{"id":990629,"date":"2026-07-31T11:52:03","date_gmt":"2026-07-31T11:52:03","guid":{"rendered":"https:\/\/practaice.nl\/?p=990629"},"modified":"2026-07-31T11:56:04","modified_gmt":"2026-07-31T11:56:04","slug":"ai-adoption-why-the-technology-has-long-been-here-but-the-behavior-has-not","status":"publish","type":"post","link":"https:\/\/practaice.nl\/en\/ai-adoption-why-the-technology-has-long-been-here-but-the-behavior-has-not\/","title":{"rendered":"AI Adoption: Why the Technology Has Long Been Here, But the Behavior Has Not"},"content":{"rendered":"

Virtually every organization now has access to AI. Licenses have been purchased, tools rolled out, pilots launched. And yet, the results fall short of the promise: a handful of enthusiasts use the tools daily, while the rest rarely open them. This piece describes why AI adoption is fundamentally not a technology question but one of behavioral change, what skills employees need for it, and what organizations can learn from approaches that actually work in practice.<\/i><\/p>\n

The tools are there, but the organization is searching<\/h2>\n

The figures on the investment side are unambiguous. The report Superagency in the Workplace by McKinsey (2025)<\/a> shows that 92 percent of companies plan to invest more in AI over the coming years, while only 1 percent of leaders call their own organization mature in its usage. Even more striking is where the bottleneck lies according to that same study: not with the employees, who are further along than their executives think, but with leadership. Executives estimate that a small group of employees uses AI intensively, whereas the actual proportion is more than three times higher.<\/p>\n

What I see in practice aligns with this. Organizations are flooded with AI solutions, and only then do the real questions begin. Is this allowed, and on what legal basis? Where is our data going? Who owns the risks? And above all: how do we get people to actually change their way of working? The technology has long been here. Trust and behavior are not always there yet.<\/p>\n

Perhaps you recognize this: the licenses are arranged, the kick-off has taken place, and three months later everyone is working just as they did before.<\/p>\n

What is AI adoption?<\/h2>\n

AI adoption is the process in which employees and teams genuinely and permanently integrate AI into their daily way of working. It is different from AI implementation: implementation is about access to tools; adoption is about different work behavior. An organization in which everyone has a license but no one adapts their working method has implemented, not adopted.<\/p>\n

That distinction explains why so many AI initiatives stall after the pilot. Implementation is a project with an end date. Adoption is behavioral change, and behavior does not change through a rollout, but through repetition: trying something new, experiencing that it works, doing it again, until it becomes habit. Those who treat adoption as an IT project measure progress in rolled-out tools. Those who treat it as behavioral change measure progress in what people do differently.<\/p>\n

New work demands new skills, and they aren’t technical<\/h2>\n

The skills that this adoption requires are only limitedly technical. Formulating a prompt can be learned by almost anyone in an afternoon. The Future of Jobs Report by the World Economic Forum (2025)<\/a> shows which skills truly matter alongside technological literacy: critical and analytical thinking, curiosity and lifelong learning, resilience and flexibility. Translated into daily work: evaluating output instead of blindly accepting it, checking sources, recognizing bias, knowing when AI is precisely *not* the answer, and continuing to engage one’s own professional judgment. Those are soft skills<\/a>, not button knowledge.<\/p>\n

Since February 2025, this is no longer just an optional ambition either. Article 4 of the EU AI Act<\/a> obliges organizations deploying AI to ensure a sufficient level of AI literacy among their employees, appropriate to their role and the context in which the systems are used. The question is therefore no longer *whether* employees should be developed in this area, but *how*: as a one-off e-learning course that fades after a week, or as practiced behavior that lasts.<\/p>\n

What works in practice: five ingredients<\/h2>\n

A recognizable pattern emerges from the organizations where adoption does take off. Five ingredients consistently return.<\/p>\n

First: governance in order, prior to the experiment. Clarity regarding privacy, data flows, and frameworks is not a brake but a prerequisite; it creates the trust to be allowed to experiment. Second: AI champions on the work floor, people who experiment themselves and bring colleagues along, up close and credibly. Third: a small learning rhythm instead of a big bang, for example learning one application every week and using it immediately. Fourth: AI in the real work context, not in a separate course environment; people adopt what makes their own work easier today. And fifth: room for job crafting. From the research by Wrzesniewski and Dutton (Academy of Management Review, 2001)<\/a>, we know that employees who actively redesign their own work experience greater ownership and meaning. Allowing people to reshape their work with AI, instead of imposing a new way of working, makes the change their own.<\/p>\n

The common thread through all five: they are behavioral interventions. None of these ingredients are about the technology itself.<\/p>\n

Practice as the engine of AI adoption<\/h2>\n

If AI adoption is behavioral change, the law of all behavioral change applies: knowing is not doing. A presentation on responsible AI usage changes work behavior just as little as a poster about feedback changes a culture. What changes behavior is practicing in one’s own work context, with immediate feedback, repeated over time.<\/p>\n

That practice has two sides here. The first is the new work behavior itself: critically questioning AI output, testing advice from an AI system before building on it, explaining to a client or colleague how a decision was made. The second side is often forgotten: the conversations that support the change. The team leader who faces resistance from an employee who feels their profession is threatened. The AI champion who brings a skeptical colleague along without trying to persuade by pushing. The professional who explains to a concerned client what happens to their data. It is precisely those conversations that determine whether adoption shifts or stalls, and it is precisely those conversations that almost no one practices.<\/p>\n

With AI Avatar training<\/a>, that becomes possible: every conversation, scenario, and learning objective can be practiced in the employee’s own work context\u2014from the resistance conversation to the critical assessment of AI advice\u2014with immediate feedback on concrete behavior. This makes it visible, per competency and over time, whether the organization is truly moving forward; that is the difference between training and long-term retention<\/a>. PractAIce is built on this exact idea, and is itself an example of AI that accelerates behavioral change rather than just taking over tasks.<\/p>\n

What this means for HR and L&D<\/h2>\n