.

4. Teaching and learning: everyday AI, customer-focused. In the classroom, AI did not replace teaching. It reshaped expectations. As first-pass work became automated, assignments shifted toward interpretation, synthesis, and ethical reasoning. Faculty emphasized how to question AI output rather than how to produce it. Students learned that AI was a tool to engage with, not a shortcut to avoid thinking. Universities that succeeded framed AI literacy as a core educational outcome, not a technical skill, preparing students for a world where judgment, context, and responsibility mattered more than speed.

Taken together, these four domains explain why the AI-infused university was quieter and more disciplined than many expected. The most successful institutions did not deploy AI everywhere at once. They matched the right kind of AI to the right kind of work, invested deliberately, and governed consistently. Overall, they built capabilities that survived the hype cycle and remained valuable long after the boom ended.

The Leadership Lesson that Endured

The boom rewarded a particular kind of leader. Vision was currency. The ability to tell a compelling story about the future and move quickly in its direction signaled serious leadership. Skepticism was reframed as resistance, or the sign of someone who’s behind. That phase of the hype cycle feels decisive while it lasts, and it always ends.

When capital tightened, the center of gravity shifted. Vision gave way to stewardship. Leaders were no longer judged by the ambition of their promises, but by what they could sustain under pressure. Institutions confronted the risks and tradeoffs they had minimized previously. Scarcity imposed the discipline that abundance had deferred.

The technology market followed this correction. The bust pushed vendors away from bloated per-seat licensing toward consumption models where businesses paid only for work completed by autonomous agents. Smaller, more efficient models displaced the assumption that scale alone produced value. Institutions that invested in data quality and process clarity were positioned to take advantage of those shifts. Those who had not found themselves holding expensive infrastructure with uncertain returns.

What the correction clarified, more than anything, was the relationship between credibility and outcomes. The leaders who lasted were not the ones who had been most enthusiastic. They were the ones who consistently managed the space between promise, resources, and results. They resisted innovation theater and focused on measurable efficiency, reduced risk, and clear ownership when those qualities were less celebrated. By 2035, those qualities were the ones that defined success with AI.

The final word

From the vantage point of 2035, the AI boom looks less like a turning point and more like a rite of passage. Every major technology follows this arc. Exuberance gives way to overreach. Overreach invites correction. Maturity comes later. The Internet followed this path. AI was never going to be different. The mistake was not enthusiasm. The mistake was believing enthusiasm could substitute for discipline.

By 2035, AI is no longer exciting. It is reliable. It is embedded. It is constrained.

The institutions that emerged stronger were not the ones that moved fastest. They were the ones who prepared for the moment when budgets tightened and questions sharpened. They trained people to orchestrate, not just execute, recognizing that AI required deeper thinking, not less of it. They invested in boring systems that scaled and worked under pressure, the core systems through which work is done in 2035.

From the other side of the hype cycle, the lesson is clear. Do not fear the bust. That is where the real work begins.

I asked the team to brief me on the issues. The reactions split instantly along style lines. The assertive members felt challenged and wanted to fire off a quick response. The accommodators worried the relationship was slipping and looked for ways to ease the tension. The analysts wanted to pause and make sure nothing had been missed. Same email, three interpretations. None of them is wrong. All of them are predictable.

In today’s Dispatch, I use that moment to walk through the negotiation escalation ladder and the three styles that shape how we perceive it. The goal is simple. When you understand both the rung and the instinct driving the reaction, you can keep the conversation steady, protect the relationship, and guide the work back to solid ground.

The big picture

Every negotiation starts in a reciprocal relationship. Trust exists. Expectations are clear. You believe you are solving a shared problem. Most of the time, that is exactly what happens. But when reciprocity breaks, it does not drift. It climbs.

Two ideas help explain why. The first is the negotiation escalation ladder, a simple map of how conversations rise from quiet bargaining to personal attacks. The second is the set of three negotiation styles, articulated by Chris Voss.

• Assertive. Moves fast, drives toward outcomes, and treats time as the primary currency. Strength is momentum; risk is escalation when challenged.

• Accommodator. Values connection and harmony, listens openly, and builds trust through dialogue. Strength is rapport; risk is conceding too early to ease tension.

• Analyst. Thinks slowly and precisely, focuses on logic and preparation, and avoids surprises. Strength is clarity; risk is withdrawal when things feel irrational.

Each style brings its own logic to the conversation. Each reacts to pressure differently. When the ladder appears, style determines whether the conversation slows down, speeds up, or blows up. These instincts shape the very meaning people assign to differences. What feels like a small shift to one style can feel like a breach of trust to another. Leaders who miss these signals often unknowingly. Leaders who see them clearly know when to pause, when to probe, and when to let silence do the work.

You cannot de-escalate without understanding both. The rung tells you what is happening. The style tells you how it feels to you or your counterpart. A demand that seems manageable to an Analyst may feel like betrayal to an Accommodator and a direct challenge to an Assertive. Leaders who miss this confuse personality for principle. They assume the reaction is about values or intent rather than instinct. Leaders who see the distinction protect both the relationship and the institution.

What follows is a walk-through of the negotiation escalation ladder, rung by rung, looking at how each style processes the climb and how negotiation principles from Chester Karrass and Chris Voss can help keep things grounded.

Rung 1: Bargaining and the Extreme Anchor

This is the first sign that reciprocity is slipping. Someone reopens a settled point. Someone asks for something they know you cannot give. In sales, you see the extreme anchor. A number so unreasonable it is meant to rattle you and force concessions.

• How Assertives react. Assertives read the anchor as a challenge. Progress matters. Time matters. An extreme anchor feels like friction. Their risk is retaliation. They counter-anchor. They escalate. The negotiation becomes a contest instead of a conversation.

• How Accommodators react. Accommodators feel the strain in the relationship. They worry the other side is upset or under pressure. Their risk is premature concession. They give something away to avoid tension, long before the real negotiation begins.

• How Analysts React. Analysts see the anchor as wrong. Not offensive, but inaccurate. Their risk is withdrawal. They want to check the data. They want to run the numbers again. Momentum dies while they seek clarity.

The disciplined response. Voss gives the Accommodator a lifeline with calibrated questions. “How am I supposed to do that?” slows the tempo and forces the counterparty to justify their position. Karrass gives the Assertive a simple discipline. Do not take the anchor at face value. Test it. Ask for the reasoning, the constraints, and the context. Extreme anchors collapse under scrutiny because they were never meant to hold weight. For Analysts, the key is pace. Give them a moment, then label the pressure, which keeps them in the room instead of retreating into spreadsheets.

Rung 2: Appeals to Outside Authority

When bargaining fails, many counterparts climb the negotiation escalation ladder. They introduce a silent third party. The CFO. A policy. A nameless board member. It is the invisible No. You cannot negotiate with someone who is not in the room.

• How Assertives react. Assertives see the outside authority as an obstacle to break. They want to escalate the issue upward. Their risk is overreach. They push too hard, too quickly, and damage the relationship.

• How Accommodators react. Accommodators take the claim at face value. They assume the authority is real. Their risk is internal surrender. They negotiate against themselves because they trust what they hear.

• How Analysts react. Analysts want proof. They ask for documents, minutes, or policy citations. Their risk is turning the conversation into an audit. The rigor is correct, but the timing is terrible. It signals distrust and invites further escalation.

The disciplined response. Voss suggests labeling the constraint. “It seems like this policy is driving the concern.” When you do this, the counterparty often shifts from being a messenger to a collaborator. Karrass tells the Assertive to change posture. Instead of breaking the authority, ask for a proposal and say to take back to them. The request restores reciprocity and keeps the tone of the negotiation where it belongs.

Rung 3: The Use of Direct Authority

Here, the mask drops. No more CFO. No more policy. No more straw man to argue with. The counterparty stops signaling outside constraints and starts signaling control. “We are doing it this way.” Reciprocity is formally suspended.

• How Assertives react. Assertives experience this as a direct challenge. Their risk is explosion. They match force with force. The conversation becomes a power struggle and the relationship fractures.

• How Accommodators react. Accommodators feel rejected. Their risk is resentful compliance. They say yes to restore calm, then slow-roll the work later because the agreement never felt fair.

• How Analysts react. Analysts see the move as irrational. It breaks the logic of the process. Their risk is disengagement. They shut down until the other party returns to reason.

The disciplined response. Karrass suggests the Accommodator use a conditional yes, asking for an immediate concession. “We can do that, provided the timeline shifts.” This protects boundaries without confrontation. Voss gives the Assertive the mirror. Repeat the last three words as a question. “Doing it this way?” The counterparty often backs up, clarifies, or softens. For Analysts, narrate the logic. Acknowledge the command, then outline the consequences. It restores order to their thinking.

Rung 4: Appeals Above You

This is the end run. Your counterparty bypasses you and goes to your leadership: presidents, provosts, or boards. They are not negotiating. They are repositioning.

• How Assertives react. Assertives view this as betrayal. Their risk is scorched earth. They start telling colleagues the other party is untrustworthy. The issue becomes political theater, not problem-solving.

• How Accommodators react. Accommodators internalize the bypass. They think they failed to keep the relationship warm. Their risk is apology. They undermine their own authority to regain harmony.

• How Analysts react. Analysts see the bypass as a process violation. Their risk is procedural retaliation. They write long memos that no one reads. While they document the misstep, the decision moves on without them.

The disciplined response. Karrass is clear that the best way to manage an end run is to prevent it before the negotiation starts. This requires real alignment among leaders so they and their leadership speak with one voice. Voss reinforces the same idea through anchoring. Senior leadership should already understand the issue, the likely ask, and the boundaries. When the counterparty tries to bypass you, they encounter a unified front. The shortcut fails, and the negotiation returns to where it belongs.

Rung 5: Attacking and Discrediting

The final rung turns personal. The counterparty targets you, not the issue. They complain to your boss or others about your tone, integrity, or motive. Or, they lobby a similar attack against a member of your team. The attack is meant to shake you off your position and quickly gain a concession to “make up for the faux pas.”

• How Assertives react. They fight back. They protect their honor. The negotiation collapses into a brawl.

• How Accommodators react. They absorb the attack. They wonder if it is true. They concede to prove they are reasonable.

• How Analysts react. They freeze. Personal attacks break the model. There is no logic to respond to.

The disciplined response. Voss offers one path. Use a low, calm voice. Label the emotion. “It sounds like you are frustrated.” Then stop talking. Silence becomes the pressure, not the attack. The counterparty often backs down because the attack has nowhere to land. For Karrass, preparation matters. Bring colleagues into earlier conversations. Shared memory protects credibility when accusations drift upward.

The final word

Every leader finds themselves on the escalation ladder at some point. The problem is not the pressure of the moment. The problem is treating that pressure as a signal to push harder, to climb to the next rung of the ladder. Escalation follows a predictable pattern, but your response determines whether it accelerates or settles.

Negotiation is less about technique and more about self-awareness. Leaders need to understand their own style before anything else. You cannot slow a negotiation that is escalating if you cannot see your own instinct to climb with it. The task is clear: protect the relationship and the work you are trying to accomplish. Know which rung you are on and choose the response that keeps the conversation steady.

Silence remains the most reliable brake. Use it early and without apology. A well-timed pause or a simple “let me take this under advisement” creates space for the temperature to drop and gives everyone room to return to collaboration. These small moments of quiet help keep you grounded when others feel pulled to climb.

That understanding of institutional mechanics is exactly why he famously turned down the position of Director of National Intelligence (DNI) in 2005. On paper, the DNI role looked like the pinnacle of influence. It was designed to coordinate the entire US intelligence apparatus. Yet Gates realized the role was flawed because it severed responsibility from authority. The DNI would be held responsible for the performance of the intelligence community, yet the major agencies, such as the NSA and the NRO, remained under the budgetary and operational control of others. Gates argued that without direct command over personnel and dollars, the DNI was less like a CEO and more like a “powerful congressional committee chair.” He refused to take a job where the structural design guaranteed friction rather than alignment.

I find myself thinking about Gates’ refusal when I look at the sudden proliferation of the “Chief AI Officer” role. Just as with the DNI, we are witnessing a rush to solve a complex coordination problem by creating a figurehead who holds few if any of the operational levers required to move the machinery. In today’s Dispatch, I want to explore why organizations are rushing to appoint Chief AI Officers and why, over the long term, this structural choice may be far less impactful than its proponents believe.

The big picture

Large organizations respond to technology-driven uncertainty in predictable ways. When boards and presidents feel pressure to demonstrate responsiveness to a shifting technology landscape, they reach for the one lever they control quickly: they add a box to the organizational chart. The current wave of Chief AI Officer appointments fits this pattern precisely. It signals motion in a moment when uncertainty demands reassurance. It is a rational-legal defense mechanism, yet it is a strategic mistake.

We’ve seen this movie before. Two decades ago, institutions rushed to name Chief Digital Officers and Chief Innovation Officers. These roles often struggled not because the people were unprepared, but because the mandate was abstract. They owned a concept rather than a system. They spoke the language of the future without control over the mechanisms of the present. They were tasked with changing the culture, but they lacked the budgetary and operational levers to move the machinery.

The current moment repeats this pattern. The appointment of a Chief AI Officer confuses visibility with capacity. It offers reflexive signaling when what is required is institutional rewiring. The risk is not that AI leadership is unnecessary. The risk is that by isolating it, the enterprise convinces itself that the problem is contained. It treats a pervasive shift as a domain to be managed. This provides a sense of temporary relief to the cabinet and the board, but it creates a long-term structural deficit.

The Physics of Organizational Structure

Artificial intelligence is not a vertical function. It does not sit cleanly alongside Human Resources, Finance, or Advancement. It behaves more like literacy, numeracy, or electricity. It is a horizontal capability that permeates every role, every workflow, and every decision surface of the university. It will touch everything and everyone.

When institutions attempt to centralize a ubiquitous capability, friction follows. The physics of the organization are immutable. If you force a horizontal capability through a vertical funnel, decision velocity slows. Approvals must pass through a new checkpoint. Experimentation becomes permission-based. Innovation acquires more and more queue time. The organization does not become smarter. It becomes slower.

In the complex reality of a modern university, a Chief AI Officer enters the cabinet with high visibility but little institutional capital. Their authority is implied rather than granted by the budget or the organizational chart. To act, they must negotiate with leaders who already control the budget, the enterprise systems, the faculty governance structures, and the workforce. They are an influencer, not a decider.

This creates a persistent negotiation tax. Every initiative requires a coalition. Every policy requires a treaty. Energy that should be spent on integrating AI into teaching, research, and administration is instead consumed by alignment meetings and jurisdiction management. The organization spends its limited bandwidth determining who is allowed to make a decision rather than making the decision itself.

Furthermore, the incentives never really align. The Chief AI Officer must demonstrate novelty to justify the existence of the role. They are structurally pressured to announce partnerships, launch pilots, and generate news releases. They must show that the “AI strategy” is a tangible product. Meanwhile, deans, vice presidents, and CIOs are incentivized to preserve continuity, manage risk, and deliver core outcomes with limited disruption. The result is structural tension rather than new momentum. The “change agent” is pitted against the operating reality of the institution, and in a university, the operating reality almost always wins, eventually.

The Abdication of Responsibility

The most counterproductive effect of naming a Chief AI Officer is not duplication, it’s disengagement. Once a specialist exists, others step back. The cognitive load is passed on. The Chief Information Officer may retreat to the comfort of cybersecurity and ERP management, believing the “AI person” is handling the strategic technology shift. Academic leaders may treat AI as an external service to be consumed rather than a pedagogical shift to be metabolized. Administrative leaders defer automation questions to the expert instead of rethinking how their own teams work.

This fragmentation is fundamentally lethal during a transition involving general-purpose technology, as the leaders who own the budget, the workforce, and the risk profile must internalize the shift themselves rather than delegating sense-making, which serves as a form of institutional avoidance. Because AI governance is primarily institutional rather than technical, touching ethics, employees, privacy, and procurement, these responsibilities already belong to existing leadership roles and governing bodies. Creating a parallel AI structure does not strengthen the organization's internal capacity for change; instead, it effectively bypasses it.

We do not need a new policy author to invent rules for generative text. We need a General Counsel who understands how existing liability laws apply to probabilistic systems. We need a Provost who understands how AI reshapes assessment, tenure, and academic integrity, and who can lead that debate in the faculty council. We need a CFO who understands where productivity gains are real and where they are illusory, ensuring we do not pay for efficiency we never capture. We need a CIO who ensures the data layer is robust enough to support the inference engines of the future. When these leaders abdicate their role in AI to a specialist, they hollow out the institution’s capacity to adapt. They treat the technology as a product rather than a new baseline.

The final word

While the impulse to appoint a Chief AI Officer is an understandable response to uncertainty, providing a visible answer to anxious trustees and stakeholders, it remains structurally unsound because it treats a fundamental shift in infrastructure as a discrete project all to its own. Resilient institutions will resist this quick fix in favor of the slower, harder work of forcing their existing leadership and departments to become fluent in the new reality; the CIO must own the architecture, academic leaders must own the pedagogical shifts, and finance must own the economics.

This approach demands patience and leadership that is willing to hold all departments accountable for internalizing these changes, rather than hiring a proxy to do the learning and planning for them. If the current leadership table cannot carry that additional load, the solution is not to build a bypass around them, but to honestly confront the harder truth that the organization does not need a Chief AI Officer; it simply needs leaders capable of governing in the environment that now exists.

This puts leaders such as myself in a structural bind. To challenge these policies is to risk being viewed as ineffective at managing unpopular workplace changes; to enforce them is to be seen as an unsympathetic leader indifferent to employee well-being. This friction is difficult to resolve, yet it now masks a bigger, more consequential risk.

In the era of GenAI, remote work jobs can be more detrimental to a career than many realize. The digital efficiencies of remote work often turn individuals into “executors” of discrete tasks, the exact domain where GenAI thrives. In today’s Dispatch, I explore how remote work is easily automated and how workers can transition to becoming orchestrators and integrators. Those who remain tethered to remote roles may find their opportunities curtailed as GenAI begins the systematic automation of work.

The big picture

Shane Legg, a co-founder of DeepMind, recently proposed a framework that should cause any institutional leader to pause. He calls it the “Laptop Test.” The premise is simple: if your job can be done entirely on a laptop, using an internet connection, it is pure cognitive labor. It is work that has been abstracted from the physical world. In the language of systems, it is a role defined entirely by digital inputs and outputs.

For the last four years, this abstraction has been celebrated as a triumph of flexibility. Remote work was framed as a long-overdue modernization of the white-collar office. However, seen through the lens of technology cycles, this move toward “purely digital” roles may have been an error for the individual worker. By stripping away the physical presence, the hallway negotiation, and the localized context, these jobs have been inadvertently prepared for a seamless transition to artificial intelligence.

If a job exists solely behind a screen, it is a job that can be measured, mapped, and replicated by an agentic system. The Laptop Test suggests that the very features that made remote work attractive, the lack of friction, the focus on discrete tasks, and the digital-first interface, are the exact conditions that make a role susceptible to commoditization. When you remove the person from the room, you remove the “human buffer” that protects a position from being reclassified as a routine service.

The Erosion of the Human Buffer

Decades of leadership within higher education suggest that the most valuable work often occurs in the margins. It encompasses the conversation that follows a meeting, the ability to sense the tension in a room during a budget discussion, and the social connections that sustain a complex system. This may be termed the "human buffer."

Remote work optimizes for the queue. It forces work into tickets, Slack threads, and scheduled Zoom calls. This process of “cleaning up” the work, making it legible for a digital environment, is a form of work simplification. When a role is optimized for execution at a distance, it essentially creates a roadmap for an AI to easily replace it.

The remote executor is highly vulnerable here. Without physical presence, their routine tasks are easily abstracted. A remote project manager who only updates spreadsheets and sends follow-up emails is no longer competing with other project managers; they are competing with the marginal cost of an AI agent that can do the same task for a fraction of the price. The physical office, for all its perceived inefficiencies, provides a layer of institutional complexity that AI cannot yet navigate. It requires a level of “embodied” judgment that doesn’t exist on a screen.

The Broken Apprenticeship

Perhaps the most significant long-term risk of the remote-work era is the cracking of the career ladder. Institutional leadership is not a set of skills you learn from a manual; it is a craft learned through observation, replication, and osmosis.

Most executives learned how to lead by watching others navigate the messy, non-linear realities of organizational life. They watch how others handle an executive in a bad mood or how someone built consensus for an ERP project that no one wanted, but everyone needed. These are the moments that turn an executor into a leader.

In a remote environment, junior staff are increasingly isolated. While they observe the finished product, the polished presentation, or the final decision, they are denied the “queue time” required for professional growth. They are not privy to the negotiations that lead to consensus, nor do they pick up the unstated objections that shape a strategy. Without this immersion, remote work risks becoming a career dead end. The result is a generation of “expert executors” who possess technical proficiency but lack the organizational literacy required to transition into orchestrator roles. As AI assumes the burden of execution, these workers may find themselves without a viable path, as the middle rungs of the professional ladder are effectively hollowed out.

The Credibility Gap and the Currency of Trust

As work moves further into the AI era, the value of a digital artifact, a report, a line of code, or an email, will continue to drop toward zero. If anyone can produce a high-quality memo with a prompt, then the memo itself is no longer the source of value.

The value shifts to the trust and credibility of the person delivering it. This is where the remote worker faces a structural disadvantage. Credibility is a form of social capital that is built through sustained, high-fidelity interaction. It is rooted in listening and negotiation, disciplines that are inherently difficult to master remotely.

The work of thinkers like Voss and Karrass demonstrates that negotiation extends far beyond the settlement of terms; it involves the precise calibration of human emotion and organizational incentives. Within the environment defined by the “Laptop Test,” these essential dynamics are often flattened. The deep, high-stakes alignment work is increasingly traded for the shallow, high-volume output of digital tasks. While AI will inevitably win on volume and raw horsepower, human workers maintain a comparative advantage only through integration and the exercise of judgment.

The Expert’s Ego and the Skeptic’s Trap

There is a certain irony in how many workers view this shift. They believe their roles are “too complex” for AI to touch. This is what Shane Legg means by saying “experts are behind the curve.” They judge AI by where it was yesterday, not by where its going.

This belief is often more ego than evidence. If your role as an “expert” is to synthesize data and provide recommendations via a laptop, you are on borrowed time. The “Ideal Type” of the rational bureaucrat is exactly what AI is designed to replace.

The workers who will survive are those who recognize that technology is infrastructure, not a novelty. They will use AI to handle the raw scale of cognitive labor while doubling down on the “human-centric” work of alignment. They understand that the messiness of organizational reality is not solved by remote-work software; it is the environment where human value is most clearly demonstrated.

The final word

The office must no longer be viewed as a place for confinement but rather as a laboratory for professional survival. The “Laptop Test” is a warning, not a destiny. Avoiding the dead end of digital labor requires moving work towards orchestration.

This means acknowledging that while AI can handle the “what” of our work, it struggles with the “why” and the “how”. Integration requires bringing back work into the physical and social fabric of the organization. It demands a focus on elements that cannot be captured in a Zoom transcript: the building of trust, the navigation of unspoken incentives, and the mentorship of the next generation.

For the senior leader, the structural challenge lies in resisting the urge to flatten institutional complexity for the sake of remote-work efficiency; they must instead preserve the professional rungs that allow for long-term growth. Simultaneously, the individual worker must ensure their value is not confined to the boundaries of a screen. If a role can be reduced entirely to digital inputs and outputs, the occupant is merely a service provider in a market soon to be flooded by nearly free alternatives.

Survival in the AI era requires a transition from executor to orchestrator, a role that utilizes the tools but derives its primary value from the ability to lead people through ambiguity. Ultimately, the future of work is not defined by distance, but by an institutional and human presence that remains deeply and stubbornly human.

Corning's agreement with Meta, valued at $6b, signals that the primary constraint on AI scaling is shifting from the availability of GPUs to the limitations of the network layer. Generative AI clusters require an intensity of fiber connectivity nearly ten times greater than that of traditional data centers. By securing long-term manufacturing capacity from Corning, Meta is treating connectivity as a strategic resource rather than a commodity. They now recognize that their primary risk is not the maturity of AI models, but the physical limits of the network. Navigating this “networking wall” has become the defining challenge for hyperscalers, as they are forced to commit billions to infrastructure while the ultimate scale of AI demand remains an unknown.

Corning’s ability to reach its twenty percent operating margin target ahead of schedule suggests that pricing power has migrated toward the connectivity layer. Corning’s updated projection, adding three billion dollars to its sales target through 2028, functions as a “canary in the coal mine” for the true scale of AI demand. Unlike cloud subscriptions, which can fluctuate wildly, these revenue figures are anchored by firm, multi-year purchase agreements with hyperscalers like Meta. This massive commitment of capital suggests that the industry’s largest players have moved past the experimental phase. They are now making a fixed commitment on the physical infrastructure required to sustain AI over the long term, treating fiber connectivity as a critical supply-chain bottleneck that must be secured years in advance.

If you want to understand the true trajectory of the AI economy, ignore the chatbots and watch Corning’s results.

For a CIO at a large research institution, this is the moment that sends a chill down the spine. Institutions facing this scenario have been forced to disconnect from the Internet for days or weeks to regain control. I knew the stakes. My first move was to activate our Mandiant retainer. Their team engaged within hours. Over the next two weeks, they validated that we had contained the intrusion. They mapped out the attacker’s path, showing how they slipped around our defenses. They also handed us a detailed set of remediations, the kind that would reduce or eliminate the chance of a similar attack succeeding again, requiring several university-wide IT policy changes.

Under our bylaws, decisions on IT policy reside in the CIO’s office. We reviewed the proposed changes with key constituency groups. We expected a debate but heard very little. And then, after the policies were enacted and rolled out, the pushback began. It arrived quickly and with force. My team felt blindsided by the intensity. Many in the community felt blindsided by the impact. The silence during our attempts at earlier consultation had not been agreement. It had been something else entirely.

In today’s Dispatch, I want to reflect on what we learned from that experience and outline a practical approach to shared governance that can help any CIO lead meaningful change with greater clarity, less surprise, and far less tension.

The big picture

A familiar pattern shows up whenever a CIO introduces a needed policy or process change. The reaction rarely centers on the substance. It centers on how the change was delivered. People say no one asked them. They say it arrived without warning. These comments point to something deeper. Shared governance is not a procedural step. It is part of how people understand their role and identity inside the institution. When IT treats a change as a process execution, the community interprets it as a shift in power, a threat to local practice, or a disruption to routines that carry meaning.

Most failures follow the same path. A small internal group shapes the policy, moving quickly to solve an urgent problem. By the time the broader community sees the proposal, it feels final. Leadership hears little during early consultation and assumes silence means agreement. It never does. The first exposure to a structural shift almost always triggers confusion or hesitation. That hesitation can become resistance. The conflict is emotional, not intellectual. Any CIO who wants to lead change without unnecessary turbulence must address that pattern directly and build a process that gives people time, clarity, and a sense of recognition before decisions are made.

A structured approach to governance

Shared governance works best when communities acknowledge the difference between decision rights and influence rights. Decision rights describe who ultimately owns the call. Influence rights describe who deserves to shape, question, and inform that call. Across a large campus, nearly everyone sits somewhere on one of those two paths. Most people do not resent a CIO’s decision rights. They resent processes that ignore their influence rights. When influence is bypassed, even necessary decisions feel imposed. When influence is honored, the same decision feels legitimate.

The institution benefits from a predictable rhythm that respects both kinds of rights. Significant change should move through a structure that mirrors the rituals of legislative bodies. Every major policy or process update deserves two readings. The first introduces the issue. Leadership explains the problem the institution is trying to solve and the direction of the proposed solution. The aim is not consensus. The aim is friction. This is the moment for stakeholders to surface concerns, question assumptions, and expose risks that may not be visible from the center.

The second reading comes after time has passed and feedback has been absorbed. Leadership returns with a revised version. They explain what changed and why. They walk through what remains and the rationale for keeping it. They ask again for unresolved concerns. By this point, the community has had time to think and shift from initial reaction toward genuine engagement. The process gives people the space to move from emotion to clarity, and from surprise to contribution.

Why the second reading matters

People engage more seriously on the second reading. The first reading hits the room cold. It triggers confusion or silence, and neither is helpful. Silence, in particular, creates risk. It leaves leadership with a false sense of alignment. The second reading signals something different. It shows that leadership is committed to transparency. It shows that feedback is sought and that the CIO is willing to pause, revise, and return. Once a second reading becomes part of the governance pattern, people cannot credibly claim later that they were left out of the process. The surprise disappears. The temperature drops. The conversation becomes constructive rather than defensive.

The bridge to empathy

This structure works best when paired with Chris Voss’ concept of the accusation audit. Before the first reading, the leadership team should list every fear or suspicion the audience might hold. They should name them honestly. People may fear a loss of control. They may believe the change will increase workloads without support. They may suspect the decision is already made. Naming these concerns out loud removes their emotional power. It gives the room a chance to relax. It shows stakeholders that leadership understands their perspective before asking for their input.

After the first reading, the team should conduct a second, shorter audit of new concerns that arise. When they return for the second reading, they can close the loop. They can say what they heard, how it shaped revisions, and why certain items remain unchanged, reinforcing trust. It makes engagement feel real rather than symbolic.

The final word

The gains of a predictable process are real. A structured approach helps leadership surface conflict, reduce defensiveness, and build trust through clear engagement and responsiveness. Stakeholders get space to think, ask questions, and shape outcomes. The institution gains a reliable way to modernize policy, strengthen compliance, and introduce new tools without triggering backlash. Disagreement still happens, but it becomes manageable. It becomes part of a constructive rhythm rather than a surprise.

CIOs cannot avoid difficult decisions or the pressures that come with modernization. They can avoid the cycle of unnecessary conflict that undermines credibility and burns relationships. The Two Readings Method, paired with a thoughtful accusation audit, creates a rhythm that fits the culture of higher education while still supporting decisive action. The goal is never manufactured consensus. The goal is clarity, trust, and a shared sense of responsibility for the institution’s long-term resilience.

This recent podcast validates this philosophy by contrasting Corning’s deep manufacturing ethos with the fragility of short-term capital efficiency. Weeks details the discipline required to maintain credibility during downturns, specifically noting the decision to absorb financial losses rather than sever ties with employees during the Covid19 pandemic. He argues that invention is trivial compared to the proprietary mastery of manufacturing processes, which serves as Corning’s true competitive moat. This focus on talent retention allowed Corning to navigate shifting geopolitical incentives, such as tariffs and industrial policy, simply by maintaining their domestic footprint when others moved manufacturing overseas. The interview illustrates that by resisting the urge to optimize for the immediate quarter, a leader can align the business with long-term technology cycles and secure a durability that endures.

Almost overnight, it stopped. By the 2000 presidential election, it was clear the cycle had turned, marking the first of four major disruptions to higher education that I would experience. My retirement account fell hard. When I tested the private sector again in 2002, the silence was striking. The moment taught a simple lesson. Easy money hides real risk. In today’s Dispatch, I want to revisit what that period revealed about the early Internet and how its most durable innovations were forged in scarcity. These lessons matter as the AI boom accelerates and the hype fills the air once again.

The big picture

The late 1990s felt like a season of limitless promise. Money poured into anything with a “.com” attached. Interest rates were low, venture capital firms were flush, and the cultural mood insisted that the Internet would rewrite the world overnight. It was a period defined less by discipline and more by dopamine-fueled hope. Leaders convinced themselves that transformation required only capital, cleverness, and speed.

Then the market collapsed.

What followed was not the end of the Internet. It was the beginning of the real one. The bust exposed a gap between what people imagined the Internet would become and what institutions were actually prepared to build. The failure was not technical. It was a failure of capital discipline and leadership judgment. Scarcity forced architectural honesty. When the money vanished, the only ideas that survived were the ones tied to genuine value, measurable efficiency, and clear constraints.

The Myth of Abundance and the Necessity of Frugality

Weber’s Ideal Types help explain the difference. The boom years reflected a charismatic moment. The novelty of the Internet carried its own authority. People trusted the vision more than the process. The bust shifted power back to rational-legal authority. Balance sheets replaced big speeches. Leaders had to make hard choices based on cost, risk, and operational reality. That tension governed institutional life until the AI boom reopened the cycle of hype and urgency.

Innovation funded by unlimited capital (typically debt) often rewards features over fundamentals. The bubble was full of ideas that were not sustainable. Scarcity changed that, requiring focus. Every dollar had to address a real constraint. Out of that pressure came the core architectural moves that shaped the modern Internet.

1. Operational efficiency became the differentiator. Companies like Amazon, eBay, and Google survived because they focused on process, scale, and internal discipline. They built logistics engines and data platforms, not buzz. The work was quiet and unglamorous, but it created the alignment needed to endure.

2. Business models returned to reality. Free content supported by vague network effects gave way to actual revenue. Google’s keyword advertising, eBay’s transaction fees, and early subscription models (Netflix, Amazon Prime) have proven that technology must enable sustainable exchange. Hope is not a plan.

3. Infrastructure is decentralized. The bubble left a glut of unused fiber that drove down Internet costs. But the deeper shift came from the move to lean, commodity computing. Institutions learned that owning large proprietary server farms was not a competitive advantage. This logic set the stage for cloud computing.

4. Software became a utility. Consumer startups burned cash on growth. In contrast, early SaaS companies like Salesforce built repeatable, mission-critical tools that became part of their clients’ operations. Utility beat consumer novelty.

5. Frugality became a core competency. PayPal and others survived because they were relentless about cost. They experimented cheaply and stayed flexible. They treated financial prudence as a leadership duty rather than an inconvenience.

Scarcity, in other words, did more than punish excess. It rewired the basics of how the Internet was built and run. Once the hype burned off, leaders had to confront what the network was actually good at, where it created real value, and how much organizational risk it could reasonably carry. Out of that sober phase came a second wave of change, the five transformations almost no one had predicted in 1999.

The Five Transformations No One Saw Coming

The bust did more than correct the excesses of the boom. It revealed the real shape of the Internet. Many of the breakthroughs that followed were not the ones investors predicted in 1999. They emerged because scarcity forced a more honest reading of what people actually needed and what institutions could sustainably provide.

• From e-tailers to peer marketplaces. The 90s assumed that businesses would dominate online retail. They built digital storefronts and tried to replace physical stores with web pages. The post-crash reality was different. The Internet turned out to be far better at connecting people to people than companies to consumers. eBay and Craigslist thrived because they built trust systems, not warehouses. They let ordinary people sell what they already owned. In a recession, that mattered. These platforms were asset-light and profitable because they offloaded logistics and inventory risk onto the users. Every home became a store.

• From big iron to commodity computing. The early vision required expensive, proprietary hardware. Real-world survival required the opposite. Google proved you could operate a global service using cheap commodity parts managed by open-source software. Linux replaced proprietary operating systems like Solaris. MySQL replaced commercial databases such as Oracle. Scale came from clustering many low-cost machines instead of buying one massive server. Cash-strapped startups had no choice. The result was a collapse in the cost of building an Internet company, opening the door to the next decade of innovation.

• From interactive TV to user-generated content. Investors imagined high-budget digital programming. What emerged instead was the Social Web. Wikipedia, Blogger, and YouTube took off because amateurs were willing to create content for free. Professional content was expensive. User-generated content cost nothing. The dark fiber overbuilt during the boom made hosting cheap enough to offer these platforms at no cost. Clay Shirky called it the cognitive surplus. Millions of people donated their time, and the platforms harvested the value.

• From micropayments to the ad auction. The 90s tried to build a world of digital cash. Users would pay a fraction of a cent to read an article or view an image. That future never arrived. Google solved the revenue problem by shifting the payer from the user to the advertiser. AdWords created the economic backbone of the Internet. During the downturn, companies needed revenue but could not charge people who expected things for free. The ad auction was the lifeline. It monetized behavior and set the stage for the surveillance economy.

• From telecommuting to the gig economy. Managers in the 90s predicted a rise in remote work for full-time employees. The recession produced a different outcome. Companies cut staff to reduce fixed costs, then turned to online marketplaces like Elance and oDesk to hire specialists by the hour. Work became modular. Tasks, not roles, became the unit of labor. Early gig platforms predated Uber by a decade and started to reshape the relationship between organizations and workers. Employment began shifting from relational to transactional. This was not the product of visionary strategy. It was the product of budget pressure.

These shifts did not come from abundance. They emerged as institutions were forced to abandon their assumptions and adapt to real constraints. Scarcity revealed what the Internet was good at, where the real revenue lived, and what had only ever been hype.

The final word

The real Internet was built in the years after the crash. It was built by people who learned that technical cleverness fails without financial discipline. That insight matters now. We are living through another period of technological ambition. AI carries many of the same promises. It also carries the same risks of hype and debt.

The lesson for organizations is to slow down and interrogate the gap between vision and capacity. Does the investment advance the mission? Does it deliver measurable efficiency? Or does it simply offer prestige? Integrity requires objective thinking about value, grounded in reality, not hype and early adopters’ enthusiasm.

Technology leadership is not built on chasing every new wave. It is built on credibility. It is built on the restraints learned through failure. It comes from promoting people who know how to communicate, align, and deliver. It comes from hiring for core competencies, not the flavor of the year. The strongest architectures are not built in abundance. They are built when resources are tight, and choices are clear. That is the work of stewardship. It is how institutions stay resilient over time.

Given my ERP background, Jack asked me for help. I tasked a student to diagram the process end-to-end, and the resulting diagram revealed 32 swimlanes of handoffs. Each handoff often resulted in defects, causing significant rework loops. The analysis revealed that activity time accounted for a tiny fraction of the overall queue time. Jack took action to eliminate handoffs, eliminate steps, and delegate authority to speed the process, with some success. In today’s Dispatch, I discuss what I believe is a near-universal truth: that most customer service problems are rooted in poor process flow, lack of structural discipline, and excessive queue time, not constrained capacity.

The big picture

The most fundamental threat to customer trust for any service unit is not poor quality work, it is the wait. Leaders across student affairs, facilities, business services, and IT must reckon with the same destructive outcome: a request is submitted, it languishes untouched for days in a queue, and when the task is finally completed, even if the actual activity took just two hours, the customer perceives a four-day failure. The distinction between activity time (when work is being done) and queue time (when work is sitting idle) is irrelevant; the delay entirely shapes perception.

This slow flow kills customer satisfaction. When work sits, it goes stale and quickly becomes frustrating, leading to dissatisfaction and possible escalation. This systemic friction is rarely due to incompetence or a lack of capacity; it is a direct result of organizational structure. In higher education, complex governance and a culture that normalizes caution have created processes where aging queues are accepted as the cost of doing business. Most institutions envision premium, high-quality service experiences, but their underlying internal processes are built on outdated, clunky methods. The work itself is sound, but the process does not flow, and that silence, those black holes of momentum, destroy credibility. The queue time is the killer.

Why Queue Time Destroys Institutional Trust

Waiting creates anxiety; it feels like a loss of control. If someone waits long enough, they may escalate the situation. Escalation feels like enforced accountability, but it is usually a sign that the underlying process has already failed. Once escalations become routine, politics takes over. Priorities shift based on noise, not business strategy.

This leads to a pernicious cycle:

• Misdiagnosis: Leaders attribute staff burnout and high queue times to a shortage of resources, focusing on hiring rather than fixing the system.

• The Constraint: Typically not labor, but an unpredictable flow from political queue jumping and the abandonment of first-in, first-out (FIFO) discipline.

• Blocked Capacity: A ten-person team may look overwhelmed. But if five of those ten people are blocked by unnecessary handoffs, unclear ownership, or persistent defects, the system is not short on capacity; it is short on clarity and discipline.

This structural lack of clarity creates an environment prone to friction, where the process is optimized for risk avoidance rather than efficient resolution. Without a single, clearly defined owner for the end-to-end process, every handoff creates the opportunity for defects. Staff default to caution: they wait for more data or seek unnecessary approvals. This creates a “no” oriented system that provides multiple safe opportunities for employees to pause, deflect responsibility, or simply queue the work. The system is designed for bureaucratic coverage, not for speed or customer value.

The Hidden Costs of Poor Flow

The main culprits creating these stagnant queues are organizational: excessive handoffs, non-value-adding activity, and the corrosive effect of queue jumping.

• The Proliferation of Handoffs: Every handoff requires another person to pick up, understand, and act, often within their own existing queue, resulting in another delay for the customer. As work traverses unit and cultural boundaries, defects often result. The drive for “thoroughness” can result in adding extra approvals, leading to more rework and slower service. The problem is rooted in fragmented ownership. As processes are divided across departments, no single unit owns the flow from beginning to end. The customer sees the entire journey; the units only see their lane. In that critical gap, quality degrades and institutional trust decays.

• Non-Value Work Creeps In: Universities are burdened by legacy processes inherited from a world of paper. When these old habits migrate to digital systems, they create significant friction. Many steps and approvals remain simply because their necessity was forgotten; removing them now feels risky. Leaders often respond to service breakdowns by adding more checks and forms, which increases the feeling of oversight but rarely improves outcomes. These additions increase handoffs and multiply the likelihood that work sits, untouched and aging, because the next person must act on a non-value-adding task.

• The Corruption of Queue Jumping: Queue jumping will create responsiveness for the politically powerful few at the expense of predictability for everyone else. When a system is full of exceptions driven by politics, everyone loses control of their workload. The queue loses integrity because first-in, first-out principles are abandoned. Once that happens, work priorities become unstable, leading to widespread demoralization among staff trying to run a solid, fair process.

The Discipline of Fixing Flow: Lean Six Sigma and DMAIC

Fixing this problem requires a sustained, analytical focus, often best approached using a framework like DMAIC: Define, Measure, Analyze, Improve, Control to eliminate wastes (lean the process) and reduce defects (applying six sigma principles).

• Define and Measure: Start by collecting data on customer satisfaction and measuring the queue. Service leaders must shift their dashboard metrics away from simple request counts and close rates to focus on:

  • Queue Aging: The average time a request sits before work begins.

  • Work in Progress (WIP): The total number of tasks actively in the system. Lower WIP usually correlates with faster cycle time and fewer defects.

  • Cycle Time: The total time from request to completion.

The data will be uncomfortable at first. It will show that most delays are structural, not capacity-related. They will show that teams need fewer tasks in flight and fewer handoffs, not more people. Once you can visualize the end-to-end queue, you can fix it.

• Analyze and Improve: Use this data to isolate defects, non-value-adding handoffs, and unnecessary activity. The goal is to enforce three core disciplines:

  • Simplify to Eliminate: Map processes from start to finish. Remove every step that can not change the outcome. Many approvals exist only to confirm something known. Removing them shortens cycle time and reduces rework.

  • Reduce Handoffs: Enforce a single, clear owner for each end-to-end process. This owner must eliminate handoffs to avoid kicking work around, and they must be empowered to engage stakeholders to facilitate process completion or to consider process improvement opportunities, driving simplification.

  • Protect the Queue: Get the political support to guard the first-in, first-out principle. Where necessary, the cycle times for activities should be equalized to prevent a single bottleneck from crippling the entire flow.

The solution is not to eliminate high-touch service, but to provide it. To handle the politics, create a small, highly skilled unit whose job is to handle one-off requests, political heat, and messy problems. This preserves the flow of the main operation and turns politics into data, revealing where the system is truly breaking down.

The final word

When executive leaders respond to long queue delays by immediately throwing more capacity at the problem, they are choosing the path of least resistance. They are validating less mature management approaches that have allowed disorganized flow, excessive defects, and destructive queue jumping to become the norm. Worse, they are actively demoralizing the more mature managers who run solid, data-driven business processes but must now compete for capacity with their less mature peers.

A strategic service leader must require the hard work of work simplification rather than rewarding inertia with capacity. Customers perceive exceptional value when it is tied to speed and predictability; slow, opaque service delivery drives dissatisfaction regardless of the final work product. Service credibility depends on flow. Service that moves builds confidence. Service that waits erodes trust, even if the work is excellent.

Queue time is the killer, flow is the cure.

In today’s Dispatch, I name the warning signs that this kind of catastrophe is building and describe how ERP leaders can create a culture that raises friction early. Silence in the design phase is not alignment. It is the first signal that the project is losing its ability to see downstream consequences. When people nod through decisions rather than scrutinize them, the cascade begins. And once it begins, it is very hard to stop.

The big picture

The first year of an ERP implementation sets the tone. Once consultants arrive and design workshops begin, the work shifts from planning to institutional redesign, where every early conversation shapes how payroll, budgeting, and approvals will function for years. Yet these sessions are often treated lightly, with people nodding through process maps and assuming the implications are small. Consultants interpret silence as alignment. Leaders mistake the absence of conflict for progress. Green dashboards reflect politeness rather than clarity. Silence in these moments is the first warning sign that the institution does not understand the consequences of its choices.

ERP implementations rarely fail because the software breaks. They fail when early agreement is mistaken for real understanding and “simple” design choices trigger consequences no one anticipated. A harmless workflow tweak in finance becomes a payroll crisis under load. A security toggle disrupts reporting. An approval path becomes a political flashpoint when units lose control of their work. In these first months, every decision begins a chain reaction. Leaders who recognize that decisions travel and accumulate friction give their project a chance. Those who do not invite the quiet failures that later surface as missed go-lives, blown budgets, and eroded trust.

Why Silence is the Most Dangerous Signal

Silence takes hold early in ERP projects because people want to appear cooperative, informed, and efficient. Too many staff defer to hierarchy, while consultants prioritize schedule over debate. Stakeholders assume others understand the details better, so they speak less and meetings feel smooth. But quiet rooms do not signal clarity. They signal that stakeholders cannot yet see second and third-order impacts, so they default to politeness rather than inquiry. That is when risk begins to accumulate.

The result is cosmetic alignment. Dashboards remain green. Meetings feel efficient, but this creates an illusion of progress. Risk accumulates in these quiet moments. When the absence of questions is mistaken for understanding, structural fragility sets in. The project fails not because of noise, but because no one exposed the weaknesses while they were being built. Silence is the most critical performance indicator

The Cascade of Consequences

ERP failures rarely erupt from a single decision. They result from dozens or even hundreds of small choices made without scrutinizing their downstream effects. The Cascade of Consequences framework breaks this into five levels, revealing how early design decisions can transform into institutional friction that threatens go-lives.

• First-order effects: The easy yes. Someone requests a new approval path or data field. The software supports it, looks logical, and feels safe. People assume the configuration is wise because the system allows it. Solving a local problem creates a sense of progress, and the decision is saved. The meeting ends, but no one considers the downstream impacts on timing, dependencies, or workflow load.

• Second-order effects: Local friction. The impacts of a decision manifest locally. A task takes longer, a team loses flexibility, and someone complains about extra clicks. Leaders dismiss these as change management issues, but local friction signals that the decision created unnecessary load. Most institutions misinterpret this friction as a temporary annoyance, but it could be inefficiency in the design.

• Third-order effects: The breach into other departments. Consequences become real when changes cause another team to miss a deadline, an integration fails, or someone not present during the original decision now pays the price. Trust erodes, and people blame the software, but the problem lies in the design session decision, and the institution faces its unattended consequences.

• Fourth-order effects: Latent conditions. These hidden problems emerge under stress. Testing conditions are controlled, with small loads, precise timing, and clean data. However, month-end, payroll, or student hiring spikes can cause harmless rules to multiply task queues tenfold. Integrations that worked at low volume time out under real conditions. Staff invent workarounds that bypass the system and destroy data integrity. The system becomes structurally unsound, and no one saw it coming because earlier phases prioritized speed over deep analysis.

• Fifth-order effects: Cultural and institutional breakpoints. When misaligned decisions accumulate, the institution changes its behavior. Burnout rises. Leaders lose political capital. Units retreat into shadow systems to avoid the ERP. The system becomes technically live but culturally weak. The institution absorbs the cost through human capital, manual work, and declining trust. At this point, repairing the system costs more than doing the design work correctly at the start.

What Leaders and Teams Miss during Design

Many leaders underestimate how much the early design phase determines the entire ERP project. They assume risks will surface in testing or that consultants will catch mistakes. But the real danger is that people naturally see only first-order impacts. Decisions that seem logical in the moment often create new handoffs, extra steps, and hidden rework loops that ripple. Every early choice carries institutional consequences.

Collaboration often compounds this risk. Institutions equate representation with engagement, but having people in the room does not mean they are scrutinizing the decisions being made. The project team absorbs ambiguity to keep the project moving, and consultants avoid slowing the schedule. This creates polite alignment rather than real clarity. When discovery is rushed, and early decisions aren’t scrutinized, small inefficiencies accumulate into structural failures. True teamwork requires healthy friction, not quiet acquiescence, because without it, the design becomes a set of isolated preferences rather than a coherent institutional blueprint.

Why Silence Emerges and How Leaders Must Break It

Silence takes hold when business leaders fail to guide day-to-day design. If the business steps back and the project feels like an IT effort, the people closest to the work stop asking questions because they assume someone else owns the decisions. Hierarchy reinforces this pattern. Teams stay quiet when executives avoid bad news, when objections feel risky, or when challenging a design might be misread as resistance. Without psychological safety, silence becomes the project’s default culture. Early misalignment goes unspoken until testing, when it is too late to easily correct.

Breaking that silence is a leadership responsibility. It requires shifting from accepting first-order explanations to interrogating downstream impacts. Leaders must ask how each decision affects timing, data, integrations, and load, and require teams to map second and third-order scenarios before proceeding. They should simulate stress early, long before UAT, and encourage people to surface uncertainty wherever possible. When rooms are quiet, leaders must treat that as a signal to dig deeper.

How to Build a Culture That Surfaces Downstream Impacts

ERP projects succeed when institutions create a culture where deeper analysis is expected, dissent is normal, and early conflict is treated as a sign of health. The first months of an implementation are when the institution decides whether it will surface risks early or pay for them later. This requires visible executive engagement, clear decision rights, and psychological safety so people feel free to question assumptions.

Executives must be present in the work, ask the questions others avoid, and insist on clarity. Project teams and consultants must translate business requests into downstream maps and stop treating design sessions like it’s as software training. The goal is not speed but integrity. Strategies that reinforce this culture include:

• Interrogate decisions early. Ask how each choice affects timing, data, workload, and integrations. Require second and third-order analysis before approval.

• Normalize dissent. Reward people who raise concerns. Use structured methods, such as two-round readings and reviews, to ensure thoughtful engagement.

• Simulate stress. Test processes against real peak conditions long before UAT to expose latent weaknesses. Consider the impact of defects and rework under load.

• Make tradeoffs visible. Document choices and the name of the individual(s) requiring them, what they cost, and what the institution is deferring.

• Ensure business owners lead. Stakeholders must steer the design and own its consequences. When key decisions are vetted, they must lead those discussions.

When these habits take hold, design becomes intentional rather than reactive. Risks surface early, and downstream impacts are understood before they become costly.

The final word

ERP systems do not fail loudly; they fail quietly. They fail because of polite meetings, shallow approvals, and decisions made without understanding their reach. They fail because silence is mistaken for alignment or agreement. If institutions want ERP success, they must treat silence as a warning. The Cascade of Consequences shows how those quiet moments transform into operational breakdowns and cultural retreat. ERP project leaders must build cultures that reward scrutiny. They must lead with friction rather than speed. ERP design is the work of understanding consequences before they cascade. Speak early. Question often. Build clarity. Silence is the killer.