The process of integrating AI into how an organization works, decides, and collaborates. AI adoption is not a technology project. It is a change in how people think and exercise judgment. Organizations that treat it as a rollout rather than a transition gain efficiency while quietly losing capability.
Glossary
A working vocabulary
The way we talk about AI shapes how we use it. These terms come from Suon’s work with teams navigating AI adoption. They name the patterns that matter most: where judgment holds, where it slips, and what organizations can do about it.
A pattern of reliance on AI that gradually degrades human judgment and independent problem-solving. AI dependency rarely looks like a crisis. It looks like convenience. People stop checking outputs. They stop forming their own views before consulting the model. Over time, the skills they once brought to the work atrophy, not because they chose to abandon them, but because nothing in their workflow required using them.
The ability to use AI effectively while maintaining and developing independent judgment. AI mastery is not about becoming a power user. It is about knowing when the tool serves you and when it substitutes for you. Mastery means your thinking gets sharper with AI, not thinner. It requires deliberate practice, self-awareness, and a willingness to do things the slow way when the slow way is the right way.
Deliberate practice in sustaining focus against the fragmentation that AI workflows create. AI tools constantly interrupt the thinking process: prompting, evaluating, refining, re-prompting. Each interruption is small. Across a workday, they erode the deep concentration that complex judgment requires. Attention training draws on contemplative traditions, from Buddhist meditation to monastic stabilitas, that have cultivated sustained focus for centuries. Applied to AI work, it means staying with a problem long enough for your own thinking to emerge before reaching for the model.
The brain’s evolved tendency to minimize mental effort, which AI amplifies by making intellectual shortcuts feel costless. Humans instinctively gravitate toward the easiest path, especially when promised similar outcomes. AI supercharges this. When a model can produce a passable draft in seconds, the effort required to think independently feels irrational. The result is not laziness. It is biology meeting infrastructure. Recognizing the cognitive miser in yourself is what makes deliberate effort possible.
Delegating thinking tasks to AI in ways that erode the thinker’s own capability over time. Not all delegation is offloading. Using AI to handle routine formatting is sensible. Using AI to form your argument, draft your strategy, or evaluate your options is a different matter. The distinction is whether you are saving time on tasks you have already mastered or skipping the thinking that builds mastery in the first place.
The growing mismatch between the power of AI tools and the human development required to use them wisely. AI alignment is the technical problem. The cultivation gap is the human one. Organizations invest heavily in AI infrastructure while investing almost nothing in the judgment, self-awareness, and collaborative capacity of the people using it. The result: increasingly powerful tools operated by people who are not growing at the same rate. Closing the cultivation gap means treating human development as seriously as model development.
The gradual expansion of tasks handed to AI, from the routine to the consequential, without a conscious decision. Delegation creep starts sensibly. You let AI handle email drafts, then meeting summaries, then first passes on strategy documents. Each step feels like a small efficiency gain. But the cumulative effect is a shift in who is actually doing the thinking. By the time you notice, the boundary between “AI assists me” and “AI works while I supervise” has moved further than you intended.
A challenge that feels inefficient in the moment but builds lasting capability. Cognitive psychologists Robert and Elizabeth Bjork showed that making learning harder in the short term leads to stronger retention and transfer. AI inverts this principle. It removes friction from nearly every knowledge task, which feels productive but quietly undermines the struggle that builds expertise. In AI work, desirable difficulty means deliberately choosing the harder path when the task is one that develops your judgment: drafting before prompting, evaluating before accepting, thinking before delegating.
The gap between what you know you want and your ability to articulate it to an AI system. Working with AI forces a new kind of self-knowledge. You must make explicit what has always been implicit: your standards, your reasoning, your sense of what “good” looks like. Many people discover, often uncomfortably, that they cannot clearly express goals they have pursued for years. The gulf of envisioning is not a prompting problem. It is a thinking problem that AI makes visible for the first time.
An approach to AI adoption that puts human capability, judgment, and wellbeing ahead of speed or output. Humanising AI does not mean making AI more human. It means making AI adoption more humane. It asks: what happens to the people using these tools? Are they growing or shrinking? Are they making better decisions or just faster ones? Organizations that humanise their AI adoption protect the capacities that no model can replace.
The ability to think about your own thinking, and a defining skill in AI work. Using AI effectively requires you to articulate goals you may never have stated, analyze processes you usually run on instinct, and evaluate results against standards you have never made explicit. People with strong metacognitive skills get better AI outputs because they can specify what they actually want. More importantly, they notice when AI is subtly reshaping their thinking. Without metacognition, you cannot tell the difference between AI enhancing your judgment and AI replacing it.
A cycle of increasingly specific AI prompts that produces diminishing returns while draining time and attention. The prompting spiral begins when an AI output is close but not quite right. You refine the prompt. The next output is different but still not right. You refine again. Each iteration feels like progress because the output keeps changing. But the underlying problem is rarely the prompt. It is that you have not clarified what you want independently of the model. The spiral breaks when you stop prompting and start thinking.
The compulsive pursuit of the perfect AI output, driven by the illusion that one more refinement will finally produce it. Named after the Greek figure condemned to reach for fruit that always recedes. AI systems generate slightly different responses each time, so it feels like the ideal result is always one prompt away. The Tantalus effect explains the peculiar exhaustion that follows long AI sessions: you have been working hard, but not on your own thinking. You have been chasing a moving target set by the model.
The practice of knowing when to rely on AI output and when to override it. Trust calibration is a skill, not a setting. It requires understanding what the model is good at, where it tends to fail, and how your own biases interact with its outputs. People who overtrust AI accept errors they would have caught. People who undertrust it waste effort redoing reliable work. Calibration sits in the middle: a learned, context-sensitive judgment call.
The organizational pattern where AI decisions are made in rooms that lack the knowledge to make them well. Boardrooms and IT departments choose tools, set policies, and define workflows. The practitioners who understand the actual work, and where AI could genuinely help or harm it, sit three floors down and are rarely consulted. The wrong room problem explains why many AI investments underperform: the people with decision-making authority and the people with domain expertise are not in the same conversation. The fix is changing who is in the room, not what technology is on the table.