Foundational Change Requires Foundational Buy-In
Created on 2026-06-02 20:05
Published on 2026-06-02 20:31
Lessons learned to effectively implement AI into the workplace.
Across decades of research on institutional change, the factor that separates the efforts that take from the ones that fail is not the quality of the plan handed down from the top. It is whether the people inside the institution own the change. In 1997, Richard Pascale, Mark Millemann, and Linda Gioja made the point plainly in the Harvard Business Review: plans concocted at the top and rolled out to the organization tend to fail, while change that engages the people doing the work is what succeeds.[^1] Any institution, of any size, absorbing any real change, runs into the same law. Without buy-in from the people inside, the change does not hold.
Buy-in is not a communications problem. It is what produces alignment. When the people inside genuinely take a change on as their own, the levels of the organization start pulling in the same direction. When they do not, you can draw the perfect plan and the institution still tears apart at the seams. Buy-in is the cause. Alignment is the result. This is the foundation that everything else stands on, and it is the one AI is about to run straight into.
We have seen this movie before. In 1990 Michael Hammer told companies to stop automating their broken processes and redesign them from scratch: “Don't automate, obliterate.”[^2] The insight was right. Through the 1980s, companies had poured capital into computers and bolted them onto the way they already worked, and the productivity gains never showed up. The economist Robert Solow caught the era in a line: you could see the computer age everywhere except in the productivity statistics.[^3] Hammer's point was that the value was never in the machine. It was in redesigning the work around it.
Then reengineering became a catastrophe. By Hammer and Champy's own estimate, between half and seventy percent of efforts failed to achieve what they set out to do.[^4] The reason is the one that matters here. Reengineering arrived as a top-down restructuring, imposed from above, and its real-world meaning collapsed into one word: layoffs.[^5] It became closely associated with the corporate downsizing of the 1990s.[^6] It violated the foundation. It demanded that people tear down and rebuild their own jobs while only the top captured the gain, so the people on the ground correctly read it as a threat and resisted it. The idea was right and the direction was wrong. You cannot redesign how work happens by firing the people who know how it happens. This all sounds very familiar.
AI now sits exactly where enterprise computing sat around 1990. The capability is powerful and largely purchased, and the value is not showing up. A 2025 MIT Project NANDA study found that ninety-five percent of organizations studied were getting zero return from their generative AI initiatives, while only five percent of integrated AI pilots were extracting meaningful value. Its authors were explicit that the divide was not driven by model quality or regulation, but by approach: most systems do not retain feedback, adapt to context, or improve over time, and enterprise-grade tools fail when they do not fit existing workflows.[^7] The productivity paradox, replaying beat for beat. The machine is not the value. Redesigning the work around it is. And that redesign will not take unless the people inside own it.
Here is what makes AI different from the reengineering wave, and why it has to be financed differently. Done right, AI is not an upgrade to the existing company. It is a re-founding. You are not bolting a tool onto the old organization; you are rebuilding how the work happens from the ground up. A change that is fundamentally is closer to starting a new company than to improving the current one. And if it is effectively a new company, it has to be owned like one.
Think about the startup model at its best. The people who build the company early share in the value they are creating. When the company wins, they win in the same currency. That is why people build differently when they are participating in the outcome rather than simply executing someone else's plan. The incentive runs all the way to the floor. This is not just intuition. The research on “shared capitalism,” where workers participate economically in the firm through ownership, profit sharing, or broad-based stock options, finds better workplace performance, with the strongest effects on loyalty, effort, and retention when economic participation is paired with involvement and job security.[^8] Reengineering failed in part because it demanded startup-scale transformation on legacy-corporation incentives: tear it all down, and only the top profits. The fix is to match the ownership to the magnitude of the change. Startup-scale change needs startup-scale ownership.
That means aligning all three levels of the institution to the same outcome, each through what they actually care about. The top, the CEO and the board, cares about stockholder value, and a failed AI rollout destroys it while a successful one creates it, so the top is aligned the moment the change reliably produces returns instead of write-offs. Middle management bears disproportionate implementation risk, squeezed from both sides and rarely rewarded for making transformation succeed, which is why its incentive has to be pulled into line directly: bonus middle managers on the value the change creates, and the layer that carries the most risk becomes financially motivated to drive it. And the floor, the people doing the daily work, has to share in the upside too, the way a startup's early employees do, so that the worker also profits when the change succeeds. Align all three to the same measure and buy-in stops being something you beg for. It becomes the natural result of everyone owning the outcome.
The objection is obvious: an existing public company cannot hand real shares to every worker without dilution, tax, and governance problems. True, and it does not have to. The executable instrument is value-tied cash, phantom equity that tracks the share price, or bonus pools keyed to the value the change produces, paid in cash against a measure rather than carved out of the cap table. The worker still wins when the value rises, the incentive still points the same direction as the chairman's, and none of the ownership mechanics break. That is how you push startup-style alignment down to the floor inside a real company today.
One piece makes the whole structure executable rather than theoretical. For the bonuses to be real, you have to be able to measure the value the change actually created, and that is precisely what a persistent foundation underneath AI tools make possible. A layer that holds the organization's real work and context over time can trace what the change produced and what it saved, so the reward is tied to documented value rather than a guess. The foundation that makes the change work is also the thing that makes the reward measurable, and the measurable reward is what produces the buy-in. The pieces are the same piece.
That is the difference between the reengineering that failed and the change that could work. The first demanded a re-founding and kept the old ownership, so the people fought it. The second recognizes that a change this fundamental is a re-founding, and finances it like one, with everyone from the chairman to the floor sharing in the outcome. Build the foundation, push the ownership down, and the change finally has what reengineering never gave it: people on the inside who win when it wins.
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## Notes
[^1]: Richard Pascale, Mark Millemann, and Linda Gioja, “Changing the Way We Change,” Harvard Business Review 75, no. 6 (November–December 1997): 126–139. The authors argue that engaging employees in solving the organization's challenges, rather than rolling out plans concocted at the top, is essential to leading successful change.
[^2]: Michael Hammer, “Reengineering Work: Don't Automate, Obliterate,” Harvard Business Review, July–August 1990. Expanded in Michael Hammer and James Champy, Reengineering the Corporation: A Manifesto for Business Revolution (New York: HarperBusiness, 1993).
[^3]: Robert M. Solow, “We'd Better Watch Out,” The New York Times Book Review, July 12, 1987, 36. Solow's observation that the computer age could be seen everywhere except in the productivity statistics became known as the productivity paradox.
[^4]: Michael Hammer and James Champy, Reengineering the Corporation: A Manifesto for Business Revolution (New York: HarperBusiness, 1993), 200. Hammer and Champy estimated that 50 to 70 percent of reengineering efforts failed to achieve their intended results. The figure was later described as an estimate rather than a scientific measurement. See Michael Hammer and Steven A. Stanton, The Reengineering Revolution: A Handbook (New York: HarperBusiness, 1995).
[^5]: Thomas H. Davenport, “The Fad That Forgot People,” Fast Company, issue 1, November 1995, 70–74. Davenport, an early architect of business process redesign, wrote that reengineering had not begun as a program of layoffs, but had come to stand for restructuring, layoffs, and failed change programs.
[^6]: Davenport, “The Fad That Forgot People.” He reported that the 1994 CSC Index State of Reengineering Report found that 73 percent of surveyed companies were using reengineering to eliminate jobs, with an average planned reduction of 21 percent.
[^7]: Aditya Challapally, Chris Pease, Ramesh Raskar, and Pradyumna Chari, The GenAI Divide: State of AI in Business 2025 (MIT Project NANDA, July 2025). Based on a review of more than 300 publicly disclosed AI initiatives, interviews with representatives from 52 organizations, and survey responses from 153 senior leaders, the report found that 95 percent of organizations were getting zero return from generative AI investment, while only 5 percent of integrated AI pilots were extracting meaningful value. The authors identify lack of contextual learning, workflow fit, and improvement over time as central causes of the divide.
[^8]: Joseph R. Blasi, Richard B. Freeman, Chris Mackin, and Douglas L. Kruse, “Creating a Bigger Pie? The Effects of Employee Ownership, Profit Sharing, and Stock Options on Workplace Performance,” NBER Working Paper No. 14230 (August 2008), subsequently published in Douglas L. Kruse, Richard B. Freeman, and Joseph R. Blasi, eds., Shared Capitalism at Work: Employee Ownership, Profit and Gain Sharing, and Broad-Based Stock Options (Chicago: University of Chicago Press, 2010), 139–165. The authors find beneficial effects on workplace outcomes other than absenteeism, with the strongest effects on turnover, loyalty, and worker effort when shared capitalism is combined with employee involvement, training, job security, limited supervision, and market-level or better fixed wages.
