Month: July 2026

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.

The tools are there, but the organization is searching

The figures on the investment side are unambiguous. The report Superagency in the Workplace by McKinsey (2025) 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.

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.

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.

What is AI adoption?

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.

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.

New work demands new skills, and they aren’t technical

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) 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, not button knowledge.

Since February 2025, this is no longer just an optional ambition either. Article 4 of the EU AI Act 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.

What works in practice: five ingredients

A recognizable pattern emerges from the organizations where adoption does take off. Five ingredients consistently return.

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), 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.

The common thread through all five: they are behavioral interventions. None of these ingredients are about the technology itself.

Practice as the engine of AI adoption

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.

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.

With AI Avatar training, that becomes possible: every conversation, scenario, and learning objective can be practiced in the employee’s own work context—from the resistance conversation to the critical assessment of AI advice—with 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. PractAIce is built on this exact idea, and is itself an example of AI that accelerates behavioral change rather than just taking over tasks.

What this means for HR and L&D

• Treat AI adoption as a behavior program, not as a rollout. Define what concrete work behavior you want to see six months from now, and design interventions around that: a small learning rhythm, practice in the work context, champions close at hand.
• Develop AI literacy as a skill, not as a certificate. Article 4 of the EU AI Act calls for a sufficient level appropriate to the role. A ticked-off e-learning course demonstrates participation; repeated and evaluated behavior demonstrates literacy.
• Train the conversations surrounding the change as well. Managers and AI champions carry the adoption. Let them practice dealing with resistance, concerns, and bringing colleagues along before those conversations really matter.

Frequently Asked Questions

What is AI adoption?

AI adoption is the process in which employees and teams genuinely and permanently integrate AI into their daily way of working. It differs from implementation: implementation is about access to tools, whereas adoption is about lasting changes in work behavior.

What is AI literacy and is it mandatory?

AI literacy is the ability to use AI systems in an informed manner: evaluating output, recognizing risks and bias, and knowing where the boundaries lie. Since February 2025, Article 4 of the EU AI Act obliges organizations that use AI to ensure a sufficient level of AI literacy among their employees, appropriate to their role.

How do you get employees onboard with AI?

Not with a big launch, but with behavioral interventions: clear frameworks that instill trust, champions on the work floor, a small weekly learning rhythm, application in their own work, and room to reshape the work itself. Practicing with immediate feedback, including on the conversations surrounding the change, makes the difference between a pilot and lasting behavioral change.

About the author: Sven is the founder of PractAIce and a behavioral expert. He has worked for years on driving behavioral change, embedding new habits into daily practice, and making soft skills concrete and measurable.

Wondering if AI adoption in your organization is moving beyond the pilot stage? With PractAIce, employees and managers practice the behaviors and conversations that drive change, in their own work context, with immediate feedback. Discover AI Avatar training or request a demo today.

Few professional groups feel the rise of AI as directly as trainers and coaches. E-learning platforms promise the same results for a fraction of the price, clients demand demonstrable impact, and the traditional one-day training course is losing ground. The natural knee-jerk reaction is understandable: viewing AI as a competitor. This article outlines why that is the wrong interpretation, and how AI role-plays can instead become the missing piece of your value proposition: the practice arena between sessions, delivering proven behavioral change as the end result.

The environment for trainers is changing faster than their offerings

The demand for personal and professional development has never been higher. The Future of Jobs Report by the World Economic Forum (2025) shows that employers expect 39 percent of workers’ core skills to change by 2030. According to research by Gartner, 85 percent of business leaders expect the need for skill development to surge in the coming years due to AI and digitalization, with many feeling that traditional learning reacts too slowly to the pace of this shift.

At the same time, what organizations are asking for is changing. The Workplace Learning Report by LinkedIn (2025) reveals that nearly half of L&D professionals hear executives question whether employees possess the skills needed to execute company strategy. Organizations must become more agile, shifting the demand from generic programs toward tailored training: focused on the specific conversations, clients, and tensions of *this* team, with visible impact on behavior. Clients are asking very different questions than they did five years ago. No longer “what does the training cover?”, but “what actually changes on the work floor afterward, and how do we prove it?”

You might recognize this shift in your own proposals. The content still convinces, but the demand for retention and evidence grows stronger with every pitch.

The gap in the traditional proposition

This is precisely where the classic training model falls short. From the research on deliberate practice by K. Anders Ericsson (Psychological Review, 1993), we know that true skill mastery comes from targeted, repeated practice paired with immediate feedback. A single training day can deliver insight and initial practice, but it cannot offer repetition: a trainer cannot stand beside every participant for weeks as they apply what they learned. This creates the well-known transfer problem: what was learned in the classroom fades in the weeks that follow, and no one can prove what actually stuck.

The gap in many training companies’ value proposition isn’t the quality of the training itself, but what happens *after* the session. Whoever bridges that gap fundamentally strengthens their position.

AI role-plays: an extension of the trainer, not a replacement

The productive view of AI role-playing isn’t replacement—it’s extension. The trainer remains the expert who reads the group, sets the methodology, and facilitates the real human conversation. What AI adds is scale—something one human simply cannot provide: every participant can keep practicing between sessions, at their own convenience, in scenarios tailored to their daily work.

In an AI role-play, via AI Avatar training, a participant re-enacts the scenario from the workshop with an AI counterpart that responds realistically: offering pushback, expressing emotion, and adapting where a static script would fail. Every session provides immediate feedback on concrete behavior. The trainer then sees not only *that* participants practiced, but *how* the group is progressing, competency by competency over time.

PractAIce as a strategic tool for training firms

PractAIce was built to fulfill this role, keeping the trainer as the true owner of the content. Trainers and training companies build their own AI role-plays and embed their own methodology and frameworks into the feedback system, ensuring the AI evaluates along the exact principles the trainer teaches. Every conversation, scenario, learning objective, and skill can be trained within the participant’s actual work context—whether it’s the sales call for a specific sales team or the difficult news conversation for a healthcare provider.

In this way, AI ceases to be an expense or a threat; it becomes part of your business model. Strategically, this transforms the training firm’s position. The proposition shifts from selling workshop days to delivering end-to-end programs with measurable results: workshop, practice phase, and progress measurement forming a unified whole. This sets you apart in tenders where long-term retention and measurability are increasingly required, creates a recurring revenue stream alongside training sessions, and answers the demand of organizations looking for agile, customized solutions. The trainer is no longer a reseller of technology, but uses the platform as a direct extension of their own craft.

How a training agency puts this into practice

A concrete example makes this clear. A training agency delivers a leadership program on constructive feedback and tough conversations to a mid-sized healthcare provider. Previously, this consisted of two training days and a follow-up half-day session. With PractAIce as a tool, the program looks fundamentally different.

The agency translates the client intake into role-plays written in the client’s language: a conversation with an overworked nurse, or addressing a colleague regarding protocol compliance. Following the first training day, participants spend six weeks doing short, twice-weekly AI role-plays with an AI Avatar, scored against the agency’s custom feedback model. The trainer tracks group-level progress and spots where people get stuck—for instance, digging deeper after encountering resistance. As a result, the follow-up session isn’t a recap of theory from six weeks ago, but a targeted deep-dive into patterns revealed by the practice data. The client receives a clear report showing competency development over time.

Three things change for the agency simultaneously:
1. The trainer shifts from a lecturer to a data-informed coach.
2. The client sees black-and-white proof of the program’s return on investment, making contract renewals significantly easier.
3. The agency gains a recurring platform license component in its offerings without ever having to build software themselves.

What this means for trainers and coaches

• Reposition your offer around behavior and evidence. The market is shifting from knowledge transfer to demonstrable behavioral change. Structuring your programs around practice journeys backed by analytics aligns directly with client demands.
• Use AI as an extension of your craft, not as a gimmick. The true value lies not in the technology itself, but in the repeated practice and behavioral insights it places directly into the trainer’s hands.
• Choose a platform where your own content takes center stage. A trainer who can build their own methodology, scenarios, and feedback frameworks reinforces their unique value proposition. A rigid, closed library makes you interchangeable.

Frequently Asked Questions

What is an AI role-play?
An AI role-play is a conversational practice exercise where a participant engages in a realistic dialogue with an AI Avatar that responds like a real human: complete with resistance, emotion, and distinct objectives. Afterward, the participant receives feedback on specific behaviors, enabling repeated practice to translate into visible development.

Does AI replace the trainer?
No. The trainer sets the methodology, reads the room, and leads the high-value conversations that only a human can facilitate. AI takes over the component that is practically impossible for a human trainer: providing weeks of continuous practice with instant, individual feedback. In return, the trainer receives behavioral data to sharpen their own coaching.

How do you integrate AI into an existing training program?
By structuring the practice period between touchpoints: translate the program’s learning goals into role-play scenarios set in the participants’ real work context, have them engage in short, frequent practice sessions between workshops, and use the progress data to guide the follow-up session. This ensures that training and practice reinforce each other rather than existing in silos.

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About the author: Sven is the founder of PractAIce and a behavioral expert. He has worked for years on driving behavioral change, embedding new habits into daily practice, and making soft skills concrete and measurable.

Wondering if this fits your training practice? PractAIce is built by and for trainers and coaches: you build your own role-plays, maintain your own methodology, and gain the behavioral data that elevates your proposition. Discover the possibilities or request a demo today.

Almost every organization wants a feedback-and-accountability culture (*aanspreekcultuur*). The core values have been defined, the feedback training has been completed, and the model is figuratively hanging on the wall. And yet, the conversation still doesn’t happen: the colleague who consistently delivers late is not called out, concerns about workload are swallowed, and a mistake is only discussed when it can no longer be hidden. This article describes what a feedback culture truly is, why giving feedback goes wrong so often, and why practiced behavior—rather than knowledge of models—determines whether people actually speak up to one another.

The values are on paper, but the conversation remains unheld

Most organizations have carefully articulated their values: ownership, respect, collaboration, openness. Often, the specific behavior associated with them has even been made concrete. The challenge rarely lies in defining those values, but in consistently applying them in daily work. Because that is precisely where differences arise. To one person, ownership means taking initiative; to another, it means honoring agreements. To one person, respect means giving space; to another, it means having the honest conversation.

Culture does not change because values are communicated. Culture changes when people recognize, practice, discuss, and repeat the behavior behind those values. However, the hustle of the day quickly shifts focus back to deadlines and operational priorities. If speaking up is not given a fixed place in daily work, that culture remains something people talk about, rather than something people do.

Perhaps you recognize this: the training has been completed, everyone knows the feedback rules, and in the very next team meeting, nobody says a word again.

 

What is a feedback culture?

A feedback culture is a work environment where colleagues address each other on behavior, agreements, and results in a timely, direct, and respectful manner, and where giving and receiving feedback is a normal part of daily work rather than an exception. It is not about harshness, but about clarity: issues are discussed while they are still small, directly with the person involved, and with the intention of improving together.

That final point distinguishes speaking up from blaming or taking someone to task. Addressing someone is focused on behavior and the future; calling someone out or punishing them is focused on the person and the past. The difference between the two lies not in the intention on paper, but in how the conversation is actually conducted. And that is precisely where things often go wrong.

Why giving feedback goes wrong so often

Feedback is often treated as if more is always better. Research shows a more uncomfortable picture. In the classic meta-analysis by Kluger and DeNisi (Psychological Bulletin, 1996), based on hundreds of studied feedback interventions,

more than a third of the interventions actually reduced performance. Feedback that shifts attention toward the person rather than the task triggers defensiveness instead of learning. Hattie and Timperley (Review of Educational Research, 2007) reach a similar conclusion: feedback is among the most powerful learning interventions we know, but only when it is aimed at the task, the process, and the recipient’s self-regulation, and not at the person.

Research by Gallup confirms that the problem lies not in the need for feedback, but in its execution: employees who receive valuable feedback are far more engaged and significantly less burned out, but a large portion of employees are not looking for more frequent feedback because the feedback they do receive is not valuable.

The conclusion can be sharply defined. Giving feedback is not a knowledge problem. Almost anyone can list the rules: describe the behavior, state the effect, do it in a timely manner. The problem lies in execution under pressure: the tone in the very first sentence, the silence after the message, following up with questions when the other person becomes defensive. That is behavior, and behavior is not learned from a model.

Psychological safety as a prerequisite

There is a second layer as well. Speaking up requires that people feel safe enough to take the risk of having the conversation. Research by Amy Edmondson (Administrative Science Quarterly, 1999) showed that teams with high psychological safety report mistakes sooner and learn faster; not because more things go wrong, but because what goes wrong is actually discussed.

That safety is not a policy document either, but the sum of behaviors. It arises in a manager’s reaction to the first critical remark, and in the way a team deals with a colleague who shares a mistake. A single punitive reaction does more damage to that culture than ten posters can build up. Anyone who wants to strengthen the culture must therefore start with this behavior, and especially with the behavior of leaders.

You practice a feedback culture, conversation by conversation

Knowing is not doing. Between understanding a feedback model and conducting a good conversation lies practice: trying, reviewing, trying again. The problem has always been that this space to practice was missing. Practicing on a actual colleague is risky, and hiring an actor for every employee is not scalable.

This is where AI substantially changes the possibilities. With AI avatars that respond like real people, every employee and manager can practice the exact conversations they need to hold in their own daily work: addressing a colleague about an unfulfilled agreement, making workload open for discussion, sharing a concern about quality or safety, or receiving feedback without becoming defensive. Every conversation, scenario, and learning objective can be practiced in one’s own work context. After practicing, targeted feedback and feedforward follow: what went well, what could be stronger, and what alternative behavior would help in the next situation. The AI coach helps identify patterns and make targeted adjustments to personal development.

Additionally, teams can create, share, and discuss roleplay scenarios themselves. This generates not only individual growth, but a shared language for addressing one another: what does it sound like here when someone brings up an agreement? That is the difference between a single training and true embedding: not practiced once, but repeated, recognizable, and scalable. In this way, a feedback culture becomes something that people build together, conversation by conversation. PractAIce was built for precisely that movement.

What this means for HR and leaders

• Start with leaders. Their response to criticism and mistakes determines the safety of the entire team. Have them be the first to practice receiving feedback, not just giving it.
• Make practicing a permanent part of work. A single feedback training will not change a culture. Short, repeated practice moments built around real situations from daily work will.
• Measure behavior, not satisfaction. A high satisfaction rating for a training says nothing about whether the conversation actually takes place afterward. Look at observable behavior over time: are people speaking up sooner, more often, and better?

Frequently Asked Questions

What is a feedback culture?

A feedback culture is a work environment where colleagues address each other on behavior, agreements, and results in a timely, direct, and respectful manner. Giving and receiving feedback is a normal part of daily work, aimed at improving together rather than placing blame.

How do you create a feedback culture in your team?

By practicing behavior instead of merely communicating values. Start with the manager, make desired feedback behaviors concrete, have people repeatedly practice them in recognizable situations from their own work, and discuss as a team what works. Psychological safety grows with every positive experience.

What is the difference between feedback and feedforward?

Feedback looks back: what did you do, and what effect did that have? Feedforward looks forward: what concrete alternative behavior will help in the next, similar situation? The combination works best, because the recipient not only hears what happened, but also knows what they can do differently tomorrow.

About the author: Sven is the founder of PractAIce and a behavioral expert. He has worked for years on behavioral change, embedding new habits into daily practice, and making soft skills concrete and measurable.

Are you unsure whether speaking up in your organization is moving from paper to practice? With PractAIce, employees and leaders practice giving and receiving feedback with an AI Avatar in their own work context, supported by immediate feedback and feedforward. Discover AI Avatar training or feel free to request a demo.

Ask an executive about the biggest risks, and the answer will focus on the market, technology, or finance—rarely on skills. Yet, a risk is quietly mounting in precisely that area: the skills gap—the disparity between people’s current capabilities and the demands of the work. This article explains why that gap is widening faster than ever, how ignored gaps accumulate into “skills debt,” and why practice, feedback, and repetition offer the only reliable path to bridging the divide.

The work changes, the job description does not

The figures on the labor market have been pointing in the same direction for years. According to research by Gartner, 58 percent of the workforce needs new skills to continue performing their jobs successfully. The number of skills required for a single role has been increasing by around ten percent per year since 2017, and one in three skills listed in a 2017 job posting has now become outdated. The Future of Jobs Report by the World Economic Forum (2025) adds a forward-looking perspective: employers expect that 39 percent of employees’ core skills will change by 2030.

Compare those figures with the average organization, and the tension becomes clear. Job architectures are revised only every few years. Learning and development plans are organized by calendar year. Performance review cycles look backward. The work itself pays no attention to any of that: tasks shift from month to month, and AI changes work processes while they are already in motion. On paper, every position is filled. In practice, the work beneath those positions has already changed. Roles are static, skills are dynamic, and it is precisely in that gap that the skills gap emerges.

You may recognize the situation. The vacancy has been filled, the team is complete, and yet the question remains: are the people in those roles truly equipped for the work that awaits them next year?

What exactly is a skills gap?

A skills gap is the difference between the skills employees currently possess and the skills required for the work—both today and in the near future. While this may sound abstract, it can be made concrete by asking three questions: What skills does the work require, both now and two years from now? What skills are already present in-house—often exceeding what the formal job structure indicates? And which skills are missing? That third question is where targeted development begins.

Anyone who seriously addresses these questions usually discovers two things. First, the organization possesses more capabilities than job titles suggest: people perform work that is not documented anywhere. Second, skill shortages rarely align with where training budgets are currently allocated. This lies at the heart of a skills-based approach: focusing on skills—rather than job roles—as the fundamental unit for assessment, development, and deployment.

From Skill Gap to Skill Debt

Software developers are familiar with the concept of technical debt: every quick fix you choose today becomes maintenance you will eventually have to perform later—with interest. The same mechanism applies to skills. A skill gap that is visible today can be measured and addressed. A gap that is left unattended becomes skill debt: deferred maintenance on an organization’s ability to do its work.

This debt does not appear in a single report but creeps in gradually. In customer conversations that become more difficult. In projects that slow down because no one has truly mastered the new way of working. In employees leaving because they are no longer developing. Research by Deloitte shows the other side of the same coin: organizations that focus on skills deploy talent far more effectively and anticipate change better than organizations that continue to rely on traditional job-based thinking. Organizations that manage by skills gain an advantage—but only if they are able to develop those skills.

Why Soft Skills Represent the Biggest Gap

Not every gap is equally visible. A shortage of technical skills is easy to spot: a missing certification or a system that no one knows how to operate. The gap in soft skills is of a different nature. As AI takes over more task-based work, the value of human work shifts toward what cannot be automated: collaboration, critical thinking, leadership, and the ability to adapt while the work itself continues to change. The World Economic Forum consistently ranks these skills among the most important core skills for 2030.

At the same time, these are the skills that are the hardest to measure. “Handles a difficult conversation calmly and clearly” does not appear on any diploma. It is behavior, and behavior only reveals itself in real situations. This creates an uncomfortable reality: the skills that will become most critical to organizations are also the skills that no one knows with certainty who truly possesses. As a result, the largest gap is also the least visible.

Closing the Gap: Practice, Feedback, Repeat

The standard response to identifying a gap is predictable: purchase a training course. But transferring knowledge does not close a behavioral gap. Ever since Hermann Ebbinghaus described the forgetting curve—replicated in 2015 by Murre and Dros—we have known that most newly acquired knowledge fades within days when it is not reinforced through repetition. A skill is not developed by hearing about it, but by doing it: practicing, receiving immediate feedback, trying again, and applying it in real work.

For a long time, this has been the practical challenge: practice with feedback is labor-intensive and difficult to scale. AI role-playing changes that equation. With an AI Avatar training, employees practice the conversation they will actually have tomorrow, within their own work context, with an AI counterpart that responds realistically. Every scenario, every learning objective, and every skill can be practiced, and every session provides feedback on concrete behavior. This not only closes the gap but also makes it visible: by competency, by team, and over time. Learning shifts from an annual HR activity to continuous infrastructure for organizational agility. PractAIce is built on that principle.

What This Means for HR and L&D

Three key implications stand out:

• Map the gap by skill, not by job title. Do not ask which positions are vacant, but which behaviors the work of the next two years will require and who can demonstrably perform them today. The answer is often very different from what the organizational chart suggests.
• Treat the gap as an ongoing challenge. An annual learning plan fits a labor market that changes once a year, not work that shifts every month. Short, frequent practice sessions with feedback keep skills up to date in ways that occasional training never can.
• Measure progress by behavior, not participation. Completing a course only shows that someone attended. Only repeated, observable, and assessed behavior demonstrates that the gap is actually becoming smaller.

One important nuance should be added. Behavioral data are development data, not a performance evaluation tool. Their value lies in making growth visible. Organizations that safeguard this distinction build the trust people need to practice and improve their behavior.

Frequently Asked Questions

What is a skill gap?

A skill gap is the difference between the skills employees currently possess and the skills that the work requires, both now and in the near future. The gap can be identified for each team and each skill and serves as the starting point for targeted development.

What is skill debt?

Skill debt is the accumulated impact of skill gaps that remain unaddressed: deferred maintenance on an organization’s capabilities. Like technical debt, skill debt grows quietly over time until it becomes visible in quality, agility, and employee turnover.

How do you close a skill gap in your organization?

Start by measuring: what behaviors does the work require, and who can demonstrably perform them? Then develop those behaviors through repeated practice with immediate feedback, preferably within the employee’s own work context. AI role-playing makes this type of practice scalable and enables measurable progress for every competency.

About the author: Sven is the founder of PractAIce and a behavioral change expert. For many years, he has focused on helping organizations achieve lasting behavioral change, embedding new behaviors into daily practice, and making soft skills concrete and measurable.

Not sure where the biggest gap in your organization lies? PractAIce makes skills visible and trainable. Employees practice every conversation, scenario, and learning objective with an

AI Avatar in their own work context, while their development becomes measurable for every competency. Discover the AI Avatar training or request a demo.