Executive AI Competencies: Practical Guide for Mid-sized Business Leaders 2025

The New Leadership Reality: AI as a Strategic Competitive Factor
AI Competence Gap 2025: Current Data on Leadership Qualifications
The 7 Key Competencies for AI-Competent Leaders
From Concept to Implementation: AI Strategies for Medium-Sized Companies
Practical Examples: How German Mid-Sized Companies Benefit from AI-Competent Leadership
The Three Levels of AI Implementation: People, Process, Technology
Your 90-Day Roadmap: Concrete Steps to AI-Competent Leadership
Frequently Asked Questions (FAQ)

The New Leadership Reality: AI as a Strategic Competitive Factor

The implementation of Artificial Intelligence has evolved from a technological gimmick to a business-critical necessity. According to IBM’s latest “Global AI Adoption Index 2025,” 78% of companies worldwide now have AI applications in productive use – a 35% increase compared to 2023. Yet while large corporations forge ahead with dedicated AI labs and specialized teams, medium-sized companies face unique challenges.

This makes the role of leadership as architects of digital transformation even more crucial. The numbers speak for themselves: A McKinsey study from the first quarter of 2025 demonstrates that companies with AI-competent leaders achieve a 23% higher success rate in implementing AI projects compared to firms where this knowledge is lacking.

For you as a decision-maker in a medium-sized company, this means: AI competence no longer belongs exclusively in the IT department – it’s a core competency for leadership. While large corporations can build specialized teams, as a mid-sized decision-maker, you must develop sufficient AI understanding yourself to set the right strategic course.

This transformation looks different in every company. Thomas, the CEO of a medium-sized machine manufacturer, sees the greatest opportunities in accelerating the proposal creation process. Anna, HR director at a SaaS provider, focuses on AI training for her teams. Markus, IT director of a service group, is working on implementing a company-wide chatbot.

What unites these different approaches: They don’t require technical expertise from management – but they do demand a strategic understanding of AI possibilities, a clear vision, and the ability to lead teams through AI transformation.

“The biggest challenge for leaders in medium-sized businesses is not the technical understanding of AI, but the ability to translate its transformative power into concrete business value.” – Dr. Carsten Bange, Managing Director BARC, 2024

Particularly noteworthy: According to the German AI Monitor 2025 by the digital association Bitkom, there is a direct correlation between leaders’ AI knowledge and the economic success of AI projects. Companies whose leadership team has completed at least basic AI training report a 2.7 times higher ROI on their AI investments.

This article provides you with exactly the competencies you need as a leader to successfully navigate a business world driven by AI – without having to become a programmer or data scientist yourself.

AI Competence Gap 2025: Current Data on Leadership Qualifications

The discrepancy between available AI technologies and the competence level of German executives has widened further since 2023. This is shown by the European Leadership AI Readiness Index 2025, which ranks Germany only 8th out of 27 in European comparison – behind countries like Estonia, Finland, and the Netherlands.

Medium-sized companies in particular face major challenges. The figures illustrate the extent of the competence gap:

Only 23% of executives in medium-sized companies feel adequately qualified to make strategic AI decisions (Source: KfW study “Digital Mittelstand 2025”)
Although 89% of companies surveyed rate AI as “important” or “very important” for their future viability, only 31% have implemented concrete AI training measures for their leadership
The average investment in AI training per executive in medium-sized companies is only €1,250 per year – less than one-tenth of what is spent on technical AI infrastructure

Particularly concerning: A study by the Fraunhofer Institute for Industrial Engineering (IAO) shows that 67% of executives surveyed underestimate the complexity of modern AI systems while simultaneously having unrealistic expectations about their short-term business impact.

These knowledge deficits lead to concrete economic disadvantages. According to a Commerzbank survey of 400 medium-sized companies, over 60% of AI projects failed in 2024 due to lack of leadership competence – not because of technical problems or resource constraints.

From these findings, three particularly critical competence gaps can be identified:

Strategic AI assessment competence: The ability to distinguish real business value from short-lived AI trends
AI change management: Knowledge of how to prepare teams for collaboration with AI
AI governance understanding: The competence to create legal and ethical frameworks for AI deployment

The good news: These competencies can be developed in a targeted manner, without leaders having to become AI experts themselves. There is a clear distinction between the technical AI knowledge that developers need and the strategic AI knowledge that decision-makers require.

Also noteworthy is the development since 2023: While the lack of technical understanding was considered the main obstacle back then, data for 2025 shows that today, the ability to strategically evaluate and organizationally integrate AI is crucial.

Development of AI Leadership Competencies in German Medium-Sized Businesses
Competence Area	2023	2025	Change
Basic Technical AI Understanding	18%	42%	+24%
Strategic AI Assessment Competence	12%	23%	+11%
AI Change Management	8%	16%	+8%
AI Governance Understanding	7%	14%	+7%
Source: German AI Monitor 2025, Bitkom

These figures make it clear: While basic technical understanding has improved, strategic competencies are significantly lagging behind. This is precisely where leaders need to focus to close the gap.

The 7 Key Competencies for AI-Competent Leaders

Based on current studies and our practical experience with over 200 medium-sized companies, seven core competencies have crystallized as crucial for leaders in the context of AI transformation. These competencies form the foundation for successful AI initiatives – without you having to become a programmer yourself.

Strategic AI Understanding: Recognizing Potential and Limitations

A leader with strategic AI understanding can distinguish between genuine innovations and short-lived hypes. They understand the basic functional principles of different AI types and can assess their applicability to their own business processes.

In practice, this means: You don’t need to know exactly how a Large Language Model (LLM) or an image generation algorithm works. But you should understand what types of problems these technologies can solve and – perhaps even more importantly – what they cannot.

“The most important skill is not programming AI yourself, but knowing what to expect from it and what not to expect.” – Thomas Ramge, AI expert and author

A study by the Technical University of Munich among 180 medium-sized companies shows: Leaders with a clear understanding of AI limitations make 34% better investment decisions in the AI field than those who are either overly optimistic or pessimistic.

Specifically, you should master the following aspects:

Distinction between strong and weak AI
Basic understanding of machine learning, neural networks, and LLMs
Awareness of typical problems such as hallucinations in generative AI
Ability to assess the maturity level of AI technologies (research vs. production-ready)

Data Culture and Data Literacy for Decision-Makers

AI without data is like an engine without fuel. Successful AI leaders understand the central importance of data and actively promote a data-oriented corporate culture. They know that the quality of data directly influences the quality of AI results.

The “European Data Literacy Survey 2025” by the European Data Innovation Hub shows: Companies with data-savvy leaders implement successful AI projects three times more frequently than those whose management has little data competence.

As a leader, you don’t need to conduct data analyses yourself, but you should:

Understand core concepts such as structured vs. unstructured data
Know basic quality criteria for training data
Develop a sense for data availability and quality in your own company
Recognize the significance of data silos and promote their elimination

A particularly important aspect: You must be able to distinguish between correlation and causation. AI systems often recognize statistical relationships without understanding the underlying causes. Knowing this limitation protects against poor decisions.

Practical example: The managing director of a medium-sized logistics company recognized that the quality of delivery time predictions was limited by missing traffic data. Instead of investing in more complex algorithms, he ensured the integration of a real-time traffic data service. The result: 28% more accurate predictions with the same AI model.

Establishing AI Ethics and Governance in Your Company

With the EU’s AI Act coming into force in 2024 and the German AI Transparency Act, legal frameworks have emerged that leaders need to know and implement. Beyond this, the ethical dimension of AI decisions is becoming increasingly important for reputation and brand perception.

A consumer survey shows: 73% of customers now prefer companies that are transparent about their AI use. At the same time, 68% of medium-sized companies state they have no clear guidelines for ethical AI use.

As an AI-competent leader, you should:

Have basic knowledge of legal requirements for various AI risk classes
Define ethical guardrails for AI use in your own company
Establish transparency about AI use towards customers and employees
Introduce processes for reviewing AI results for fairness and freedom from discrimination

Particularly important: The occasional “hallucinations” of generative AI (invented facts or sources) pose a significant risk. Leaders must establish control mechanisms that catch such errors before they lead to reputational damage.

An exemplary approach is shown by a medium-sized financial service provider who created an internal “AI Ethics Committee” that includes not only executives but also regular employees. This committee reviews new AI applications not just for legal compliance but also for alignment with company values.

Change Management: Preparing Teams for AI Transformation

The technical implementation of AI is often easier than the cultural one. According to Deloitte’s “AI Adoption Report 2025,” 58% of AI projects fail not because of technology but due to resistance within the team, unclear responsibilities, or lack of acceptance.

AI-competent leaders understand that transformation is a change management process. They must actively address fears and create a climate of openness.

The most important abilities in this context:

Developing a clear vision of how AI will improve (not replace) work
Identifying AI multipliers in the team and promoting them in a targeted manner
Building a progressive training concept that gradually introduces employees to AI
Establishing an “experimentation culture” that provides space for learning and failure

A medium-sized machine manufacturer from Baden-Württemberg developed a three-stage model for this: First, purely assistive AI tools were introduced (such as translation aids), then collaborative systems (AI-supported planning tools), and only in the third step autonomous AI applications. This gradual introduction significantly reduced resistance.

“The most difficult part of AI transformation is not the technology itself, but convincing the people who will be using it.” – Christine Haupt, Management Consultant and Change Expert

Particularly effective: Creating “quick wins” – rapidly implementable AI applications that immediately bring work relief and thus create acceptance.

Prompt Engineering for Effective AI Use in Leadership

While complex programming was necessary to interact with AI a few years ago, today the formulation of precise prompts (instructions) has become the most important interface between humans and AI. The art of “prompt engineering” has established itself as a key competence – especially for leaders.

Research by OpenAI shows that the difference between an average and an excellent prompt can improve the quality of AI output by up to 70%. The economic implications are enormous: Leaders who can formulate precise prompts get better decision-making foundations.

Effective prompt engineering includes:

Knowledge of the CRISPE method (Context, Request, Instructions, Specifics, Persona, Examples)
Understanding of different prompt structures depending on the task type
Ability to translate complex business problems into clear, structured instructions
Evaluation competence for AI outputs to recognize errors or hallucinations

A practical example: The CEO of a medium-sized electronics supplier systematically uses structured prompts for strategic market analysis. Instead of simply asking for “market trends,” he specifies exactly which parameters should be analyzed, what time horizon he is considering, and in what format he needs the results.

This precision not only leads to better results but also saves time – a critical factor for executives. Through optimized prompts, he reduced the time spent on market analyses by 67%.

Important note: Good prompts are not static. With feedback from AI responses, you learn to continuously improve your prompts – a process that successful leaders systematically integrate into their daily work routine.

Hybrid Decision-Making Processes: Combining Human Expertise and AI Recommendations

One of the biggest challenges for leaders is integrating AI recommendations into human decision-making processes. When should you trust AI? When is human judgment superior? How do you optimally combine both?

MIT Sloan Management Review published a study in 2024 showing: Hybrid decision-making processes that combine human and AI intelligence outperform both purely human and fully automated decisions by an average of 31% in quality and speed.

As a leader, you should:

Clearly define which aspects of decisions are delegated to AI and which are not
Understand in which situations AI is particularly prone to errors (e.g., with unusual cases)
Establish a structured process for critically questioning AI recommendations
Create the right feedback mechanisms to learn from wrong decisions

A prime example of hybrid decision-making is provided by a medium-sized insurance company: Routine applications are processed entirely with AI support, complex cases are pre-sorted by AI and provided with recommendations, but decided by humans. Particularly interesting: Regularly, AI decisions are randomly checked by humans – not just for control, but also as training material for the system.

“The future belongs not to pure AI or humans alone, but to leaders who understand how to optimally combine the strengths of both intelligences.” – Erik Brynjolfsson, Professor and Director of the Digital Economy Lab, Stanford University

Particularly important: As a leader, you must create a culture where critical questioning of AI recommendations is understood not as technophobia, but as healthy caution.

Anchoring Continuous AI Learning in Leadership Roles

No other technology is currently evolving as rapidly as Artificial Intelligence. What is state-of-the-art today may already be outdated tomorrow. For leaders, this means: AI competence is not a one-time learning process, but a continuous journey.

According to the Boston Consulting Group’s “Future of Work Report 2025,” relevant AI knowledge for executives doubles every 14 months – significantly faster than in other technological areas.

To stay up-to-date, leaders should:

Block fixed time slots for AI training in their calendar (recommended: at least 2 hours weekly)
Build a network of AI experts inside and outside the company
Establish a systematic process for evaluating new AI tools
Create experimentation zones where new AI applications can be tested risk-free

“Learning by doing” has proven particularly effective. Leaders who actively experiment with AI tools develop a deeper understanding than those who only accumulate theoretical knowledge. A practical approach: Start with personal productivity applications before planning company-wide AI initiatives.

An innovative example is provided by a medium-sized IT service provider from Hamburg that introduced a monthly “AI Friday.” On this day, executives and team members dedicate at least two hours to experimenting with new AI applications – without pressure to succeed, but with structured exchange of experiences.

Not to forget: As a leader, you are a role model. When you actively and visibly use AI tools yourself, it sends a strong signal to your organization.

These seven core competencies form the foundation for AI-competent leadership. You don’t need to become an expert in all areas, but a basic understanding in each of these fields is essential.

The good news: These competencies can be systematically developed – often with less time investment than initially suspected. The following section shows how to move from knowledge to practical implementation.

From Concept to Implementation: AI Strategies for Medium-Sized Companies

AI implementation in medium-sized businesses follows different rules than in large corporations. While large companies often work with broad transformation projects and dedicated AI personnel, medium-sized businesses need a more pragmatic approach.

The “AI in Medium-Sized Businesses” Report 2025 by the German Chamber of Industry and Commerce identifies three main obstacles mentioned by medium-sized companies when introducing AI:

Uncertainty about the right entry point (73%)
Lack of orientation in tool selection (68%)
Unclear expectations about realistic ROI (65%)

A structured 5-step process has proven effective in practice to overcome these obstacles:

Step 1: Conduct AI Potential Analysis

Start with a systematic analysis of your processes. Evaluate them according to three criteria:

Degree of repetition: Processes with high repetitive character are particularly suitable for AI
Data intensity: The more structured data available, the easier the AI implementation
Error susceptibility: Processes where human errors are frequent often offer high AI potential

A practical tool for this analysis is the “Impact-Effort Matrix”: Map potential AI use cases according to expected benefit and implementation effort. Initially focus on high-impact/low-effort candidates.

Step 2: Define AI Pilot Project

Choose a clearly defined, manageable use case for your first AI project. Ideally, it meets the following criteria:

Implementable in 6-8 weeks
Measurable success based on clear KPIs
Visible benefit for the employees involved
Low risk in case of errors

Typical entry projects in medium-sized businesses include:

AI-supported document analysis (e.g., automatic categorization of incoming emails)
Chatbots for standard customer inquiries
Optimization of offer texts through generative AI
Automated quality control for visually recognizable features

Step 3: Organize Team and Resources

Crucial for success: The right composition of the project team. Ensure a mix of:

Domain experts from the affected area (domain-specific knowledge)
At least one person with technical AI understanding
A decision-maker with budget responsibility
Ideally an “AI enthusiast” who acts as an internal ambassador

Experience from successful projects shows: The time requirement is often underestimated. Reserve at least 20% of the team members’ working time for the pilot project. A common mistake is trying to implement AI projects “on the side.”

Step 4: Make a Make-or-Buy Decision

A central strategic question: Do you rely on ready-made AI solutions or develop your own? For most medium-sized businesses, starting with pre-configured AI tools is the more efficient path today.

According to a Commerzbank study, companies that start with ready-made AI solutions save an average of 68% of initial costs compared to in-house developments. Consider the following when selecting tools:

Data protection compliance (especially for cloud-based solutions)
Integration capability with existing IT landscape
Adaptability to specific requirements
Support quality and update frequency

Step 5: Measure and Scale Success

Define clear success metrics in advance. These should include both quantitative aspects (time and cost savings) and qualitative factors (employee satisfaction, error reduction).

After a successful pilot project, structured knowledge transfer is crucial. Systematically document:

Technical insights (What worked, what didn’t?)
Organizational learnings (What resistance was encountered, how was it overcome?)
Economic results (What ROI was actually achieved?)

An example of successful scaling: A medium-sized supplier began with an AI pilot to predict machine failures on a single production line. After measurable success (reduction of unplanned failures by 23%), the system was gradually rolled out to additional lines – with a clear focus on knowledge transfer from early adopters to new users.

“The biggest mistake in AI projects in medium-sized businesses is not the wrong technology choice, but the lack of a structured process from idea to implementation.” – Dr. Judith Meyer, Head of AI Competence Center for SMEs

Particularly successful are companies that develop a systematic “AI roadmap” after the pilot project – a prioritized list of further use cases with clear temporal perspective and resource allocation.

Practical Examples: How German Mid-Sized Companies Benefit from AI-Competent Leadership

The abstract discussion about AI competencies becomes tangible when we look at successful implementation examples. The following case studies from German medium-sized businesses show how leaders with different backgrounds have established AI in their companies.

Case Study 1: Machine Manufacturing Company Optimizes Proposal Creation

Initial Situation: A machine manufacturing company with 140 employees was struggling with long processing times for creating complex proposals. Project managers spent up to 40% of their time compiling technical documentation and adapting texts.

Leadership’s AI Competence: The managing director had no deep technical AI knowledge but a clear understanding of process optimization potential. He recognized that generative AI could revolutionize text creation.

Approach: Instead of starting a complex AI project, the company began with a simple approach: Project managers were trained in effective prompt engineering for ChatGPT-4. In parallel, a database of successful proposals and technical descriptions was built to serve as reference material.

Result: The time for proposal creation decreased by an average of 62%. Particularly noteworthy: The conversion rate increased by 18% as the AI-assisted proposals were more customer-specific and persuasively formulated. The return on investment of the project was achieved after just 2.5 months.

Leadership Competence Success Factor: The managing director focused on the right aspects: He ensured adequate training, clear processes for quality assurance, and transparently communicated that AI should enhance, not replace, employee creativity.

Case Study 2: Financial Service Provider Implements AI-Supported Customer Service

Initial Situation: A financial service provider with 85 employees faced increasing customer inquiries. The service team was overwhelmed, response times increased, and customer satisfaction declined.

Leadership’s AI Competence: The management had basic AI knowledge but also concerns regarding data protection and customer perception. Their focus on change management was particularly strong.

Approach: The company developed a multi-stage strategy:

Implementation of an AI chatbot for standard inquiries
AI-supported pre-qualification of more complex inquiries
Intelligent routing to specialists based on customer history

Particularly innovative: The decision to transparently label the chatbot as an “AI-assisted assistant” rather than disguising it as a human employee.

Result: 68% of standard inquiries are now processed fully automatically. The average response time decreased from 8.5 to 1.2 hours. Customer satisfaction increased by 24 percentage points.

Leadership Competence Success Factor: The systematic involvement of the service team was crucial. Leadership clearly communicated that automation would give employees more time for complex, consultative cases. Additionally, part of the time saved was invested in further training – a signal that AI implementation goes hand in hand with personnel development.

Case Study 3: Medium-Sized Trading Company Uses AI for Inventory Optimization

Initial Situation: A trading company with 210 employees was struggling with inefficiencies in inventory management. Excessive stock levels tied up capital, while at the same time shortages occurred with other products.

Leadership’s AI Competence: The IT manager and logistics manager had jointly further educated themselves in AI fundamentals and recognized the potential of predictive analytics for inventory management.

Approach: The company combined internal sales data with external factors (seasonality, market trends, even weather forecasts) in a machine learning model. Important: Leadership ensured a hybrid decision model – AI provides forecasts and order suggestions, but the final decision lies with the purchasing team.

Result: Inventory levels could be reduced by 23%, while product availability increased by 14%. Part of the freed-up capital (1.4 million euros) was invested in expanding the product range.

Leadership Competence Success Factor: Leadership understood the importance of the data foundation and initially invested in consolidating and cleaning historical sales data. Additionally, a transparent process was established for how AI recommendations are evaluated and, if necessary, overridden. Clear communication that AI serves as a support tool and not a replacement for human expertise was crucial for acceptance.

Common Success Patterns

Analyzing these and other successful AI implementations in medium-sized businesses reveals five recurring success factors:

Focus on concrete business problems: Successful leaders don’t start with technology but with a clearly defined business problem.
Incremental approach: Instead of planning large transformation projects, they begin with small, manageable steps and scale after proven success.
Hybrid decision models: They use AI as decision support, not as a replacement for human judgment.
Transparent communication: They communicate openly about AI’s potential and limitations to employees and customers.
Continuous learning: They treat AI projects as ongoing learning processes, not one-time implementations.

These case studies show: Successful AI implementation in medium-sized businesses depends less on detailed technical knowledge than on leadership’s ability to make the right strategic decisions and shape cultural change.

The Three Levels of AI Implementation: People, Process, Technology

A successful AI transformation must take place simultaneously on three levels. Leaders who consider only one or two of these dimensions often experience implementation problems.

Human Level: Building the Right Competencies in Your Team

According to Deloitte’s “State of AI in Enterprise 2025” report, the human factor is the most common reason for the failure of AI initiatives in medium-sized businesses. Successful leaders address the following aspects:

Skills Mapping and Development

Start with an honest inventory: What AI-relevant competencies already exist in the company? Where are the gaps? A structured skills mapping helps to set training priorities.

In practice, three competence levels have proven effective:

AI users (all employees): Basic understanding and ability to use existing AI tools
AI mediators (selected domain experts): Deeper understanding of possibilities and limitations, ability to formulate requirements for AI systems
AI specialists (few key persons): Technical detailed knowledge for implementation and customization

According to a study by the University of St. Gallen, the optimal ratio in medium-sized businesses is: 80% users, 15% mediators, 5% specialists. Particularly important: Invest primarily in the “mediator” level, which can translate between specialist departments and IT.

Addressing Fears and Resistance

A McKinsey survey of 2,500 employees shows: 72% fear negative impacts of AI on their job – even when objective data points in the opposite direction.

Successful leaders:

Communicate transparently about AI implementation goals from the beginning
Emphasize the supportive (not replacing) role of AI
Show concrete examples of how AI takes over monotonous tasks and creates space for more demanding activities
Actively involve employees in designing new AI-supported workflows

A medium-sized tax consulting firm demonstrated a particularly successful approach: Instead of presenting AI as a cost-reduction or efficiency tool, it was introduced as a “personal assistant” for each employee, helping with routine tasks and creating more time for customer consultation. This framing strategy led to significantly higher acceptance.

Process Level: Optimizing Workflows for AI Integration

A common misconception: Integrating AI into existing, possibly inefficient processes. Experience shows: AI unfolds its potential best when processes are critically evaluated and redesigned beforehand.

Process Analysis and Redesign

Before AI implementation, leaders should ask the following questions:

Which steps in the current process are truly value-adding?
Which decision points could be supported or automated by AI?
How would information flows need to be redesigned to optimally use AI?
Which human control points are still necessary despite automation?

Structured process modeling (e.g., with BPMN 2.0) helps to document current processes and define target processes. Important: Actively involve process participants – they know the weaknesses and undocumented workarounds best.

Optimizing Data Flows

AI systems are only as good as the data they work with. A study by RWTH Aachen University shows: In 63% of medium-sized companies studied, suboptimal data flows were the main cause of disappointing AI results.

Leaders should ensure that:

Data silos between departments and systems are eliminated
Uniform data standards and formats are defined
Data quality is continuously monitored
Feedback mechanisms exist to evaluate and improve AI results

A practical example: A medium-sized wholesaler initially failed with its AI-supported demand forecast. The problem wasn’t the algorithm, but inconsistent product categorizations between the purchasing and sales systems. Only after harmonizing the data structures did the AI deliver usable results.

Hybrid Process Design

In practice, a hybrid approach has proven effective: AI handles rule-based, data-intensive tasks, while humans focus on exceptions, creative aspects, and customer contact.

A successful example is provided by a medium-sized insurer: The AI-supported claims processing handles standard cases fully automatically but transparently escalates to claims handlers when certain criteria are met (unusual claim amount, contradictory information, etc.). The system continuously learns from the claims handlers’ decisions, gradually increasing the degree of automation.

Technological Level: The Right Tools for Your Specific Use Case

Selecting the right AI technology is one of the biggest challenges for leaders without deep technical knowledge. The good news: The AI ecosystem has developed significantly, and there are now numerous solutions that can be implemented without extensive programming.

Make-or-Buy Decision

For most medium-sized companies, a pragmatic approach is recommended:

For generic use cases: Use ready-made SaaS solutions (Software-as-a-Service)
For company-specific use cases: Choose configurable platforms with low-code/no-code approach
Only for truly unique requirements: Consider individual development

An analysis by the Research Institute for Rationalization (FIR) at RWTH Aachen University shows: 74% of AI use cases in medium-sized businesses can be covered by existing solutions or moderate adaptations. Only 26% require truly individual development.

Technology Selection and Evaluation

When selecting AI tools, leaders should consider the following criteria:

Data protection and data security: Especially critical for cloud-based solutions
Integration capability: Connection to existing systems without extensive adjustments
Scalability: Growth potential of the solution with increasing requirements
User experience: Intuitive usability for the intended user group
Total Cost of Ownership: Not just acquisition costs, but also operation and maintenance costs

A proven approach is the “Proof of Concept” (PoC): Test different solutions in a limited but realistic scenario before committing long-term. Ensure that the PoC is conducted with real data and actual users.

Avoiding Technical Debt

A common problem: Short-term implementation decisions lead to long-term “technical debt” – costs that arise from suboptimal technical solutions.

Leaders should therefore:

Ensure modular architectures that can be expanded incrementally
Establish standards and documentation from the beginning
Establish regular technical reviews
Plan resources for continuous improvement, not just for initial development

A medium-sized online retailer demonstrated an exemplary approach: Instead of directly implementing a comprehensive AI solution, they introduced a microservices architecture where individual AI functions (product recommendations, search optimization, customer support) were implemented as separate, interchangeable modules. This allows continuous adjustments without complete redesign.

The integration of these three levels – people, process, and technology – is crucial for success. Leaders must keep all dimensions in view simultaneously and balance resources accordingly.

“The most successful digitalization projects devote 50% of resources to people, 30% to processes, and only 20% to the technology itself.” – Dr. Holger Pfau, digitalization expert and author

Finding and maintaining precisely this balance is the central leadership task in AI transformation.

Your 90-Day Roadmap: Concrete Steps to AI-Competent Leadership

How can you as a leader in a medium-sized business concretely get started now? We have developed a pragmatic 90-day plan that provides you with a structured entry into AI leadership competence – without disrupting your daily business.

Phase 1: Building Foundations (Days 1-30)

Weeks 1-2: Develop personal AI understanding

Register with 2-3 leading AI tools (e.g., ChatGPT, Perplexity, Claude) and experiment for 20 minutes daily
Identify 3-5 routine tasks from your leadership routine that you can accomplish with AI support
Book a half-day executive AI workshop or individual coaching

Weeks 3-4: Status quo analysis in the company

Conduct 5-7 interviews with key personnel from different departments about AI potential
Create an overview of AI tools already in use in the company (often more exist than thought)
Identify AI-affine employees as potential “champions”
Analyze the biggest efficiency bottlenecks in your core processes

Phase 2: Develop Strategy and Team (Days 31-60)

Weeks 5-6: Define AI potential areas

Prioritize 3-5 concrete use cases according to impact-effort ratio
Conduct a one-day workshop for the most promising use case
Define measurable success metrics for each use case
Clarify legal and data protection framework conditions

Weeks 7-8: Organize team and resources

Form a cross-functional AI core team (3-5 people)
Define clear roles and responsibilities
Reserve dedicated time contingents (min. 20% of working time per team member)
Plan an initial training program for the core team

Phase 3: Realize First Success (Days 61-90)

Weeks 9-10: Start pilot project

Start with a clearly defined, manageable use case
Evaluate available tools and make a make-or-buy decision
Develop a prototype or implement a test instance
Define clear test criteria and feedback mechanisms

Weeks 11-13: Evaluate and communicate

Conduct a structured test with real users
Collect quantitative and qualitative feedback
Adjust the solution based on feedback
Document lessons learned and prepare for broader introduction
Transparently communicate successes within the company

Practical Tips for Implementation

Certain approaches have proven particularly effective for each phase:

For personal competence building:

Learning by doing: Experiment with AI tools yourself for your daily tasks
Micro-learning units: Better 15 minutes daily than two hours once a week
Peer learning: Regularly exchange AI experiences with other leaders

For team organization:

Voluntariness: Start with intrinsically motivated team members
Mixed teams: Bring together AI enthusiasts and skeptics
Error tolerance: Establish an “experimentation zone” without pressure to succeed

For the pilot project:

Start small, think big: Begin with a manageable use case that has strategic relevance
Early success experiences: Choose a use case that quickly brings visible improvements
Agile approach: Work in short iterations with regular feedback

“The 90-day plan has proven ideal in our practice: Long enough to achieve real results, short enough not to lose focus.” – Johannes Meyer, AI Transformation Consultant

At the end of 90 days, you will not only have significantly expanded your personal AI competence but also implemented a first concrete use case and gained important experience for the further AI transformation of your company.

The crucial thing is to start concretely now – not with a comprehensive transformation program, but with the first step of your personal AI competence development. Experience shows: Leaders who actively use AI tools themselves are significantly more successful in strategic AI implementation in the company.

Frequently Asked Questions (FAQ)

What AI knowledge do medium-sized business leaders really need?

Leaders in medium-sized businesses primarily need strategic AI competencies, not detailed technical knowledge. These include: a basic understanding of AI functional principles, the ability to assess AI potential for their own business, competence in prompt engineering, basic knowledge of AI governance and ethics, and change management skills for transformation. Unlike data scientists or AI developers, leaders don’t need to know how to program, but they should be able to assess the possibilities and limitations of AI technologies to make informed strategic decisions.

How high are the investment costs for AI entry in medium-sized businesses?

The costs for AI entry in medium-sized businesses are highly use-case dependent but significantly lower than a few years ago. According to current surveys by the Mittelstand-Digital Center, the average initial costs for first AI pilot projects range between €30,000 and €80,000. These consist of costs for tools/software (depending on the use case between €5,000 and €25,000 annually), training and competence building (€10,000-20,000), as well as internal resources/working time. The good news: There are now numerous funding opportunities specifically for AI in medium-sized businesses, such as the “AI-Starthelfer” program of the BMWK, which can cover up to 70% of the costs. What’s crucial for ROI is not the amount of investment, but the right selection of the first use case.

How do we address data protection concerns with AI applications?

Data protection concerns should be proactively addressed as they are legitimate but not an insurmountable obstacle. Specifically, the following measures are recommended: Conduct and document a data protection impact assessment for each AI application. Look for AI providers with European data centers and GDPR-compliant data processing agreements. Many providers now offer “private instances” where your data is not used to train the model. Implement technical precautions such as data preprocessing, where sensitive information is removed before AI processing. A practical approach that has proven effective: Start with use cases that process no or little personal data (e.g., production data, anonymized market analyses) and expand gradually after gaining experience with data protection governance.

What typical mistakes do leaders make when introducing AI?

The five most common mistakes in AI implementation in medium-sized businesses are: 1) Technology-driven rather than problem-oriented approach – many leaders look for applications for AI instead of starting from concrete business problems. 2) Unrealistic expectations about implementation speed and autonomy level of AI – successful projects plan from the beginning with human control and iterative improvement. 3) Neglect of data quality – often more is invested in algorithms than in improving the underlying data. 4) Insufficient change management – the technical implementation succeeds, but acceptance among employees is lacking. 5) “Big bang” approach instead of incremental implementation – successful companies start with small, defined use cases and scale after proven success. These mistakes can be avoided by leaders understanding AI as strategic organizational development, not just a technical project.

How do I identify reputable AI providers and avoid hype-driven poor decisions?

Distinguishing between reputable AI providers and pure hype marketers is a central leadership competence. Pay attention to the following criteria: Reference customers from your industry and similar company size who can cite concrete ROI figures. Transparency about the development status of the technology – reputable providers communicate openly about their solutions’ limitations. Detailed information on data processing and protection instead of vague statements. A realistic implementation plan with clear milestones instead of promises of immediate revolutionary results. Technical documentation and support offerings that go beyond marketing materials. Particularly revealing: Ask for a proof of concept with your own data and realistic use cases. Reputable providers will support this and even advocate for it. Avoid providers who present their technology as a “magic solution” that requires no adaptation or integration.

Which AI application areas offer the fastest ROI for medium-sized companies?

Based on data from the Fraunhofer Institute for Production Technology (IPT) and the German Association for Small and Medium-sized Businesses (BVMW), the following AI applications show the fastest return on investment for medium-sized companies: 1) Automation of document-intensive processes such as proposal creation, contract analysis, or invoice processing (average ROI after 4-6 months), 2) AI-supported quality control in production, especially for visually recognizable features (ROI after 5-7 months), 3) Intelligent inventory optimization and demand forecasting (ROI after 6-9 months), 4) Automation in customer service for standard inquiries (ROI after 7-10 months), 5) Preventive maintenance of machines and equipment through anomaly detection (ROI after 9-12 months). The key to quick ROI lies in identifying use cases with high volume of recurring activities or clearly measurable business impact, as well as the availability of high-quality data.

How do I introduce my employees to AI without creating fears?

A successful introduction of employees to AI is based on transparency, participation, and gradual implementation. Start with open communication about the strategic goals – emphasizing the complementation of human work, not replacement. Early integration of employee representatives in planning processes allows direct addressing of concerns. A three-stage approach has proven practical: 1) “Hands-on AI” – low-threshold workshops where employees experiment with user-friendly AI tools, 2) Identification of personal “pain points” – each employee defines a task that could be facilitated by AI, 3) Implementation of “assistance systems” rather than fully automated solutions – employees retain control and use AI as support. Successful companies also establish a “buddy system” where AI-savvy employees act as mentors for less technology-versed colleagues. The crucial message is: AI is not coming to replace jobs but to make work more valuable and fulfilling.

What AI competencies should I consider when hiring for leadership positions?

When hiring for leadership positions, you should consider the following AI-related competencies: 1) Strategic AI understanding – the ability to recognize and prioritize business potential of AI, 2) Data competence – a basic understanding of data quality, structures, and management, 3) Adaptability and willingness to learn – more important than specific tool knowledge given the rapid development, 4) Change management experience with digitalization projects, 5) Critical thinking and judgment when evaluating AI solutions and providers. Focus less on technical detail knowledge (which quickly becomes outdated) and more on the combination of business understanding and technological openness. Practical experience with implementing data-driven projects is more meaningful than theoretical AI knowledge. According to a McKinsey study from 2025, leaders with “combinatorial abilities” – who can bridge technology and business value – are particularly successful in AI transformation.