Artificial Intelligence (AI) is seen as a key technology for the future. But in many organizations, the reality looks very different: According to a study by the RAND Corporation (2024), more than 80% of AI projects fail—twice as many as traditional IT initiatives.
The reasons are varied: there is a lack of concrete use cases, management does not support the project, or employees are not properly trained in working with AI. Yet in practice, it becomes clear: those who create the right conditions for AI can generate real value.
That’s exactly what this article is about. You’ll learn:
- Which strategic, technological, and organizational foundations are critical for AI success
- Why data quality and an open company culture make the difference
- How to start small with real-world use cases—and grow from there
Content
- Concrete use cases – the first step to success
- Access to data for AI – the foundation for successful use cases
- Technological infrastructure – the technical backbone of AI
- Skills and culture – AI needs people who are on board
- Leadership and monitoring – AI needs clear ownership
- Iterative approach and piloting – start small, think big
1. Concrete use cases – the first step to success
Many companies launch AI projects without a clear idea of what the system should actually achieve. This often leads to frustration, stagnation, or results that miss the real need. A better approach: define early on which specific use case you want to address with AI—and what outcomes you expect.
Start with a concrete process or problem from your day-to-day operations. The key questions are: What goes into the system—and what should come out? Describe typical workflows using at least five examples.
Also take time to identify at least five rare or ambiguous edge cases—and how the AI should handle them. These scenarios help set realistic expectations and clearly define system boundaries.
Business departments are especially important in this step. They know the everyday workflows—and the exceptions. Use this expertise to work with your IT team to build an AI system that delivers real value and works in practice.
Our tip: For every use case, take the time to document both typical workflows and edge cases. This gives you a solid foundation for development—and a measurable basis for evaluating success later on.
2. Access to data for AI – the foundation for successful use cases
Artificial Intelligence only works with the right data. To successfully implement a use case, the following steps are essential:
- Identify data needs: Analyze which information your specific use case requires—and in which systems that data currently resides.
- Ensure access: Make sure the AI can securely access this data—for example via APIs or controlled data exports.
- Prepare the data: Structure, clean, and check the data for GDPR compliance. At this stage, it’s wise to involve external experts to reliably meet both legal and technical requirements.
- Clarify responsibilities: Who is accountable when AI-based decisions are made?
- Create transparency: Users and customers should be able to understand how and why the system delivers certain results.
Our tip: Build data literacy across your organization—not just in IT. Those who understand data can better unlock its full potential.
3. Technological infrastructure – the technical backbone of AI
One of the most important prerequisites for AI is a stable, flexible, and scalable technical foundation. Many companies only realize during a project that their existing IT infrastructure quickly reaches its limits. That’s why it’s worth asking early on: Is your IT ready for AI?
What matters:
- Scalable systems: Cloud platforms provide the computing power AI needs and can scale as your projects grow.
- Data integration: Systems must communicate smoothly with one another—seamless data flow is essential.
- Tool landscape: Use platforms that integrate easily with your existing IT setup—for data analysis, model training, or deployment.
- Don’t forget security: The more systems are connected, the more critical IT security becomes. Think encryption, access controls, and regular audits.
Our tip: Start with an infrastructure check. Identify bottlenecks before they slow down your projects. That way, you ensure your tech stack can keep up with your AI ambitions.
4. Competences and Change Management – AI needs people who are on board
Technology alone doesn’t drive progress—people do. Artificial Intelligence changes workflows, decision-making, and sometimes entire job profiles. For this transformation to succeed, two elements are essential: the right skills and an active change management approach that brings employees along.
Key skills for a successful AI journey:
- Data & AI literacy: One often underestimated aspect: AI doesn’t think like humans. That’s why employees should receive training to learn how to use and interpret AI systems effectively. It’s similar to learning a new piece of software—but with a much greater impact on daily work.
- Technical expertise: Involve data and AI experts who can evaluate what’s feasible—and what a realistic outcome of your AI implementation could look like.
- Process knowledge from the business side: Those working with processes every day best understand where AI can make a difference.
But knowledge alone isn’t enough. Mindset, communication, and leadership often determine whether AI projects gain momentum—or get stuck in resistance.
Cultural and organizational prerequisites:
- Foster openness to change: Make it clear early on that AI is here to support people—not replace them. Leadership must actively carry and communicate this message.
- Practice transparency: Communicate openly about plans, goals, and the specific benefits for employees.
- Actively demonstrate management support: At least one leader should visibly advocate for the topic and take a clear stand. This is the only way AI gains the strategic backing it needs (see Leadership & Monitoring).
- Start small, learn together: Begin intentionally with small automations or assistive AI systems. This keeps risks manageable and gives your team a safe space to gain experience. Just as important: share your learnings across the organization. This builds acceptance—and sparks curiosity for what’s next.
Apply change management strategically:
- Involve people early: Don’t wait until go-live—engage employees from the start.
- Take emotional reactions seriously: New technologies can create uncertainty. Offer space for questions and feedback.
- Align training with communication: Technical learning is important—but just as crucial is ongoing, open communication that explains the “why.”
Our tip: Make change management a core part of your AI strategy. Those who actively support change build trust, reduce resistance—and turn employees into allies for the future.
5. AI Leadership – AI needs clear ownership
Artificial Intelligence only reaches its full potential when there is clear accountability and real commitment from leadership. Many AI initiatives don’t fail because of technology—but because no one in the organization truly takes ownership.
That’s why AI needs clear leadership—both technically and culturally. At least one person in top management should actively champion the topic, track progress, and visibly communicate why AI belongs on the company’s agenda. This creates orientation—and sends a clear message: this is a leadership priority.
At the same time, organizations should set clear expectations and boundaries right from the start. That includes continuously monitoring the use of AI—not just technically, but also in terms of fairness, transparency, and real-world impact. This is the only way to catch issues early and build lasting trust.
Our tip: Make leadership visible—and turn monitoring into a habit. Those who take responsibility, communicate clearly, and regularly review outcomes build trust. That’s how early AI efforts grow into long-term success.
6. Iterative approach and piloting – start small, think big
Many AI projects don’t fail because of technology—but because of unrealistic expectations or rigid planning. A better way: start small, learn quickly, and improve continuously.
An agile, iterative approach helps minimize risks and make success visible early on.
How to start the right way:
- Set up pilot projects: Choose a manageable use case with clearly measurable value.
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Test quickly: Build a prototype and focus on input and output quality, drop ideas that don't deliver quality yet.plan
- Evaluate results: Measure the impact, learn from mistakes, and optimize step by step.
The benefit: You gain early hands-on experience, build internal support—and avoid costly missteps.
Key factors for successful scaling:
- Plan the transition early: A pilot should cover all core functionalities that a full, production version of the system has.
- Share knowledge: Document learnings and best practices so other teams can benefit, too.
- Involve stakeholders: Bring all relevant departments on board—this is how a small pilot becomes a company-wide AI program.
Our tip: Don’t fix your AI plans for the next three years. Instead, create a flexible framework—and adapt your strategy as you go. That way, your organization stays agile and ready to innovate.
Use Case: Self-checking AI optimizes knowledge management in the energy sector
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Conclusion: AI isn’t a self-starter – but it’s a real opportunity
Artificial Intelligence can speed up processes, enable smarter decisions, and open the door to entirely new business models. But without the right foundations, no AI system will deliver meaningful results.
It takes more than just technology. Clear goals, clean data, the right infrastructure, and a committed team—all of these factors determine whether AI works in practice or ends up abandoned in a drawer.
The good news: Most of these foundations can be built step by step. Those who start small, test early, and keep learning lay the groundwork for real, long-term value.
Start with a realistic check: Where does your organization stand today? And what’s the next achievable step? We’re happy to support you in taking the first steps—and developing sustainable solutions together.
Simply schedule a no-obligation initial consultation, and we will assess how we can assist you.
