Introducing AI in companies: How to implement it successfully in SMEs

Why many AI projects fail and how things can be done better

Before we talk about the right approach, it's worth taking a look at the most common pitfalls. Because AI initiatives often fail not because of technology, but because of avoidable mistakes.

AI as a pure IT project

The specialist department defines a wish, IT implements it, and in the end, no one uses the result. Implementing AI is not just a technology issue. It affects business processes, working methods and, ultimately, corporate culture.

Too large the project

Anyone who tries to immediately introduce a company-wide AI system is often faced with major challenges. Successful AI deployments start small. As soon as these deliver results, you can build on them.

Poor data quality

Loud Federal Statistical Office 45 percent of companies cite inadequate data quality and availability as an obstacle to the use of AI. If you want to introduce AI, you must first create a solid database.

‍

Step 1: Find the right first project

The most important step in implementing AI is the one that has the least to do with technology: choosing the right start-up project. A good first AI project meets three criteria.

Clearly measurable goals: This can include saving time, reducing errors or reducing costs. It is important that success is objectively verifiable, not just felt.

Manageable complexity: That means no company-wide processes, no dependencies on dozens of AI systems, no politically charged topics. The fewer participants and interfaces, the higher the probability of success.

A dedicated project manager from the department: AI projects that are only driven by IT often remain proof-of-concepts without real value. It takes someone from the business who really wants the result and uses it later on.

Specifically, areas where repetitive, data-based tasks are required to get started:

• Categorization of customer inquiries
• Extracting information from documents
• Preparation of standard texts
• Analyzing sales data

An often mentioned goal when using AI is to relieve routine work. In which areas AI is already being used successfully, our article on Using AI in companies.

‍

Step 2: Clarify the technical requirements

Once the pilot project has been defined, it is time for technical implementation. Many applications no longer require a complex in-house infrastructure. Cloud-based AI technologies make it much easier to get started than just a few years ago.

Nevertheless, a number of basic questions need to be clarified:

• Which data is required and in what quality is it available?
• Where is this data stored and how can it be made accessible to the AI application?
• What are the data protection and data security requirements?

The question of cloud vs. on-premise is particularly relevant in medium-sized companies. Cloud solutions such as Azure, AWS or Google Cloud offer quick start and low initial investments. In return, data leaves the company, which can be problematic depending on the industry and type of data. On-premise solutions keep the data in-house, but require more technical expertise and higher investments.

The following applies to getting started: Don't plan too complex. If a standard tool such as Microsoft Copilot, ChatGPT Enterprise or an industry-specific SaaS solution covers the use case, this is often the faster route than in-house development. The benefits are obvious: lower costs and faster results. Tailored AI solutions become relevant when standard tools reach their limits. If you want to delve deeper into the various AI providers, you will find in the article on AI market 2025 a comprehensive overview.

On the SME Stage of the data:unplugged Festival 2026, practitioners will be showing how SMEs are actually overcoming the technical hurdles when introducing AI. There, companies report on their experiences with various infrastructure approaches.

‍

Step 3: Involve employees right from the start

Especially at the beginning, the greatest potential in introducing AI lies in the people who use it. Success depends largely on how well employees are involved and involved right from the start.

Open and transparent communication is crucial: Why do we use AI? What benefits does it bring to work and employees? Fears of change should be taken seriously and actively addressed.

Successful companies involve employees from specialist departments early on in project planning. They know the processes best and can provide valuable information on where AI can provide real benefits.

At the same time, it is important to strengthen the competencies of employees. This is not about in-depth technical knowledge, but about a basic understanding of how AI works, how to use it sensibly and how to correctly evaluate results. Internal contacts can act as multipliers, answer questions and promote acceptance.

‍

Step 4: Implement and learn the pilot project

Now it's getting concrete. The pilot project is being implemented. Realistic expectations are important here. A pilot project isn't proof that AI works across the organization. It is an experiment from which we learn and which forms the basis for further steps.

Implementation should be iterative. This means not developing silently for months and then presenting, but working in short cycles, getting feedback early and making improvements. An MVP, minimum viable product, i.e. a minimally functional version, can often show after just a few weeks whether the approach works.

It is crucial to define clear success criteria before the start. What exactly are we measuring? When is the pilot project considered successful? Clarifying these goals in advance prevents subsequent discussions as to whether the project worked or not.

‍

Step 5: Scale but wisely

Once the pilot project has worked, there is the question of scaling. How do we get the AI solution out there? How do we transfer experience to other areas of the organization?

A successful pilot project shows the potential of the solution, but for a company-wide application, it is important to carefully review the framework conditions. Different data sources, processes or user groups can have an impact on success, which is why a careful and well-planned approach is crucial.

A step-by-step approach has proven effective:

1. From pilot to extended pilot group
2. Then to the selected area
3. Finally, on to the broad introduction

In each phase, it is checked what works and what needs to be adjusted.

Governance should also grow in parallel with scaling. Who is responsible for the AI application? How is it ensured that it is maintained and developed over the long term? What are the requirements for dealing with AI-generated results? These aspects are becoming increasingly important as they become more widespread. Anyone who would like to deal with the strategic foundations right now can find in the article on AI strategy for SMEs further orientation.

‍

The importance of management: AI needs clear support from above

Implementing AI successfully isn't a task that you can simply delegate and then forget about. It requires active engagement on the part of managers — not only by providing resources, but also by showing visible interest.

Executives should be familiar with AI tools themselves, talk openly about progress and challenges, and show that digital transformation is a strategic concern of the entire company. If senior management views AI as just a temporary trend, this attitude will quickly be felt throughout the company.

At the same time, it is important to communicate realistic expectations. AI is a powerful tool with great potential that, when used correctly, can create real added value — but it also has its limits. Leaders who understand and communicate this balance create the foundation for sustainable use of AI.

‍

Avoid typical mistakes when implementing AI

Finally, here are the most common mistakes that happen when introducing AI and how to avoid them.

The technology focus

AI projects rarely fail due to technology. Rather, clear goals, early involvement of employees and good data quality are decisive for success. Those who focus on the actual problem and set the appropriate priorities create the best conditions for successful AI implementation.

Expectations

Artificial intelligence is often associated with high expectations. It should work quickly, deliver impressive results and integrate seemingly effortlessly. However, reality shows that AI projects require time, iterative improvements, and continuous learning — which ultimately leads to sustainable success and valuable innovations.

The Silo

When IT regards AI as its topic and leaves specialist departments out, solutions are created without real business benefits. Successful AI implementation works best across disciplines: It combines technical know-how and technical expertise and thus creates real added value for the company.

The lack of a data strategy

AI can't work without high-quality, accessible data. Anyone who gets the database in order at an early stage creates the best basis for successful and effective AI implementation.

‍

Conclusion: The best time to start

The implementation of AI in SMEs is not a question of if, but of when and how. The technology is sophisticated enough, barriers to entry are low, and competition is increasing.

A structured approach is crucial: Select a manageable pilot project, involve employees from the start, learn from initial experiences and gradually scale the solution. A pragmatic start makes it possible to gain insights at an early stage and to continuously improve implementation.

You can find out how other SMEs have successfully introduced AI at the data:unplugged festival 2026 on March 26 & 27 in Münster. In master classes, experienced practitioners show how to implement AI in companies. At the Mittelstands Stage, specific use cases from a wide range of industries and departments are presented and, in specific network formats, you can learn from the experiences of others.

AI implementation affects all areas of the company. For effective implementation, it is crucial to involve key people in your company, train them and positively prepare them for deployment. data:unplugged stands for a broad and well-founded transfer of knowledge from which the entire business team benefits. Get a ticket for yourself and your core team now!