At least not measurable ones...

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.

You need to redesign end-to-end processes around AI capabilities if you’re going to get substantial value from AI. And that, as I wrote about a couple of months ago, is hard. Even if you do it successfully it’s going to take years.
We’re not training people well. I saw a good summary of this argument in Robert Scoble’s newsletter last week. To get productivity from genAI demands that individuals incorporate AI into their daily work. But if you only get generic education—unrelated to your industry or your function—that’s unlikely. AI education should be highly segmented, with each business and job class getting their own version if it.
If you use genAI in the “right way,” you don’t save a lot of time and effort. The right way, at least if you want to create high-quality content and avoid rotting your brain, is to employ multiple prompts until you get something close to what you want, and then to review it for possible hallucinations. Then edit it to take out the cliché phrases and to add your unique perspective. When I insist that my students do this with their AI-assisted essays, they often tell me it doesn’t save them any time over writing it all themselves.
Measuring the aggregate impact of individual productivity gains is really hard. You have to measure how long it took you or someone else to perform a task pre-AI. Then you have to measure post-AI. Then you have to do that for multiple tasks and determine how all that rolls up to entire jobs and organizations. Not surprisingly, few organizations do this type of measurement.
It’s unclear what people are doing with the saved time. Let’s assume that you do the needed measurement and figure out that the average employee in a particular job saves an hour a day. What are those people doing with that saved hour? Maybe they’re doing more work, or maybe they are streaming a World Cup game. Theoretically you could lay off 1/8 of the people who do the job, but that’s hardly ever done. Most organizations aren’t confident enough about the measurements to take that step.
Token costs are going up, which makes getting productivity improvements harder. Now that tokens are being measured and billed by vendors more assiduously, to claim any real productivity gains you need to compare the savings from any labor time reductions to the cost of the tokens needed to perform that task. Again, it’s a measurement hassle that hardly anyone undertakes.
To really get productivity requires that we explore all the capabilities of genAI to transform our jobs. But most people don’t invest a lot of time in personal infrastructure development. I read a lot about individuals who create agents to do much of their work. But I don’t know any such people and I strongly suspect there aren’t too many of them. It takes a substantial investment in time, tokens (see above), and learning to automate significant aspects of your life. Most of us would rather watch the World Cup, Desperate Housewives, or YouTube videos in our spare time.
Organizations don’t do controlled experiments. To really figure out whether, for example, genAI improves the productivity (and quality) of blog post creation, you should do an experiment. You might assemble a group of bloggers who use a lot of genAI help to write their posts, a group that just uses it for brainstorming, and a control group that doesn’t use it at all. You then measure their average time to produce outputs and have some independent group score the output quality. Again, a few university researchers have done this sort of experiment, but they are rare in real organizations.
Poor genAI outputs lower productivity. This is the “workslop” problem that many observers have begun to notice and write about. Your own productivity may not suffer when you use AI to create workslop, but other people down the line may need to improve your output, and their productivity will suffer. Matthias Hollweg and I recently described the “process slop” problem, in which interorganizational use of AI leads not only to productivity loss, but reduced trust in the integrity and value of the entire process.
AI agents will help with the individual productivity problem, but probably won’t solve it. Teams of agents are typically for enterprise use cases more than individual ones. Even then, somebody is going to have to monitor the quality of agents’ work, and that will detract from any productivity gains. It’s true that some aggressive individual users are already adopting agents, but their efforts will run into some of the same issues involved in enhancing productivity that I’ve described above.

What does this mean for our AI-driven economy? It probably indicates that AI providers are over-valued by the market, that GDP growth that’s largely driven by data center construction is unhealthy, and that generative AI is not enough to power the economy on its own.

These factors may also mean that we are unlikely to see substantial productivity gains from AI as an overall economy. Thus far we certainly haven’t. Over the last seven years—half of which period includes the genAI era—average annual nonfarm productivity in the US (as measured by the Bureau of Labor Statistics) has increased by 2.1%. That is exactly the amount that productivity increased between 1947 and 2026. In the first quarter of 2026 productivity increased by only 0.3%. If genAI is increasing our productivity, it’s apparently not easy to measure at the overall economy level. Perhaps it’s just too early for us to see AI-driven productivity gains, but how long do we need to wait?

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

Ten Reasons Why We Won’t See Productivity Improvements from GenAI

At least not measurable ones...