Fear of using AI - recommendations for decision-makers

AI as an opportunity for efficiency

Introducing AI requires a clear strategy and open communication

Actively involving and empowering employees - here's how

Recognise reservations, take them seriously and refute them

Start with quick wins - build trust through initial successes

Practical example: AIMAX® and EMMA as an easy introduction

Conclusion: Moving forward boldly with a sense of proportion

A common prejudice is that the use of AI is convenience, laziness or even fraud. This concern is real: many employees fear being seen as less hard-working as a result of using AI and therefore hide their use of AI from colleagues. In fact, another recent study by Princeton University confirms that employees who use AI tools are often perceived by others as less competent and less motivated. This social sanction for AI use - an implicit stigma that "if you use AI, you're cheating" - can slow down the introduction of new technologies.

Decision-makers should actively counteract this prejudice. Using AI is not cheating, but smart working. When routine tasks are performed by AI and automation, employees have more time for creative, value-adding tasks.

Instead of seeing AI as a replacement for human performance, it should be seen as a strategic tool that enables efficiency gains and better results. An apt comparison is the calculator: using it does not mean that the accountant is "cheating", but that he or she arrives at the correct result more quickly.

AI can also be used to carry out complex analyses more quickly, automatically pre-qualify customer enquiries or provide suggestions for routine decisions. Productivity increases without reducing the expertise of employees - on the contrary, they can concentrate on more demanding aspects.

This perspective should be anchored in the corporate culture: AI use is desirable when it makes sense. Managers can, for example, set an example by using AI themselves and sharing successes to show that smart AI use is seen as a strength and not as laziness.

Before a company introduces AI tools, a clear AI strategy is essential. Without a strategy, there is a risk of wild experimentation or that AI will be used "somehow", but nobody knows exactly what it is for.

Decision-makers should define specific goals: Which business processes are to be improved by AI? Is it about saving time, reducing costs, improving quality or providing new services? A clear objective helps to view AI not as an end in itself, but as a means to business success.

The strategy should also include a risk assessment and guidelines. What is AI allowed to do in the company - and what is it not allowed to do? For example:

• Which AI systems may be used for which purpose?
• Are employees allowed to use artificial intelligence for customer correspondence?
• How do you ensure that no sensitive data is released to the outside world in an uncontrolled manner?
• etc.

Management should clarify such questions at an early stage and record them in an AI policy. This creates security and trust when dealing with the new technology.

Open communication about the introduction of AI is just as important. Changes in the workplace often lead to uncertainty, especially when it comes to such a fundamental innovation as AI.

Transparency allays fears: Inform your workforce about planned AI projects at an early stage. Explain what problem the AI is intended to solve, how it works and what role employees will play in it. Proactively address typical fears - such as the fear of being replaced by AI - and emphasise that the technology is being introduced to provide support, not to make someone obsolete. If employees understand the purpose and benefits, they are much more willing to accept the innovation.

An open error culture is also beneficial: make it clear that AI systems are trialled and continuously improved and that employee feedback is welcome.

Communicating as equals and responding to questions and concerns creates a climate in which AI is seen as a joint project, not a black box imposed from above.

The best concepts are of little use if the workforce does not go along with them. Therefore, take your employees on the AI journey with you from the outset. In practice, this can involve several approaches:

• Early involvement: put together interdisciplinary teams in which specialist departments explore the potential applications together with IT/AI experts. Employees from the specialist departments know the daily processes and pain points best - their input is worth its weight in gold when it comes to identifying useful AI applications.
• Training and further education: Training should be offered precisely because many people do not have in-depth technical knowledge. Explain the basic concepts of AI, show concrete examples of applications in the corporate context and provide training on how to use the planned tools. If the fear of the unknown is taken away, acceptance will increase enormously. Employees who understand how AI works and how it can help them feel empowered rather than threatened.
• Pilot users and ambassadors: Identify employees who are open to new ideas and let them be the first to work with AI in small pilot projects. Their experiences - both positive and negative - can then be shared with everyone. These internal AI ambassadors can explain to colleagues at eye level what AI brings, where the stumbling blocks are and how to overcome them. Success stories from your own team are often more convincing than abstract promises from management.
• Feedback loops: Set up channels (workshops, surveys, regular meetings) in which employees can report back their concerns, experiences and ideas on the use of AI. Show that this feedback is taken seriously and actually results in improvements or adjustments. This creates the feeling of being part of the process instead of just being affected.

All of these measures send the message: "We are shaping this together." Those who are actively involved are more likely to develop their own initiative and curiosity about AI instead of feeling that they are being presented with a fait accompli. In addition, dialogue enables managers to recognise at an early stage where fears are still smouldering or misunderstandings exist and can take targeted countermeasures.

Even with the best preparation, there will be real reservations about using AI - these should not be brushed aside, but taken seriously and constructively refuted. In addition to the stigma already mentioned ("AI users are lazy"), there are other typical concerns.

"AI use could be viewed negatively": As mentioned above, employees fear for their reputation if they use AI. Studies refer to this phenomenon as a social evaluation penalty - although AI increases performance, its use can damage reputation.

This reservation can only be dispelled through a cultural change in the company. Make it clear that efficiency is not cheating: if someone achieves more with AI support, this should be recognised and rewarded.

Introduce a corporate culture in which the use of smart tools is viewed positively - for example, by openly asking in meetings which tools (AI or other) were used to achieve good results. This makes transparency normal and reduces the pressure on colleagues because it is clear that whatever helps is allowed.

The role model function of management is also important - when line managers openly use AI tools and communicate this, it signals to everyone: The use of AI is desirable, not embarrassing.

"We're giving up control": Many decision-makers are worried about losing control when AI systems are introduced.

This fear has two facets: Firstly, technical/operational control ("Do we still understand the AI's decisions?") and secondly, strategic dependence on technology suppliers.

This concern is fuelled in particular by the dominance of US corporations in AI technologies - 68% of Germans believe that Germany is too dependent on the USA and China in this regard. Bitkom President Dr Ralf Wintergerst recently warned that we must not "slip into new digital dependencies" when it comes to AI .

Companies should heed this warning and rely on trustworthy, preferably local AI solutions that give them data sovereignty and creative power. In concrete terms, this means that you should also scrutinise AI applications in terms of where the data is processed and who owns the algorithms.

An AI that runs exclusively in a foreign cloud and possibly "sucks" training data from your company could create dependencies in the long term. Solutions that can be operated locally or in a private cloud and function transparently offer more control here.

In addition, expertise should be built up within the company in order to understand and adapt the AI applications - this way you remain in control of the technology and are not blindly dependent on external service providers.

"Data protection and compliance issues": Data protection is a key issue, especially in Germany. Employees and decision-makers ask themselves: Are we even allowed to have customer data or internal information processed by an AI?

It helps to choose data protection-compliant solutions from the outset and to communicate this openly. If an AI runs locally in the company or at least anonymises/encrypts sensitive data, the risk is lower.

Transparency towards the works council and employees in terms of data protection requirements is important: explain what data the AI uses and for what purpose, and obtain consent if necessary.

Show that AI and GDPR do not have to be a contradiction in terms. By proactively addressing data protection concerns, you take the wind out of the sails of one of the biggest reservations.

To summarise: Actively listen to the fears in your organisation. Whether it's the fear of looking stupid, losing control or violating regulations, each of these concerns can be alleviated through education, appropriate technology selection and cultural guardrails.

It is important not to be defensive ("Oh, it won't happen"), but to point out possible solutions. In this way, people gain confidence that AI can be used safely and sensibly with the right framework conditions.

Theory and strategy are important - but in the end, practical success is the most convincing factor. It is therefore advisable to start the introduction of AI with manageable pilot projects that quickly bring tangible benefits. Such quick wins serve several purposes: they show sceptical colleagues that AI actually helps, they provide a sense of achievement for everyone involved and they allow management to learn first-hand what works well in their own company.

How do you select suitable quick-win projects? Look for tasks or processes that have the following characteristics: relatively easy to define, frequently recurring and with a clearly measurable result (e.g. time saved per process). For example, manual data entry, reporting, simple customer enquiries or internal coordination processes are ideal. AI-supported automation can have a rapid impact here. It is important to keep the project small enough that it can be implemented in a few weeks (or even days), but relevant enough that the benefits are noticeable.

For example, robotic process automation (RPA) - software robots that perform routine clicks and data entry - is an excellent way to achieve initial automation success. By using AI and RPA, noticeable improvements can be achieved in a short space of time.

It is important to make the successes visible and communicate them internally. Present the results of the pilot project: "Thanks to AI support X, we were able to speed up process Y by 80%" - such concrete facts are convincing and take away the abstract character of the topic. Celebrate the team members who implemented the pilot project as pioneers. This may awaken the desire in other departments to also benefit from AI.

To minimise both data protection concerns and the barriers to entry, it is worth taking a look at local AI solutions. A concrete example of this is the combination of AIMAX® and the RPA platform EMMA. These are AI tools "made in Germany" that can be operated on site at the company.

This means that all data remains in-house and the application runs on the company's own servers, preventing sensitive information from being leaked. This offline capability ensures that even strict data protection and compliance requirements (keyword GDPR) can be adhered to.

How does AIMAX® and EMMA? EMMA is a cognitive RPA solution that takes care of repetitive routine tasks - from data entry to filling out forms. AIMAX is an AI agent with generative intelligence that can take on creative or more complex tasks, e.g. drafting texts, preparing decisions based on large amounts of data or recognising anomalies. In combination, this results in a powerful team: EMMA handles repetitive mass processes automatically and reliably, while AIMAX® takes care of the intelligent parts that require real thinking skills. Together, they create end-to-end automation (hyperautomation) from simple to complex tasks - with high efficiency.

The highlight from a decision-maker's point of view: integration is low-threshold. The solution can be integrated into existing system environments without having to spend months setting up large-scale IT projects. Initial processes can often be automated in just a few days. Companies can therefore bring a prototype live very quickly and test on a small scale how AI and RPA work in their context. This virtually pre-programmes quick wins - and the workforce can immediately see what "AI" actually does and how it makes everyday work easier.

This example also addresses the above-mentioned caveat of dependency: AIMAX® and EMMA run completely under your own control. You are not dependent on the servers of a tech giant and retain full data sovereignty. At the same time, you get state-of-the-art AI functions, but on your own terms (local, secure, controllable). Such solutions can allay decision-makers' fears of having to jump straight into the deep end of a huge AI project. Instead, you start cautiously, but still in a practical and effective way.

The fear of using AI is understandable - it is fuelled by concerns about reputation, control and unknown risks.

However, developments in recent months clearly show that AI offers enormous opportunities for more efficient processes, better decisions and new business opportunities. Instead of being paralysed by diffuse fears, decision-makers should tackle them proactively. With a clear strategy, transparent communication and employee involvement, an environment can be created in which AI is seen as an opportunity - not a threat.

It is important to take the first step: small but decisive. A well-chosen pilot project with a data protection-compliant, easy-to-integrate AI solution can become a catalyst for cultural change. Employees experience the benefits directly and lose their inhibitions. Management learns what works and can scale successes across the company.

In the end, AI is nothing mystical, but a tool that is intended to serve people. If decision-makers exemplify this attitude and set the framework conditions wisely, the initial fear will quickly disappear. If we replace fear with curiosity and rigid rejection with active shaping - then nothing stands in the way of successful AI utilisation.