The right way to get started with AI: start small, demonstrate impact, move forward step by step

When companies talk about using artificial intelligence, the image of a major transformation quickly emerges: processes need to be rethought, systems connected with one another, and as many areas as possible involved in the new way of working.

And this first thought is absolutely correct.

Anyone who wants to use AI successfully in their company in the long term will need to change processes, connect systems, structure knowledge and establish new ways of working. The necessary steps must therefore be taken.

But not all at once.

A successful entry into AI does not have to start at 100 percent right away. On the contrary: Small, well-chosen steps are often the better way to implement the necessary transformation in a controlled, transparent manner and with visible successes.

The goal may be big. The path to get there should be taken step by step.

What matters, therefore, is not how big the first AI project is. What matters is that it works, quickly delivers a recognizable benefit, and is accepted by the people involved.

The first step must fit the company

There is no single perfect entry point into AI that works equally well for every company.

Which application is suitable as a first step depends, among other things, on the size of the company, its organization, existing processes, the existing IT landscape, employees, and the knowledge already available.

Nevertheless, the introduction should meet some basic requirements:

It should be manageable, quick to implement and have a positive impact on as many stakeholders as possible.

Because the initial experiences with AI in particular shape the company's further development.

When employees experience that AI takes work off their hands, makes information available more quickly, or simplifies recurring tasks, acceptance emerges. At the same time, initial productivity gains can be made visible and concrete experience can be gained.

Involve employees from the very beginning

The introduction of AI is not solely a technical task.

It is always also a process of change.

Employees need to understand why AI is being introduced, how it is intended to be used, and what specific benefits it offers in their day-to-day work. Uncertainties and fears should be taken seriously and reduced through transparency, training, and practical experience.

That is precisely why a step-by-step approach is important.

A small, easy-to-understand use case enables employees to gain their own experience with AI. An abstract technology becomes a concrete tool that supports everyday work.

The effect of the initial applications should be made visible.

• How much time was saved?
• Which tasks have become easier?
• How quickly can information be found?
• Which errors can be avoided?
• Where is the quality of work improving?

Results like these make the benefits of AI tangible.

And this is precisely what forms the basis for the next steps.

The necessary steps are coming – just gradually

Proceeding step by step does not mean postponing necessary changes or losing sight of the bigger goal.

Quite the opposite.

The individual steps build on one another and gradually lead to the comprehensive transformation necessary for successful AI deployment.

Initially, perhaps a single subprocess is supported. Then the next process follows. Existing solutions are expanded, additional data sources are connected, and more employees are involved.

With increasing experience, speed can also increase significantly.

Therefore, it does not have to remain one process at a time permanently. As soon as experience, technical foundations, and acceptance are in place, several processes can also be considered and further developed simultaneously.

The further a company has progressed along this path, the faster AI can be extended to additional business areas.

The goal therefore remains the comprehensive use of AI within the company.

The difference lies in the path:

Do not change everything at once; instead, implement the necessary changes in a sensible order.

What is suitable as a first AI use case?

The best starting point can look completely different from one company to another.

However, it is often relatively small applications that quickly produce a noticeable effect.

For example:

• Meeting assistants that document conversations and summarize tasks
• AI-powered conversation matching and analyses
• Enterprise GPTs for internal information and inquiries
• automated or AI-assisted invoice checks
• Training on the safe and productive use of AI
• internal chatbots for knowledge sharing
• Assistants to support recurring administrative processes

The crucial point is not the technology itself.

A good initial use case should ideally help to capture knowledge, make it usable, and continuously improve the company's knowledge base.

Because this is precisely where a particularly great leverage effect is created in the long term.

Individual applications create a knowledge base

With every meaningfully integrated process, additional company knowledge can be structured.

Insights from meetings can be retained. Information from conversations can be analyzed. Documents can be found more easily and linked to one another. Employees’ questions can reveal which knowledge is missing or should be documented better.

This creates an increasingly valuable knowledge base step by step.

And this knowledge base, in turn, improves future AI applications.

From a single assistant, an intelligent corporate structure can thus emerge in the long term, in which knowledge is not only stored but actively used.

Create value first, then scale

Companies don't have to start with the largest AI solution.

You should start with the right first step.

The long-term goal can certainly be comprehensive. Processes will need to change. Systems will need to work together more closely. Knowledge must be structured and made available. Employees must develop new ways of working.

All of these steps are right and necessary – they just do not have to happen at the same time.

A manageable use case, a visible benefit, and employees who gain positive experience are often the better foundation for further development.

After that, we continue consistently:

First use case. Initial experience. More knowledge. Next process. More speed.

With each step, expertise, acceptance, and the ability to transition additional areas more quickly into the company’s AI structure increase.

Getting started with AI is therefore less a single large project than the beginning of continuous development.

The goal can be 100 percent from the outset. The path to getting there happens gradually.