Using AI Effectively in Business: These Fundamentals Must Be in Place

Many companies are starting to realize that they should be exploring AI. The possibilities seem vast. The examples sound exciting. Initial ideas are emerging internally. Perhaps some initial tests are already underway. Maybe your team has already experimented with ChatGPT, Copilot, or other tools.

And yet, the feeling that many SMEs are currently experiencing often lingers:

• Where should we even start?
• Which AI application really makes sense for us?
• Are our processes and data even ready for this?
• What brings real value, and what is just knee-jerk action?
• How do we avoid investing time and money in something that won’t work properly in the end?

In companies, AI rarely fails because of the technology itself. More often than not, another foundation is missing: a clear use case, usable data, defined responsibilities, suitable processes, or meaningful integration into daily operations. We don’t view AI simply as a tool, but as an interplay of business case, data, software, integration, governance, and practical implementation in everyday operations.

• What Foundations Do Companies Really Need for Implementing AI?
• Why many AI projects fail
• Which processes are suitable for AI and which are not
• How to identify meaningful AI use cases
• What matters when it comes to data, systems, security, and responsibility
• What a realistic and controlled introduction might look like

AI is useful in situations where a lot of time is currently spent on tasks that are repetitive, require a lot of reading or searching, or involve processing large amounts of information.

Three examples of how this works in practice for SMEs:

Pre-sorting incoming inquiries
A company receives dozens of emails and inquiries every day (from customers, suppliers, and internally). Currently, someone reads through everything and forwards it. AI can automatically categorize, prioritize, and assign inquiries to the right team. What used to take 45 minutes a day now takes seconds.

Making knowledge from documents accessible
Technical documentation, contracts, internal guidelines: the knowledge is there, but buried. Employees spend a long time searching or keep asking the same colleagues. An AI-powered search finds the relevant information directly, even if it’s buried in an 80-page PDF.

Preparing reports and analyses
Gathering, processing, and commenting on the same data every month.AI can structure raw data, flag anomalies, and deliver a first draft that a human then reviews and approves.

Important: These are not promises, but possibilities. Whether such an application works for you depends on your processes, data, and systems. That is exactly what this page is about.

When companies want to get started with AI, they shouldn’t ask which tool to use first. The more important question is: What foundation must be in place for AI to be used effectively, securely, and productively in our company?

1. A clear problem and a specific goal

AI should never be implemented simply because it’s a hot topic right now. The starting point must always be a specific problem.

For example:

• a high level of manual effort for recurring tasks
• long search times because knowledge is scattered across many systems and documents
• large volumes of text, documents, or requests that need to be organized
• Media breaks where information must be manually transferred or verified
• Bottlenecks that cost teams time and slow down processes

Only once it is clear which problem needs to be solved can one assess whether AI is even the right solution.

2. Processes that are clear enough

AI can only meaningfully support a process if it is even possible to understand how that process works today. In many companies, this is precisely where the problem lies: The workflow has evolvedover the years, individual steps are handled differently depending on the person, and important decisions are made based on experience rather than clear rules.

Before AI can help, it should therefore be clear:

• what information is actually available at the start of a process
• which step should normally follow
• how you recognize a useful result in everyday practice
• at which points manual checks, additions, or decisions are currently made
• where exceptions, special cases, or follow-up questions regularly occur

AI does not organize unclear processes on its own. It works well where workflows, decision criteria, and expectations are at least tangible enough to form a meaningful use case.

3. Data that is truly usable for AI

AI doesn’t just need data. It needs information thatisavailable, understandable, and usable in the right context. This is often where things fall short in practice sooner than expected.

What matters isn’t just whether data is available, but above all:

• in which systems, files, or tools the relevant information is currently stored
• whether this information is complete and up-to-date enough
• whether content is clearly structured, named, and discoverable
• whether data from different sources is even compatible
• whether there are gaps, duplicates, or contradictions in the information
• whether this data may even be used for AI applications

Many companies quickly realize here: The real hurdle is not AI itself, but how they handle their own information. When data is scattered across various tools, folders, Excel files, documents, or specialized systems, a good idea quickly turns into a time-consuming data cleanup project.

4. Systems and interfaces that work together

AI only delivers real value within a company if it can be integrated into existing processes.

This often means:

• Information must be consolidated from multiple systems
• Documents, databases, or specialized applications must be accessible
• Results must feed back into the process
• Employees must be able to work where they already work

If AI exists only as an isolated, standalone solution separate from day-to-day operations, it often remains interesting but not truly effective.

This is precisely why system integration is a central part of any serious AI strategy. After all, the value does not come from the model alone, but from the interplay of processes, data, user interfaces, and the system landscape.

5. Responsibility and clear decision-making paths

An AI project needs more than just interest and an initial use case. It requires clear accountability within the team. Otherwise, after the initial tests, the very factors that will later determine success or failure remain unresolved.

Before the project begins, you should define

• who is technically responsible for determining what constitutes a “usable result”
• who reviews the results and intervenes in case of deviations
• who approves changes to prompts, logic, or models
• who is responsible for operations, monitoring, and further development
• who decides on data protection, usage limits, and approvals

This is particularly important when it comes to AI. Many results are not simply unambiguously right or wrong. In practice, we often see the same disconnect here: business units expect benefits, IT handles the technical implementation, but no one takes binding responsibility for quality during ongoing use. Then a pilot project may work technically, but it never becomes a reliable tool internally.

6. Security, Data Protection, and Internal Rules

As soon as AI works with internal information, the question of security and data protection arises.

Important points include, for example:

• What data is permitted to be processed?
• What content is sensitive?
• Where is data stored or transmitted?
• What internal guidelines apply?
• What approvals are required?
• What needs to be documented or restricted?

These questions should not be addressed only after a pilot is already underway. They need to be addressed at the outset because they directly influence architecture, tool selection , and usage limits.

7. Team acceptance and effective user guidance

Even the best AI solution is of little use if it isn’t adopted in everyday work. This happens faster than many people think.

Employees need to understand:

• What is the solution for?
• What can it do, and what can’t it do?
• When and where is it helpful for me?
• Who reviews the results?
• How does it fit into my daily work routine?

Employees should not get the impression that AI is simply meant to replace their knowledge, their responsibility, or even their job. AI is a tool that can provide support and reduce workload. However, the professional interpretation, responsibility for results, and decision-making remain with humans. Once this is clear internally, acceptance increases significantly.

Not every task is automatically a good use case for AI. A simple rule of thumb is that AI is particularly useful when it identifies patterns in large amounts of data or complex content, reveals connections, or processes information that would be very difficult to handle using traditional software.

Many companies aren’t looking for a theoretical assessment, but for honest guidance. Our honest answer is almost always: to some extent. Most companies have good approaches, but also areas that need work. What matters isn’t whether everything is perfect, but whether you know where you stand.

Three questions that quickly show how far along you are:

Can you describe the problem in one sentence?

“We want to use AI” is not a problem; it’s a wish. “Our staff spend two hours every day manually classifying incoming documents.” That is a problem. Anyone who can clearly articulate this has already taken the most important step.

Would two employees describe the same process in the same way?

If not, the process isn’t clear enough for AI yet. That doesn’t mean AI isn’t possible, but it shows where clarity needs to be established first.

Do you know where the relevant data is located and who is authorized to access it?

If the answer is “somewhere in various systems,” that’s not a deal-breaker. But it’s a clear indication that data structure and access rights must be clarified before AI can be deployed.

If you can clearly answer all three questions, the groundwork is laid. If not, then that’s the starting point.