The right way to get started with AI: start small, demonstrate impact, and 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, and as many areas as possible involved in the new way of working.

And this initial thought is absolutely correct.

Companies that want to use AI successfully 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, understandable manner and with visible successes.

The goal may be ambitious. The path to it should be taken step by step.

What matters, therefore, is not how large 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 way to get started with 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 current IT landscape, employees, and the knowledge already available.

Nevertheless, getting started should meet some fundamental requirements:

It should be manageable, capable of being implemented quickly, and generate a positive impact for as many stakeholders as possible.

After all, initial experiences with AI in particular shape the company's further development.

When employees experience AI taking work off their hands, making information available more quickly, or simplifying recurring tasks, acceptance grows. At the same time, initial productivity gains can be made visible and concrete experience can be gained.

Engage employees from the outset

Introducing 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. Uncertainty and concerns should be taken seriously and reduced through transparency, training, and practical experience.

This 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 practical tool that supports day-to-day work.

The impact 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?

Such results make the benefits of AI tangible.

And this is precisely what creates the foundation for the next steps.

The necessary steps will come – just gradually

Taking a step-by-step approach does not mean postponing necessary changes or losing sight of the larger goal.

On the contrary.

The individual steps build on one another and gradually lead to the comprehensive change required for successful use of AI.

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

As experience grows, the pace can also increase significantly.

Therefore, it does not have to remain one process after another forever. Once experience, technical foundations, and acceptance are in place, several processes can also be considered and further developed simultaneously.

The further a company progresses along this path, the more quickly AI can be extended to additional areas of the business.

The goal remains the comprehensive use of AI throughout the company.

The difference lies in the approach:

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

What makes a suitable first AI use case?

The best way to get started can look completely different from one company to another.

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

For example:

• Meeting assistants that document conversations and summarize tasks
• AI-powered conversation comparison and analysis
• Company GPTs for internal information and queries
• automated or AI-supported invoice reviews
• Training on the safe and productive use of AI
• internal chatbots for knowledge sharing
• Assistants to support recurring administrative processes

The key point is not the technology itself.

A good first use case should, as far as possible, help capture knowledge, make it usable, and continuously improve the company's knowledge base.

This is where particularly significant long-term leverage lies.

Individual applications create a knowledge base

With every process integrated in a meaningful way, 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 related to one another. Employees' questions can reveal which knowledge is missing or should be documented better.

In this way, an increasingly valuable knowledge base is created step by step.

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

Over time, a single assistant can become an intelligent company structure in which knowledge is not merely stored but actively used.

Create value first, then scale

Companies do not have to start with the largest AI solution.

They 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 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 experiences are often a better foundation for further development.

After that, progress continues consistently:

First use case. First experiences. More knowledge. Next process. More speed.

With every step, expertise, acceptance, and the ability to transition further areas more quickly into the company's AI structure increase.

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

The goal can be 100 percent from the outset. The path to it is taken gradually.