Introducing AI in the Company – Yes, But Where to Start? Answers for Business Leaders

Key Takeaways

• AI now determines how competitive companies are.
• Only those who build knowledge and create transparency can foster acceptance and motivation.
• With an AI Innovation Group, concrete use cases, and open formats, Evolit has strengthened competence and trust internally.
• Define clear goals, promote knowledge, start small experiments, communicate transparently, and develop competencies in the long term.

Artificial intelligence is no longer an abstract future technology. It is already shaping processes, products, and customer experiences across almost all industries. From automating administrative tasks to data-driven decision support, the smart use of AI determines how competitive companies will be tomorrow.

Many business leaders are therefore no longer asking whether they should use AI – but how they can use AI effectively within their company. An ill-considered introduction of AI technologies can quickly lead to skepticism, resistance, or disappointed expectations.

Why Trust Is the Key to Success

A central challenge lies less in the technology itself and more in building trust. But trust does not arise automatically. In Austria, according to Great Place to Work, only 33% of employees show interest in using AI to improve their work.

Trust requires understanding – and that only emerges through company-wide knowledge across all roles. In addition to process transparency, clarity on ethical guidelines, data protection, and accountability is crucial – not only to meet regulatory requirements but also to sustainably strengthen employee trust.

Our path to achieving this: systematic knowledge transfer, internal experiments, transparent processes, and extensive team exchange.

Before We Use AI for Others, We Must Understand It Ourselves

Our goal was to embed AI expertise not only within individual teams but across the entire company. To achieve this, we combined several measures:

AI Innovation Group

We launched a company-wide initiative and created a platform for exchange, where current developments, application examples, and best practices are presented, and consistency checks are discussed. This created a shared understanding.

Develop Concrete Use Cases Within Teams

To make AI tangible, specific applications for different areas were developed:

• Requirements Engineering: Automated impact analyses, quality checks, and support in structuring requirements.
• Software Testing: Automated test case generation and optimization of test coverage.
• Software Development: Support with recurring tasks, suggestions for code optimizations.
• UX & Design: Prototype generation and more efficient evaluation of usability tests.
• AI Agents & Custom Software: Integration into existing processes to increase productivity.

Knowledge Transfer and Transparency

Through regular workshops, internal presentations, and open discussion rounds, we were able to reduce skepticism and foster a shared understanding. Only when all stakeholders recognize the added value and develop trust can AI truly become effective in daily work.

How Companies Can Shape Their AI Adoption

We already have several years of experience with machine learning and AI, during which we have implemented numerous processes. Based on this experience, we recommend that companies take the following steps:

Define Clear Business Objectives

Instead of starting with individual tools or pilot projects, leaders should first define their company’s strategic goals: Where can AI deliver the greatest value? Which processes should be improved? What measurable results should be achieved?

Build Knowledge and Reduce Fears

Training, internal knowledge formats, and sharing best practices help promote understanding. A good approach is to set up internal AI communities or innovation groups that make technological fundamentals and potential use cases transparent.

Enable Experimentation

Small, low-risk pilot projects help gather initial experience without requiring large upfront investments. These experiments can start within individual departments and then be gradually expanded.

Create Transparent Processes

Openly communicating how AI systems make decisions builds confidence. At the same time, it is important to clearly define roles and responsibilities to avoid misunderstandings.

Develop Competencies Sustainably

AI is not a one-off project but a continuous learning process. Leaders should create conditions that encourage ongoing training, experimentation, and interdisciplinary collaboration.

Start Now – With Structure and Responsibility

The best time to engage with AI is now. Companies that invest early in knowledge, trust, and experimentation lay the foundation for sustainable success.

AI only realizes its potential where technology, expertise, and acceptance go hand in hand. With a clear vision and structured approach, the entry can succeed – and make a real difference.