{"id":15672,"date":"2026-09-10T09:00:00","date_gmt":"2026-09-10T07:00:00","guid":{"rendered":"https:\/\/www.identifire.at\/?p=15672"},"modified":"2026-11-05T20:22:39","modified_gmt":"2026-11-05T18:22:39","slug":"ai-corporate-culture-everyone-leads","status":"publish","type":"post","link":"https:\/\/www.identifire.at\/en\/ai-corporate-culture-everyone-leads","title":{"rendered":"Now everyone leads. Whether they want to or not."},"content":{"rendered":"\n

AI changes what we work on. And how we think, decide and treat each other.<\/em><\/p>\n\n\n\n

The AI writes the presentation. Analyses the figures. Drafts the email. Builds the project plan. And, on request, delivers five better ideas on top. Impressive. But who decides which idea is actually good? Who notices when the analysis is off? Who carries the responsibility? And what happens to our self-image when a machine can suddenly do what we spent years learning? The new world of work is here. And it\u2019s asking questions.<\/p>\n\n\n\n

Artificial intelligence has long been changing more than processes and tools. It reaches deep into collaboration: into roles, hierarchies, skills and the feeling of being needed. That\u2019s why AI is not just a technology project. It is a culture project. And that changes the rules for everyone: employees, managers, HR and top management.<\/p>\n\n\n\n

Angstfreude: when excitement and uncertainty show up together<\/h2>\n\n\n\n

\u201cFinally I can hand off this tedious task.\u201d \u201cAnd what if the AI soon does it better than me?\u201d Two thoughts. One person. A feeling for which identifire\u00ae and wortwelt\u00ae coined a term of their own in their 2026 white paper: Angstfreude \u2013 the simultaneous thrill and unease. Excitement about undreamt-of possibilities meets the worry of becoming less important.<\/p>\n\n\n\n

That is not a sign of resistance to change. It is an understandable reaction to a technology that calls professional certainties into question. The experienced expert is glad to save time and wonders at the same time what her expertise will be worth in future. The young employee experiments enthusiastically with new applications but is unsure which results he can use without hesitation.<\/p>\n\n\n\n

The manager sees productivity potential and wonders how to guide the team through this change. So Angstfreude is not a problem that has to be eliminated as quickly as possible. It is a starting point for culture development. Because companies need people who stay curious, ask questions and try new paths. Even when not all the answers are on the table yet.<\/p>\n\n\n\n

What People & Culture practice reveals about AI<\/h2>\n\n\n\n

For the white paper \u201cAngstfreude \u2013 AI and Corporate Culture\u201d<\/a>, identifire\u00ae and wortwelt\u00ae spoke with, among others, ten People & Culture leaders from companies in different industries. Among them representatives of A1, OMV, \u00d6BB, Dynatrace, UNIQA, Magenta Telekom and Austrian Post. The conversations don\u2019t show a uniform AI reality. But they make shared tensions visible.<\/p>\n\n\n\n

People are trying things out, training, developing and discarding again. First AI agents support workflows. At the same time, questions arise about performance, leadership, responsibility and collaboration. Particularly interesting: learning happens not only in organised training, but in doing things together. Colleagues show each other applications, compare results and discover possibilities nobody in their team knew about before.<\/p>\n\n\n\n

This way, AI can have an unexpected cultural effect: it gets people talking to each other again. Provided companies make room for it.<\/p>\n\n\n\n

1. Employees become clients<\/h3>\n\n\n\n

Leadership used to be tied mostly to a formal role. A manager distributed tasks, defined goals, checked results and took responsibility. AI changes this logic. Anyone who gives a language model a task also has to define a goal, provide context, clarify expectations and assess results. An employee who writes a report with AI is no longer just its author. She becomes the client, the reviewer and the decision-maker.<\/p>\n\n\n\n

A controller who automates analyses has to interpret, question and explain connections more. A recruiter who uses AI to prepare interviews has to judge which suggestions are helpful and which might be problematic. AI can take over tasks. Not responsibility. That\u2019s exactly why skills we used to associate mainly with leadership are becoming more important. Clarity. Judgement. Delegation. Feedback. Responsibility.<\/p>\n\n\n\n

The Angstfreude white paper describes this development with the term \u201cPeople Lead\u201d. The pointed message: now everyone leads. Not because suddenly everyone carries line responsibility. But because more and more people direct digital systems and are accountable for their results.<\/p>\n\n\n\n

What does this mean for companies?<\/strong><\/p>\n\n\n\n

AI competence must not be limited to operating individual tools. Anyone who is supposed to use AI sensibly has to learn to structure tasks, question results and make decisions in a traceable way. That is a new form of leadership competence. And it concerns far more people than before.<\/p>\n\n\n\n

2. Learning suddenly also means unlearning<\/h3>\n\n\n\n

We invest a lot of time in teaching people things. But what happens when they have to let go of something that made them successful for years? This is where unlearning begins. It means the ability to consciously question familiar ways of thinking, routines and assumptions. An example: an experienced specialist has so far proven her quality by researching particularly carefully and preparing complex information herself. Now AI does large parts of this work in a few minutes.<\/p>\n\n\n\n

The new challenge lies less in gathering information. It lies in judging its quality and drawing the right conclusions. That sounds like a welcome relief. It can also feel like a loss. Because professional identity often comes from what we are especially good at. When these skills change, our self-image changes, too. Companies introducing AI therefore have to do more than explain new tools. They have to help people rediscover their own value in a changed world of work.<\/p>\n\n\n\n

What does this mean for leadership?<\/strong><\/p>\n\n\n\n

Unlearning needs time, trust and permission to be a beginner again for a while. Expecting employees to function perfectly while developing completely new ways of working makes change unnecessarily hard. A learning culture makes room for experiments. It rewards results \u2013 and insights. And it asks an important question: which routines still help us \u2013 and which are now in our way?<\/p>\n\n\n\n

3. AI puts performance up for renegotiation<\/h3>\n\n\n\n

An employee used to need three hours for a presentation. With AI, she has a good first version in 20 minutes. Is her performance lower now? Or even higher, because she can use the time saved for better decisions? The question is more uncomfortable than it sounds. Because in many companies, performance is still equated with visible effort. Those who work long hours seem committed. Those who finish quickly have to explain themselves. AI calls this pattern into question.<\/p>\n\n\n\n

When results come faster, attention shifts from execution to the quality of the result. But that falls short, too. Good work is more than finished documents, analyses or presentations. It includes collaboration, responsibility, creativity and the ability to take others along. The People & Culture conversations for the white paper make it clear: AI also raises questions about the visibility of performance.<\/p>\n\n\n\n

What counts as one\u2019s own work in future? Does it have to be disclosed when AI helped? And how are contributions assessed that can\u2019t be expressed in productivity metrics?<\/p>\n\n\n\n

What does this mean for HR?<\/strong><\/p>\n\n\n\n

Performance appraisal and competency models need to evolve. Not every time saving is a gain in quality. And not every task that can be automated should be done without human review. Companies need a nuanced understanding of what good performance will mean in future.<\/p>\n\n\n\n

4. Trust decides whether AI is used openly or secretly<\/h3>\n\n\n\n

Let\u2019s imagine two teams. In the first team, AI is officially allowed, but the manager is sceptical. Nobody knows exactly which applications are welcome. Mistakes are better not discussed. In the second team, there are clear rules. Applications are tried out together, results discussed critically. And whoever discovers something new shares it with colleagues. In which team will AI be used more sustainably? The answer is obvious.<\/p>\n\n\n\n

AI only becomes a meaningful part of collaboration when people can talk openly about how they use it. That doesn\u2019t mean every application should be allowed uncritically. Data protection, confidentiality, copyright and the quality of results need binding rules. But rules alone don\u2019t make a good AI culture. What matters is the attitude behind them. Is AI seen mainly as a control problem? Or as an opportunity to work better together?<\/p>\n\n\n\n

A culture of trust enables transparency. And transparency helps to spot mistakes, learn from each other and reduce risks. Those who only control AI use may never find out how it actually happens.<\/p>\n\n\n\n

5. Recruiting gets faster. But does it get more human?<\/h3>\n\n\n\n

An application bot answers immediately. It explains the process, answers questions and gives updates on the status of an application. Progress compared to weeks of radio silence. But what happens when candidates receive more messages yet still feel less seen? The Angstfreude white paper describes recruiting as one of the arenas where AI and corporate culture meet directly. Automation can improve processes and take pressure off recruiters.<\/p>\n\n\n\n

But it can also create distance if personal encounters are entirely replaced by standardised processes. So the decisive question is not whether a bot is better than a human. It is: which tasks can technology sensibly take over, so that people have time where their attention really counts? A quick acknowledgement of receipt is helpful. A personal conversation about motivation, expectations and collaboration is something else.<\/p>\n\n\n\n

Good recruiting uses technology without losing sight of the human relationship. And one more thing: the communication of an AI bot is also a touchpoint with the employer brand. Communicating there in a way that is incomprehensible, impersonal or evasive already shapes the first experience of the culture.<\/p>\n\n\n\n

6. Managers don\u2019t have to know everything. But they have to give direction.<\/h3>\n\n\n\n

Many managers are under double pressure. They are supposed to understand AI themselves while guiding their teams through the change. And far from every question has been answered. Which tasks will change? Which skills will remain important? How will roles develop? And how fast will all of this happen? This is exactly when leadership becomes particularly valuable. As a source of direction, not as an all-knowing authority.<\/p>\n\n\n\n

A manager doesn\u2019t have to be the first to master every new AI application. But they should be able to talk openly about opportunities and limits, allow questions and work out together with the team what makes sense. In the interviews for the white paper, the role model function of managers is explicitly emphasised. When managers don\u2019t try things out themselves, it\u2019s harder for their teams to take the first step. Then there\u2019s psychological safety.<\/p>\n\n\n\n

People need the certainty that they can show uncertainty, ask questions and raise mistakes openly. Especially when new technologies challenge old routines.<\/p>\n\n\n\n

7. The master prompt: when corporate culture becomes a work brief<\/h3>\n\n\n\n

One particularly exciting idea from the white paper is the master prompt. There is more to it than a well-written AI instruction. It is about how a company describes its attitude, values, quality standards and ways of working so that AI systems can take them into account in their work. What does good communication mean for us? How do we make decisions? Which limits apply? Which values should be recognisable in results? A master prompt can bundle such guidelines.<\/p>\n\n\n\n

That makes it a kind of cultural work brief for the AI. This opens up opportunities, but also raises new questions. Because culture can\u2019t be fully translated into rules. It changes through experiences, relationships and decisions. A master prompt can give direction. It doesn\u2019t replace a lived corporate culture. It is only as good as the attitude behind it.<\/p>\n\n\n\n

And that is exactly why working on it pays off: anyone who wants to explain to an AI how their company thinks and acts first has to get clear about it themselves.<\/p>\n\n\n\n

How companies develop a future-proof AI culture<\/h2>\n\n\n\n

The good news: you don\u2019t need the perfect AI strategy right away. But you do need a start that goes beyond choosing tools. Five steps help:<\/p>\n\n\n\n

    \n
  1. Understand the starting point.<\/strong> How is AI already being used? What hopes and worries are there? Where are skills or clear rules missing?<\/li>\n\n\n\n
  2. Experiment together.<\/strong> Teams test applications on concrete tasks and share their experiences<\/li>\n\n\n\n
  3. Rethink leadership.<\/strong> Managers need direction in dealing with AI \u2013 and employees need skills to direct digital systems sensibly<\/li>\n\n\n\n
  4. Clarify responsibility.<\/strong> Who checks results? Which data may be used? Which decisions explicitly remain with humans?<\/li>\n\n\n\n
  5. Develop the culture along the way.<\/strong> How are collaboration, performance and learning changing? Which behaviours do we want to strengthen?<\/li>\n<\/ol>\n\n\n\n

    What matters is not ticking these questions off once in a workshop. AI keeps evolving. Culture has to keep learning.<\/p>\n\n\n\n

    Frequently asked questions about AI and corporate culture<\/h2>\n\n\n\n
    How does AI change corporate culture?<\/summary>\n

    AI changes tasks, roles, collaboration and decision-making. As a result, trust, willingness to learn, a sense of responsibility and clear leadership become more important.<\/p>\n<\/details>\n\n\n\n

    What does Angstfreude mean?<\/summary>\n

    Angstfreude describes the simultaneous excitement and uncertainty people feel about artificial intelligence. identifire\u00ae and wortwelt\u00ae use the term in their white paper as a way into the cultural tensions of AI transformation.<\/p>\n<\/details>\n\n\n\n

    What is unlearning?<\/summary>\n

    Unlearning means consciously questioning previously successful routines and thought patterns and letting go of them where necessary, to make room for new ways of working.<\/p>\n<\/details>\n\n\n\n

    What does AI readiness mean?<\/summary>\n

    AI readiness describes how well a company is prepared \u2013 technically, organisationally and culturally \u2013 to use AI sensibly and responsibly.<\/p>\n<\/details>\n\n\n\n

    Conclusion: getting faster is easy. Being different is the art.<\/h2>\n\n\n\n

    AI can speed up work. Structure information. Deliver ideas. Simplify processes. How people treat each other is not its decision. Trust, belonging and good leadership don\u2019t emerge automatically. That remains our job. The real opportunity therefore lies not only in getting more done with AI. It lies in redesigning work. With more room for creativity, better decisions, learning together and real human encounters.<\/p>\n\n\n\n

    The future of AI isn\u2019t decided in IT alone. It is decided where people work together.<\/p>\n\n\n\n

    Source: identifire\u00ae and wortwelt\u00ae, \u201cAngstfreude \u2013 AI and Corporate Culture\u201d, May 2026. This article draws on the twelve cultural arenas and insights from ten People & Culture interviews. The everyday situations described are illustrative examples.<\/em><\/p>\n\n\n\n