Nir’s Note: This guest post is by Gleb Tsipursky, PhD, author of The Psychology of AI Adoption at Work: From Resistance to Results.
Imagine two colleagues trying the same AI assistant for the first time. Both ask it to summarize a complicated document. Both receive an answer containing a useful insight and an obvious mistake. One thinks, “This thing is unreliable,” closes the window, and returns to working as before. The other thinks, “I need to learn which parts it handles well,” changes the instructions, checks the result, and tries again. Same tool. Same task. Different future.
Research with knowledge workers shows why learning to succeed with AI requires more than access. In a field experiment with 758 consultants, AI improved performance substantially on tasks within its capabilities, yet reduced accuracy on a task beyond that uneven boundary. The researchers called this the “jagged technological frontier.”
Your beliefs determine how you approach that frontier. They shape what you attempt, what you notice, how you interpret mistakes, and whether you learn from experience. Some beliefs in particular almost guarantee you will never realize the potential of AI. I outline five below.
Belief 1: “I’m Not an AI Person”
“I’m not an AI person” sounds like a description. In practice, it functions as an instruction that tells you to avoid experimenting, interpret confusion as proof of inability, and let other people shape how the technology changes your work.
Being inexperienced or uncomfortable with AI doesn’t mean you are “not an AI person.” In fact, lack of skill is a good indicator that you are the kind of person that will get the most out of AI. One workplace study of 5,172 customer-service agents found that AI adoption produced the largest gains among less experienced and lower-skilled workers. The technology helped them learn practices associated with stronger performers. Instead of leaning into your lack of AI expertise, lean into iterative improvement. The more useful belief is: “I can learn one valuable AI workflow at a time.” You do not need to master every model, feature, or prompting technique all at once. Choose one recurring task, such as preparing a meeting agenda, comparing options, or turning rough notes into a first draft. Test the tool, examine the result, and adjust your instructions. A small study found that even brief instruction in prompt engineering boosted participants’ AI confidence, knowledge, and results. It is entirely possible to improve not only at using AI, but also improve your self-conception around AI. Practice improves outcomes, and better outcomes bolster your belief in yourself as someone who can master the technology,
Belief 2: “A Smart Tool Should Get It Right the First Time”
People rarely abandon a colleague after one mediocre draft. Yet many people test an AI tool once, spot an error, and decide the entire technology has failed. Researchers call this pattern algorithm aversion: the tendency to reject algorithmic help too quickly after observing imperfection. That response reflects a poor understanding of AI. Generative AI can produce sophisticated work one moment and stumble over an apparently simple detail the next. The Harvard and BCG experiment mentioned in the introduction showed there is no clean line between “easy” and “hard” tasks. Just because a model makes occasional dumb errors, doesn’t mean it can’t be incredibly useful in other situations.
Instead of thinking you need to make a binary choice between trusting or distrusting AI, the better belief is: “AI can be valuable without being uniformly reliable.”
This belief supports calibrated AI trust. In a preregistered experiment, researchers found that allowing people to adjust algorithmic predictions reduced their aversion to using them. Treat AI output as material to inspect, improve, and sometimes reject. Ask it to explain assumptions. Request alternatives. Check consequential facts against reliable sources. Keep responsibility for the final decision. This approach avoids blind faith without turning healthy skepticism into knee-jerk dismissal.
Belief 3: “Using AI Makes Me Look Less Capable”
Some professionals hesitate to use AI because they fear what colleagues will think. They worry that asking a machine for help signals weak judgment, shallow expertise, or an inability to do the work themselves. Research on reputational concerns around algorithms shows why people may override useful recommendations when following them could make them look less capable. A field experiment involving 450 remote workers found that visible reliance on recommendations increased workplace AI resistance, even when following the recommendations could improve performance. Workers feared that using AI would make them appear less confident and able.
Rather than fearing how others will perceive your AI usage, tell yourself: “My ability to direct and evaluate AI is a sign of my expertise.” Using a calculator does not prove you cannot do arithmetic. Using an editor does not mean you cannot write.
The relevant question is who contributes the context, standards, accountability, and final judgment. Well-designed human-AI collaboration improves output without erasing human contribution. In a large marketing experiment, human-AI teams produced more work per person and higher-quality advertising copy, while human-human teams still produced stronger images. Combine strengths instead of pretending either side performs every task best.
Belief 4: “More AI Use Must Be Better”
Resistance creates one failure mode. Enthusiasm creates another. Once people see AI saves time, they may begin inserting it into every task. They stop asking whether the tool improves outcomes and start assuming that more usage means more progress. A public-sector field experiment illustrates the need for AI judgment: AI users improved quality and completion time on a document-understanding task, but their quality fell on a data-analysis task. The same technology produced opposite results depending on the specific type of work.
Instead of a ‘more is more’ approach, try telling yourself: “I should use AI where it improves the result.” Before using AI, define the outcome you want. After using it, compare the result with your usual process. Did it save time after verification? Did it improve quality? Did it expose an overlooked option? So-called ‘tokenmaxxing’ tells you very little. Outcomes are what matter..
Belief 5: “AI Will Decide My Future for Me”
Fear about AI often begins with genuine risk and ends with a surrender of agency. People often quickly move from thinking “AI may change my role” to “Nothing I do will affect what happens.” That leap encourages passivity precisely when experimentation, learning, and participation matter most. A study covering more than 36,000 workers across 35 European countries found that AI adoption depended on more than occupational exposure. Individual skills, training, non-routine work, and employee influence over organizational decisions helped explain who adopted the technology.
The more useful belief is: “I can influence how AI changes my work, even when I cannot control the entire transition.” That belief does not require cheerful predictions about every job and every aspect of the AI revolution. Instead, it directs attention toward the choices still available to individuals: learning a tool, redesigning a workflow, establishing safeguards, documenting results, strengthening human skills, and helping leaders distinguish productive applications from wasteful ones. Agency starts by separating what you can influence from what you can’t. According to recent workplace research, the more deeply you use AI, the more likely you are to be able to shape your job, develop your skills, and retain some autonomy at work.
Turn Your AI Beliefs Into Testable Hypotheses
It is easy to say you need to transform your beliefs around AI. How do you actually do it? In his best-selling book Beyond Belief, Nir Eyal lays out his belief transformation approach. It starts from a simple idea: hidden assumptions shape what we perceive, feel, and do. Applying that insight to AI does not mean replacing anxiety with uncritical optimism. It means examining those hidden assumptions and choosing beliefs that remain plausible, encourage useful action, and can be tested against evidence.
The first step is catching yourself when you tell yourself things like: “I’m bad at this.” “It should already know what I mean.” “Using it is cheating.” “It will replace me anyway.” Then identify the behavior that sentence produces. Do you avoid the tool, trust it too quickly, conceal your use, or apply it without checking the outcome? Next, write a replacement belief that creates room for action: “I can learn one workflow.” “The first answer is a draft.” “My judgment adds the value.” “I can help shape how my team uses this technology.”
Finally, run a small experiment. Apply the new belief to one low-risk task. Record the time, quality, errors, and lessons. A belief becomes durable when you collect evidence through behavior, not when you just repeat a slogan.
The central challenge in workplace AI adoption involves more than teaching people which prompts to type in. People also need beliefs that help them experiment without becoming reckless, remain skeptical without becoming dismissive, and preserve agency without denying uncertainty. You do not need to believe AI will transform everything. You need beliefs strong enough to make the next useful experiment possible.
I’ve spent over three decades bringing more than 160 products to market. Every one of them started with something I didn’t know.
The difference was never intelligence. It was the belief that I could figure it out.
AI has amplified that belief. It doesn’t replace experience. It rewards curiosity. Every regulation I have to understand, every patent I need to draft, every market I need to research starts with a question. AI gives me a faster path to answers, but I still have to ask better questions, challenge the responses, and make the final decisions.
The people who will benefit the most from AI won’t necessarily be the best programmers. They’ll be the ones who believe they can learn, adapt, and keep moving when they don’t have all the answers.
I’ve always told inventors there will never be a perfect sample, a perfect launch, or a perfect time. The same is true with AI. Stop waiting to master it before you use it. Use it, learn from it, and get a little better every day.
Belief starts the journey. Action is what turns that belief into reality.
Really helpful, thank you. I particularly like the ‘jagged frontier’ concept and the suggestions for beliefs to try on for size like ‘AI can be valuable without being uniformly reliable’.