Much easier to "AI wash”

There is increasing consensus that to get value from AI, you have to redesign your business processes and embed AI within them. A McKinsey 2025 survey, for example, found that redesigning workflows is the factor most highly correlated with getting value from AI. A 2026 paper from MIT researchers argues that AI benefits will come from supporting “chains” of business activities, i.e. processes. My friend Erik Brynolfsson, head of Stanford’s Digital Economy Lab, has long argued that in a “J curve” situation, productivity with AI initially lags as companies re-engineer processes, but then increases significantly once AI is fully integrated into new, redesigned workflows. The distinguished user interface designer Jacob Nielsen wrote recently that: “In a controlled field experiment, startups that redesigned end-to-end workflows around AI generated 90% more revenue than equally equipped peers that used AI mainly to speed up individual tasks.”

None of these assertions or survey results is terribly surprising. AI is undoubtedly a powerful tool that can enable new ways of doing work. If it’s just piled on top of existing work activities, its potential will be limited. Processes are intimately connected with the information systems that support them, and a recent Gallup survey found that whether AI is integrated with the processes and technologies used to accomplish work is a key factor in whether workers will adopt it. In short, it’s a no-brainer to use AI to redesign processes, as I have previously argued with co-authors in this article and this one.

So Why Isn’t It Happening?

If in response to the question of, “If this is such a good idea, why isn’t everybody doing it,?” you answered, “Because it’s hard,” you’d be right. There are a variety of obstacles to widespread adoption of AI-driven reengineering. To start, most companies (particularly in the US) aren’t process-oriented. They don’t have clarity on what their processes are, who is responsible for them, how they are currently performing, etc. There isn’t even consensus on what to call them. Note that some of the authors and experts I mention above use the term “workflows” or “chains of activities.” Some companies don’t like the “process” term because it sounds somewhat bureaucratic, but it is both the most historically respected and accurate term in my view.

Of course, processes can be small (as in Lean Six Sigma, and AI can help with those too) or large, end-to-end and cross-functional (as in business process reengineering). I’m a fan of the latter because of their potential for large-scale improvement, and I think that broad end-to-end processes like order management and procure-to-pay provide the greatest benefit when combined with AI. But working on any size processes is better than nothing.

Process design also requires some effort. You need to know where the process begins and ends, what technology currently supports it, and how it’s structured and performing at the moment. Lars Reinkemeyer and I argued in this article that process mining can help companies to understand the performance of existing processes and bottlenecks in their performance, and in general to make companies more process-oriented.

Implementation Is Really Hard

But implementing a new process design is much more difficult and time-consuming than designing it in the first place. That requires not only new process flows but also the new systems, skills, behaviors, etc. that accompany them. And getting people to actually do their work in new ways is never easy. For a major cross-functional process the implementation effort can stretch into many months and many millions of dollars or other currencies.

Perhaps you’re beginning to see why this doesn’t happen more often. Process reengineering for end-to-end processes is not for the faint-hearted or poverty-stricken. It can also fail for a variety of reasons—from lack of stakeholder support to politics to some human or technical component of the process not functioning correctly.

In pre-AI business process reengineering in the 90s and 00s—I was present at the creation, and wrote the first article and book on the subject (though neither were the best-selling versions)—there were several companies I was familiar with that claimed to do reengineering, but were really just laying off people (as described in this 1995 article). It’s not surprising that companies are doing that now with AI. It’s much easier to “AI wash” than to redesign and reimplement new ways of doing work.

Dr. Tom Davenport is a world-renowned thought leader and author, is the President’s Distinguished Professor of Information Technology and Management at Babson College, a Fellow of the MIT Center for Digital Business, and an independent senior advisor to Deloitte's Chief Data and Analytics Officer Program.

An author and co-author of 25 books and more than 300 articles, Tom helps organizations to transform their management practices in digital business domains such as artificial intelligence, analytics, information and knowledge management, process management, and enterprise systems.

He's been named:
- A "Top Ten Voice in Tech" on LinkedIn in 2018
- The #1 voice on LinkedIn among the "Top Ten Voices in Education 2016"
- One of the top 50 business school professors in the world in 2012 by Fortune magazine
- One of the 100 most influential people in the technology industry in 2007 by Ziff-Davis
- The third most important business/technology analyst in the world in 2005 by Optimize magazine
- One of the top 25 consultants in the world in 2003

Why It’s Hard to Redesign Work Processes with AI

Much easier to "AI wash”