The Science

The Science

Nothing ventured, nothing changed

Why adoption often stalls without anyone choosing to hold back

Dr Ryan JessonCognitive scientist5 min read

A single path forking in two directions through open ground.

Almost every real decision includes the option of carrying on as we are, and that option gets chosen more often than its merits alone would explain.

Think of something you have been meaning to switch to and haven’t. A different bank, a different phone plan, a different way of running your Tuesday mornings. You know the alternative is probably better. Now notice how quickly the reasons for not having done it arrive: the setup, the forms, the fiddling, the chance it turns out worse. Those reasons are not excuses. They are real costs, and they arrived faster and clearer than the benefits did. And now the trickier part: see if you can bring to mind something you have been doing for a long time and have not considered changing. It is difficult even to generate an answer, let alone to ask whether it is optimal, and that difficulty lives in the non-choice-ness of the experience, the feeling of merely carrying on with what already is rather than choosing it. That feeling has a name, status quo bias, and while this piece of cognitive machinery has large effects on our lives, we mostly remain oblivious to its workings precisely because of that non-choice-ness.

What this means is that a failure to switch gears often cannot be explained by ignorance of the benefits. Many people already understand there are benefits to using AI tools at work. In a survey of 767 knowledge workers, 68% believed AI tools would improve their job performance, while only 37% used them regularly (Berman et al., 2025).

Of course, some of that gap can be explained by real reasoned positions, for instance, when workers believe their organisation lacks the infrastructure to support meaningful adoption (e.g., Zhu & Gao, 2026). However, reasoning rarely explains the largest chunk of inertia. What accounts for much of it is stranger and harder to see, because it rests on how choices are shaped rather than on what people believe.

When a choice is framed as the default, it is far more likely to be chosen (Samuelson & Zeckhauser, 1988). For instance, when two American states offered drivers the same two car insurance options but made different ones the default, the majority in each state kept whichever one they had been given (Kahneman et al., 1991). This reluctance to switch is a powerful finding within the psychology literature, and its presence has been demonstrated across a wide range of domains, from things as trivial as swapping a pen (Kahneman et al., 1991) or a coin (Gal & Rucker, 2018) to potentially high-impact decisions like choosing jobs or where to invest money (Samuelson & Zeckhauser, 1988; Xiao et al., 2021).

Why the pull? One long-standing explanation is loss aversion: what we give up weighs more than an equivalent gain, so the losses of moving loom larger than the benefits of arriving (Kahneman et al., 1991). Consider what is actually at stake in a working week. In economics the currency is money. At work it is time, and the costs decompose into three: the hours to learn the new thing, uncertainty about whether it will work, and the value of skills in the current way of working that switching appears to write off. Across an enterprise system rollout, these switching costs were the central determinant of resistance, and they mediated nearly everything else, including how capable people felt of adapting to the new system and whether they believed their colleagues were in favour of the change (Kim & Kankanhalli, 2009). The third cost is the quiet one. Someone who has spent four years becoming fast at a task is not being irrational when a tool that makes them slow for a fortnight feels like a loss. It is a loss. Whether it remains one over a longer horizon is another question entirely.

The trickiest part of this whole cognitive system is its quiet voice. A default option barely registers as a conscious choice, if it registers at all, and the heaviness of losses relative to potential gains is likewise an automatic instinct that often evades conscious detection. This means that long familiarity with the current way of working changes how easy and how advantageous the new option appears, before any weighing happens (Polites & Karahanna, 2012). The scales are adjusted before anything is placed on them.

In practice

What this means for AI capability training

If people already believe the tool would help and still aren’t using it, then more persuasion is the wrong instrument. The evidence points at what mediates the decision, and it isn’t belief in the benefits.

The first move is to make the switch cheap rather than to argue that it is worthwhile. Every hour of learning removed, every uncertainty resolved by letting someone try the thing on a real task of their own, lowers the cost side of a calculation people are already making sensibly.

The second is to make use visible. Where habit and inertia run strong, what colleagues do carries more weight than information does, and people who know someone using these tools are markedly more likely to use them. Managerial endorsement moves reported usage from 34% to 79% (Berman et al., 2025). That is a structural lever, not a motivational one.

The third is to be accurate about what is actually at risk, because the usual framing overstates it. A financial analyst who has spent years learning to produce complex reports holds two different things: the judgment about what belongs in a report, what the numbers mean, and where the argument is weak; and a particular method of getting that judgment onto the page. Working with AI changes the method. It does not touch the judgment, and the judgment is what makes the report good. This is the difference between automation, handing a task over, and augmentation, keeping the task and changing how it is performed, and the same technology gets used in both ways by different people in the same organisation (Zhu & Gao, 2026). What is genuinely at stake for the analyst is a fortnight of being slower, not the expertise itself. Naming that honestly, rather than either dismissing the loss or letting people overestimate it, is working with the machinery rather than against it.

The sources

Read it for yourself.

The papers this piece draws on, and what each one shows.

  1. Most knowledge workers believe AI tools would improve their performance; far fewer use them regularly, and managerial endorsement is the strongest predictor of use.

    Berman, K., Daly, M., Frishberg, E., Loureiro, J. C., & Ott, J. (2025). The AI workplace: New research on employee adoption. Irrational Labs.

  2. Framing an option as the current arrangement makes it more likely to be chosen, across job, investment and budget decisions.

    Samuelson, W., & Zeckhauser, R. (1988). Status quo bias in decision making. Journal of Risk and Uncertainty, 1(1), 7–59.

  3. Losses weigh more than equivalent gains, demonstrated with objects as trivial as a pen and in real insurance defaults.

    Kahneman, D., Knetsch, J. L., & Thaler, R. H. (1991). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206.

  4. People decline to swap even objects that are functionally identical.

    Gal, D., & Rucker, D. D. (2018). The loss of loss aversion: Will it loom larger than its gain? Journal of Consumer Psychology, 28(3), 497–516.

  5. The effect replicates in pre-registered tests across job and investment decisions.

    Xiao, Q., Lam, C. S., Piara, M., & Feldman, G. (2021). Revisiting status quo bias: Replication of Samuelson and Zeckhauser (1988). Meta-Psychology, 5, MP.2020.2470.

  6. Switching costs are the central determinant of resistance to a new system at work, and mediate most other influences on it.

    Kim, H.-W., & Kankanhalli, A. (2009). Investigating user resistance to information systems implementation: A status quo bias perspective. MIS Quarterly, 33(3), 567–582.

  7. Long familiarity with the current system lowers how easy and advantageous a new one appears, before it is properly evaluated.

    Polites, G. L., & Karahanna, E. (2012). Shackled to the status quo: The inhibiting effects of incumbent system habit, switching costs, and inertia on new system acceptance. MIS Quarterly, 36(1), 21–42.

  8. Some caution about generative AI is a considered judgment about organisational readiness, and the same technology is used to automate by some people and to augment by others.

    Zhu, L., & Gao, T. (2026). Hybrid images of generative AI: A Q methodological study of civil servants’ perceptions. Government Information Quarterly, 43(1), 102113.

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