As I stated in my piece last February, there aren’t a lot of sure-fire methods to slow down the development of AI. But there are some potential levers that could help. The most commonly discussed are legal and regulatory approaches.

I have co-authored several articles over the last year or two that together suggest organizations are not likely to get business value with genAI if their primary focus is improving individual productivity. But everybody doesn’t read Harvard Business Review, and I have never put all the reasons for this in one place. So here goes—a laundry list of explanations for why you’re probably not going to achieve measurable productivity gains from genAI.

Much of the effort and attention around AI for the last several years has been around technical developments. New model announced! New benchmark surpassed! New contract for massive data centers! New world-class technologists hired! You know the drill.

I am happy to say, however, that things are beginning to change in this regard. AI companies are beginning to realize something that many corporate executives knew intuitively. What matters isn’t the technology—OK, that’s important too—but the ability of organizations to deploy it effectively and get value from it.

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.

But generative AI is clearly changing the process of data analysis. I’ve been experimenting with quantitative data analysis using ChatGPT for several years now. I was certainly impressed by the LLM’s ability to generate Python code to analyze structured data and create machine learning models.

I’m reading Jill Lepore’s book If/Then about the origins of analyzing human behavior data with computers. One interesting aspect of it is the automation paranoia arising from the introduction of the IBM 704 mainframe computer in 1954 (the year I was born). The book even includes an image from an automation-focused campaign leaflet for John F. Kennedy’s 1960 presidential campaign—see it above.

This is the season of holiday overeating and over-drinking—despite the fact that moderate consumption of food and alcohol is widely believed to lead to a better life. Although I sometimes agree with Oscar Wilde in advocating “moderation in all things—including moderation,” I am beginning to think that AI—particularly the generative variety—is no different than food, alcohol, or other good things that become problematic when used excessively.

The most extreme version of strategic AI is found in a small but growing number of companies might be described as “all in on AI” or “AI first.” These companies are aggressively pursuing strategic returns on their AI investments. They are using the technology to enable new strategies, new business models, and dramatically new ways of performing their business processes. While they represent a low percentage of companies, they are providing trailblazing examples for the majority of companies that are more conservative.

I am most concerned about this issue in healthcare—perhaps obviously, because de-skilled clinicians could endanger human health. The other general reason why healthcare professionals might let AI de-skill themselves is that they are under considerable pressure to be more productive—at least in the U.S. The average patient visit with a physician is only about 15 minutes, and there are temptations galore to take AI-enabled shortcuts. If AI can easily summarize patient visits in clinical notes, diagnose diseases and communicate with patients about them, and help navigate highly bureaucratic insurance processes, why shouldn’t doctors and nurses take advantage of this capability?

Yes, there is an AI bubble. AI-related stock prices are inflated, valuations of startups are inflated, there is too much marketing hype, data center construction is excessive, etc. All of these related bubblets will deflate, and probably soon.