Not all companies are the same. In many organizations, AI has long been used as a matter of course, embedded in automated processes, analysis tools, or assistance systems. There, AI is no longer hype, but infrastructure.
Nevertheless, the reality is often different. AI is perceived as new and difficult to grasp.
Not because of the technology itself, but because of its impact on work, roles, and responsibilities. This uncertainty cannot be resolved by tool demos or technical arguments. The crucial question is therefore not which tools are used, but how people are prepared for this change. So how do you prepare a team for the introduction of AI tools?
Why AI implementation is not an IT project
AI rarely fails in companies because of the technology. Most tools work, can be integrated, and can be deployed quickly. What doesn't work is the assumption that this will automatically result in better work. This is because AI not only intervenes in processes, but also in decision-making logic. Who evaluates results? Who bears responsibility? And what even counts as “good performance” when suggestions, analyses, or texts originate in part from systems?
For employees, therefore, it is not crucial how an AI tool is operated. What is crucial is how it changes their role. Will my expertise remain relevant? Will I be replaced or supported? And how will I be measured in the future?
This is precisely why the introduction of AI is not an IT project, but a management task. It requires clear decisions about how humans and AI should work together – and which responsibilities remain explicitly with humans.
Change management: How can acceptance of AI be achieved?
The successful introduction of AI is based on two pillars: useful tools and clear organizational logic. Studies such as the 10-20-70 principle show that the greatest leverage lies not in algorithms or technology, but 70 percent in people and processes (and only 10 percent in theoretical knowledge). Acceptance arises when this 70 percent is consciously designed.
Why is acceptance important?
Without acceptance, AI remains ineffective. Tools are available, but they do not shape everyday work. Costs and additional effort are incurred without improving results.
Acceptance is also a prerequisite for quality and safety. AI provides suggestions, not responsibility. Results must be classified, checked, and used consciously. This cannot be achieved through rejection or uncritical adoption. Acceptance means using AI as a tool and clearly leaving responsibility with humans.
In short: without acceptance, there is no use. Without use, there is no effect. And without effect, there is no meaningful use of AI.
Where companies are using AI today
Companies use AI in clearly defined areas. These include the automation of routine tasks such as document processing, text classification, or standard reports, as well as the analysis of large amounts of data for forecasts, risk assessments, or customer segmentation. AI supports decisions, but does not make them.
Generative AI is also increasingly being used, especially for content creation. It helps with drafting, structuring, and idea generation in marketing, communication, and internal documents. These specific areas of application facilitate acceptance because the benefits are quickly apparent and responsibility clearly remains with humans.
Why tool training alone is not enough
Those who use AI in their daily work rarely face the question of which button to click. What is more important is whether a result is meaningful, plausible, and responsible. Traditional tool training hardly prepares people for this. It explains functions, but not how results should be evaluated in the context of processes and decisions.
AI quickly generates convincing results. Texts sound well-rounded, analyses seem logical, and suggestions are neatly formulated.
This is helpful because it reduces the amount of preparatory work required. At the same time, however, it increases the responsibility of correctly interpreting results. Without this understanding, productivity can easily be confused with quality.
Which skills really count
In combination with clear processes, AI can be a very effective tool. The decisive factor is how teams deal with AI-generated results. Critical thinking plays a central role here.
Those who work with AI should be able to understand why a particular suggestion or approach is used and another is not. This is less a technical question than a question of professional working methods.
Equally important is the ability to understand AI as a support tool. It can prepare, structure, and reveal different options. The real value is created when people use this groundwork, prioritize it, and make informed decisions. Clarity about responsibilities creates security. Teams should know who makes decisions and who is responsible for what.
What rules companies need for AI
AI can be used productively if the framework is right. Clear rules do not create restrictions, but rather security. They help teams understand what is allowed, what is not, and what they can rely on. This is precisely the prerequisite for AI to be used in everyday life and not become a gray area.
Data protection and GDPR
The GDPR is the binding basis for the use of AI in companies. It always applies when personal data is processed or entered into AI systems. For everyday work, this means above all that there must be clear rules about which data may be used and which may not.
Technical responsibility for implementing the GDPR lies where AI is used. The legal framework should be centrally defined by data protection, compliance, or legal departments and reviewed regularly. This ensures that responsibilities remain clearly distributed and manageable.
EU AI Act
The EU AI Act supplements the GDPR with a regulatory framework for AI. It calls for transparency, risk classification, and clear responsibilities. For companies, this does not mean a fundamental restriction, but rather the need to consciously control the use of AI.
Responsibility for classification usually lies with a central governance function, often located between IT, legal, and specialist departments. The decisive factor is not the title, but a clear mandate for control and coordination.
Internal guidelines
Laws only have an effect in everyday life if they are translated into clear internal rules. Good AI guidelines are short, understandable, and practical. They define which tools may be used, which data is permissible, and what AI may be used for.
These guidelines should be developed jointly by specialist departments, IT, and compliance. The respective managers are responsible for their application. This makes it clear that the use of AI is not an individual decision, but part of organizational responsibility.
Conclusion: AI readiness is leadership work
The AI challenge is not the technology. It is about ambiguity, lack of decisions, open responsibilities, and the belief that change can be delegated. Companies that use AI successfully do nothing spectacular. They provide guidance, set clear guidelines, and take their teams seriously.
AI readiness does not just mean having the right tools. It means making decisions. About roles, responsibilities, and collaboration between people and machines. Those who do this leadership work will find that AI is not a risk, but a productivity lever.
FAQ
What does “AI-ready” mean for a team?
An AI-ready team understands AI as a tool to support work and decisions, not as a substitute for responsibility.
Roles, responsibilities, and quality standards are clearly defined, and results from AI systems are consciously reviewed and classified.
Why does AI implementation often fail despite functioning tools?
Because AI changes the logic of decision-making. Without clarity about who evaluates results, bears responsibility, and ensures quality, uncertainty, reluctance, or uncritical use arise—regardless of the technical performance of the tools.
Is tool training sufficient to prepare teams for AI?
No. Tool training teaches functions, but not evaluation skills. The decisive factor is not how a tool is operated, but how AI results are classified in the context of processes, risks, and decisions.
What role does leadership play in the introduction of AI?A central one. Managers provide guidance, define responsibilities, and set guidelines for the use of AI. Without this clarity, AI either remains unused or is adopted uncritically.Why is acceptance within the team so important?
Without acceptance, AI will not be used or will be used incorrectly. Acceptance does not mean agreement at any price, but rather a clear understanding of the benefits, limitations, and responsibilities involved in using AI.
What skills do teams need when working with AI?
Critical thinking, judgment, and process understanding are crucial. Teams must be able to understand why AI suggestions are used or rejected and how they contribute to their own work.
In which areas is AI used effectively today?
Primarily in the automation of routine tasks, the analysis of large amounts of data, and the preparation of decisions. Generative AI is often used for drafting, structuring, and idea generation—the final responsibility remains with humans.
How can the uncritical adoption of AI results be prevented?
Through clear rules, defined quality criteria, and transparent responsibilities. AI provides suggestions, not decisions. This distinction must be anchored in the organization.
What legal framework is relevant for teams?
The GDPR and the EU AI Act are central. They regulate the handling of personal data, transparency requirements, and risk classes. For teams, this means clarity about which data and tools are permissible and who is responsible.
Who is responsible for AI results?
Always humans. Even if AI prepares, analyzes, or formulates, responsibility for the results lies with the human professionals who execute them.
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Jens ist Mitgründer von bakedwith, einer Boutique-Beratung für smarte Automatisierung und KI. Er unterstützt mittelständische Unternehmen und Konzerne dabei, Prozesse zu optimieren, manuelle Arbeit zu reduzieren und durch effiziente Workflows Wachstum zu erzielen. Zuvor war Jens Growth Lead bei OMR und begleitete als Freelancer zahlreiche Unternehmen bei der Optimierung ihrer CRM- und Automationssysteme. Seine Leidenschaft gilt der Verbindung von Wachstum und Effizienz – damit Teams sich auf das Wesentliche konzentrieren können.
