The Missing Piece in GenAI’s Economic Impact
AI won’t transform the economy unless we simplify work—eliminating inefficiency, not just automating it.
I built my early career on work simplification, process optimization, and Internet-enabled automation. From ePayments for tuition and fees to web-based course registration, my team led these efforts for many years at Texas A&M University, eliminating inefficiencies and enabling self-service processes. When the mobile revolution hit, I had moved onto CIO leadership, focused on ERP implementations and transforming IT into a trusted business partner. Mobile technology improved access, but unlike the Internet, it didn’t challenge the way work was done—adopters simply layered it onto existing processes without rethinking them.
Now, with Generative AI, I’m focused on developing the next generation of IT leaders—leaders who can harness AI to drive the productivity growth that can reshape higher education. AI has extraordinary potential, but if we automate business processes instead of simplifying them, we’ll miss the real economic transformation. In this Dispatch, I explore the true lessons of the 1993, 2007, and 2022 technological revolutions—and what needs to happen for GenAI to deliver real productivity gains.
Why It Matters
Every major technology shift—from the Internet in 1993 to the iPhone in 2007 and now Generative AI in 2022—has promised transformation. But why did some revolutions fundamentally reshape the economy while others fell short? The answer isn’t just new technology—it’s how we simplify work. Without radical work simplification, AI will be just another tool layered onto “business as usual” rather than a force for true economic transformation.
1993: The Internet’s True Power Was in Process Simplification
The web didn’t just make information available—it removed unnecessary intermediaries and let businesses and individuals interact directly.
Universities, banks, and businesses automated processes, replacing manual approvals, phone transactions, and paper workflows with self-service portals.
A productivity boom led to decreased inflation, interest rates, and the federal deficit. The US real GDP growth rate was about 3.9% during this period.
2007: The iPhone Expanded Connectivity but Didn’t Deliver a Productivity Revolution
The iPhone revolutionized connectivity by providing direct access to people and processes anytime, anywhere. This ensured constant reach for services, transactions, and communications, but it didn’t simplify underlying processes.
Businesses and governments layered mobile access onto existing processes, digitizing them instead of eliminating unnecessary steps.
New markets and technologies emerged, but the structural productivity gains of the 1990s weren’t repeated because we didn’t fundamentally transform work.
2022 & Beyond: GenAI’s Impact Will Depend on Work Simplification
AI automates tasks, but without process reengineering, we risk automating complexity instead of eliminating waste.
AI-driven efficiency will only transform the economy if organizations radically simplify work, not just add GenAI to existing business models.
Six Principles of Work Simplification for the GenAI Era
For Gen AI to drive true economic transformation, organizations can’t just focus on process optimization and automation—they must embrace work simplification at every level. The six principles of the Toyota Production System are critical to work simplification for GenAI productivity growth and economic transformation.
Question Every Requirement—Even the Ones That “Make Sense.”
Why it matters: Over time, rules and processes accumulate without anyone questioning their necessity. This leads to unnecessary complexity, redundant approvals, and wasted effort.
What to do: Every process step should be challenged with:
Why does this step exist?
Does it add measurable value to the outcome?
What happens if we remove it?
Example: Toyota’s Just-in-Time (JIT) Manufacturing: Toyota revolutionized production by eliminating excess inventory at assembly plants. Overproduction led to waste, storage space, and increased costs. By adopting a pull-based system, Toyota cut costs, improved production speed, and maintained quality.
Delete first, optimize later—if you never add steps back, you haven’t cut enough.
Why it matters: Most organizations are too cautious when simplifying—they remove a step here or there but never aggressively challenge every part of a process.
What to do:
If a step’s removal doesn’t cause problems, it was never necessary.
Eliminate first, validate later—test whether missing steps actually impact results.
If you never have to reintroduce steps, you haven’t cut deep enough.
Example: Toyota’s Kaizen Method in Manufacturing: Toyota’s Kaizen Method in manufacturing involves physically removing production steps and analyzing their impact on the final product. This approach reduces welding points on vehicle frames without compromising safety and saves millions in production costs.
If no one owns a process step, delete it—accountability drives efficiency.
Why it matters: Many inefficient processes exist because no single person is responsible for them. If no one owns a process step, it becomes hard to remove because “it’s always been there.”
What to do:
Assign clear ownership to every process step. If no one can justify why it exists, it should be removed.
Beware of “departmental ownership”—processes should have an accountable person, not just “Finance” or “HR.”
Example: Toyota’s Andon Cord System: On Toyota’s production line, workers own their specific station and can pull an Andon cord to stop the line if they notice an issue. This forces accountability and ensures that every step in the process adds value.
Conduct a Step-by-Step Process Teardown.
Why it matters: Many processes seem necessary because they’re ingrained in how work has always been done. The best way to challenge this is to break the process down step-by-step and test eliminations.
What to do:
Map out every step of a process.
Challenge each step’s value. Does it contribute to the final outcome?
Test process variations without certain steps to see if performance is impacted.
Example: Toyota’s Production Process Redesign: Toyota redesigned its production process by questioning why certain materials were coated in manufacturing. Removing the coating and testing durability revealed no impact on product longevity, resulting in significant cost savings.
Assume a Step is Unnecessary Until Proven Otherwise.
Why it matters: The natural tendency is to keep steps “just in case.” The Lean mindset requires assuming every step is unnecessary unless proven otherwise.
What to do:
Start with the assumption that a step is waste.
If no objective evidence proves its necessity, remove it.
Example: Toyota’s Supplier Relations Approach: Toyota’s Supplier Relations Approach: Engineers ignore non-critical supplier guidelines by default and test necessity first. This approach saves millions by removing unnecessary parts and features.
Validate Eliminations with Small-Scale Experiments.
Why it matters: Many organizations are afraid to remove steps because they don’t want to disrupt operations. But small-scale testing ensures safe process elimination.
What to do:
Run limited trials where a step is removed and measure the results.
If performance stays the same or improves, remove it permanently.
Example: Lean Supply Chain Management: Toyota removed intermediate quality inspections in certain production lines for a short trial period. After confirming no decline in vehicle quality, they permanently eliminated the extra checks, cutting production time significantly.
The Bottom Line
Work simplification is a necessary condition for GenAI economic transformation.
AI will not deliver a productivity revolution if we simply automate business as usual and avoid the hard work of process simplification.
The real opportunity is in simplifying work across industries—especially in government, health care, and adjacent industries.
For AI to drive true economic transformation, organizations must focus on work simplification, not just process optimization and automation.
The 1993 Internet revolution unlocked efficiency by eliminating middlemen and streamlining workflows. The 2022 AI revolution must do the same—or risk becoming just another layer of complexity.
Epilogue: The Fundamentals of Technological Innovation
The inspiration for this Substack started with the above video. Elon Musk, because of his work in the Trump Administration, has become a polarizing and controversial figure. Regardless of one’s views on his politics, his advocacy for work simplification over blind optimization reflects a truth we’ve seen before:
The Internet (1993) transformed the economy by directly connecting individuals, eliminating bureaucracy, and enabling direct self-service.
The iPhone (2007) made those capabilities mobile, but didn’t fundamentally rethink how work gets done.
Now, with Generative AI (2022), we face another major shift—but AI alone won’t drive economic transformation unless we pair it with radical work simplification.
The principles presented in this article aren’t novel; they’ve driven the Toyota Production System and the success of other industries that have adopted them. If we aim to harness GenAI for genuine productivity gains, the message is unequivocal: genuine economic transformation commences with work simplification.