Emily Lewis-Pinnell, Driving AI Adoption at Evaila.
One of the most important signals in enterprise AI right now is not just that the technology is improving. It is that more experienced users are getting better results from it.
That matters because it challenges one of the most persistent assumptions in the market: that the path to value is simply handing more work over to AI. The latest research points in a different direction. Anthropic’s March 2026 Economic Index found that high-tenure users were more likely to take on higher-value tasks and more likely to get successful results. OpenAI’s “The State of Enterprise AI” 2025 report shows a similar pattern inside organizations: The workers reporting the greatest time savings were not just using AI more often but using more tools, applying it across a wider range of tasks and working with it in more advanced ways.
That suggests the real divide is not between organizations using AI and those that are not. It is between organizations helping people become better collaborators with AI and organizations still treating AI as a shortcut.
It also reinforces a point I made in a prior article on broad enablement versus deep transformation. The strongest AI strategies do not choose between the two. Broad enablement builds fluency and surfaces opportunities across the business. Deep transformation redesigns workflows so AI can create measurable value. The organizations seeing the strongest results are doing both.
The Anthropic and OpenAI findings sharpen that argument. Access matters because people need practice to move beyond novelty. But transformation is where more advanced AI habits get translated into business results. Organizations do not get better outcomes from AI simply because people are using it more. They get better outcomes when people learn how to use it well on work that matters.
So, what helps?
Build For Judgment, Not Just Usage
Recent Wharton research from Steven Shaw and Gideon Nave offers an important warning. Their work on “cognitive surrender” shows that people often adopt AI outputs with too little scrutiny. When the AI was accurate, performance improved. When it was wrong, performance dropped, while confidence still rose. That is a risky combination in any enterprise setting. It means AI can improve work, but it can also make weak thinking feel more certain.
This is one reason experienced users outperform novices. They are less likely to treat AI as an answer machine and more likely to use it as a thinking partner. They iterate. They challenge. They compare. They bring harder problems to the interaction. That is where leaders should focus their enablement efforts—not just on access but on the behaviors that make collaboration productive.
Create The Conditions For Knowledge To Flow
Advanced augmentation depends on human context: edge cases, institutional memory, customer nuance and hard-won pattern recognition. But workers will not consistently share that expertise if they believe the system is capturing it in ways that weaken their position. Research from Harvard and MIT shows that when workers understand that workplace data can be used to train AI systems that replicate their expertise, they become more likely to withhold documentation and resist monitoring.
That matters because augmentation gets stronger as more context enters the system. If people start protecting what they know, the organization may still see activity, but the quality of the collaboration begins to thin out. The AI has less of the expertise that makes it useful, and leaders have less visibility into why progress is stalling. The issue is not simply culture. It is whether the organization can keep the knowledge flywheel turning.
Measure Augmentation On The Right Terms
This is where many organizations still stall. Automation gets more attention because it is easier to understand and easier to model. Its value often shows up neatly in labor savings or cycle-time reduction. Augmentation is harder. Its returns show up in better judgment, faster iteration, broader problem-solving and the ability to take on work that previously sat outside a team’s capabilities.
That makes augmentation easier to underinvest in. If leaders only look for the clean math of substitution, they will miss the larger opportunity: capability expansion. OpenAI’s finding that many workers are using AI to do tasks they could not do before is important for exactly this reason. Part of the return is not just efficiency. It is new capacity.
But that value does not reveal itself automatically. Organizations have to decide where stronger human-AI collaboration should change the outcome, not just speed up the task. They have to define what better looks like in advance, and they have to track whether time saved is being converted into higher-quality decisions, better service, faster learning or more ambitious work. Otherwise, augmentation remains conceptually appealing but economically hard to defend.
The next gap in AI will not be between companies that bought licenses and companies that did not. It will be between companies whose people learned how to think with AI and companies whose people learned only how to hand work to it.
Two or three years from now, that difference will be hard to hide. One group will be moving faster because it has built stronger judgment, richer knowledge flows and new organizational capacity. The other will still be counting activity and wondering why the returns feel smaller than expected.
Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
