How to start implementing AI in your company

One third of Czech companies already plan to bring artificial intelligence into their processes. Are you one of them? Then this article is for you.

We walk through the areas you should not overlook when adopting AI, so it delivers real results. Using AI alone is not enough. Like any other change, AI needs a clear strategy, goals, and support across the company.

If you are still on the fence about adopting AI, read How to know when the time is right to implement AI first.

Do not rush headlong into adopting AI. It can end up doing more harm than good.

Before you start the actual rollout, find out where you stand right now.

Check whether you have the technical infrastructure to plug AI tools in without trouble.

Look at your processes. Where do repetitive routine tasks pile up that AI could simplify? Which metrics do you already track today (lead time or time-to-market) that AI could help improve?

Assess your data. If your goal is to evaluate data better, start by judging its current state. Without relevant, quality data, AI does not work well, just like a person you hand inadequate inputs.

Look at the team.

Does your company already have any rules or guidelines for using AI?

Your goal has to come from a real need. Setting it may well start with an audit of your processes, where you find out exactly where the shoe pinches and where AI can help.

Do you want to speed up development? Automate routine tasks or take load off the team? It also helps to set one overarching goal that the rest points to: save money, become more competitive, or bring innovation into the company.

How will you know that AI is paying off, and how will you measure it? Be clear from the start about what you want to measure, and track it continuously, not just "at the end of the project".

Knowing your data before the rollout is crucial for measurement, otherwise you will struggle to evaluate it later. Before you start tracking metrics, settle on what you really want to measure and how it ties to your business goals.

Set clear metrics to track. They follow from your goals. The most common are:

You may find that some processes do not need AI at all and plain automation is enough. So start there.

Tools such as Make or Zapier can help.

Pick the first concrete use case you want AI to solve. Ideally a quick win: a project with a big impact that will not take too long.

Besides testing your strategy, a project like that helps motivate the team to take on more of the same.

One example is a chatbot for the most common customer questions. You can finish that in a week.

Create a roadmap that breaks AI down into your processes. Be realistic. The rollout should not weigh the team down but help them work more efficiently. Schedule projects by priority and by how much time the team has. And give every project an owner who is responsible for it.

Pick the tools your strategy needs. Think about which ones fit your stack best and talk it over with the team. You may test a few at first. Review their use regularly so you do not pay for tools that overlap.

For starters, you can probably get by with tools such as:

Establish internal rules for working with AI: what is allowed, what is forbidden, and which solutions are approved. Decide which types of data are risky to feed into AI tools, such as customer data.

Make sure you use AI in line with regulations (the AI Act) and assess the possible risks.

Write a simple internal framework that sums up how you work with AI. It makes onboarding new team members easier.

Explain to people why they should use AI safely, and show them the risks they should learn to spot themselves: AI hallucinations, data leaks, and errors in generated code.

Train every team that will work with AI, not just IT.

Repeat the training regularly and keep the information up to date.

For a practical, role-by-role example, see our AI and OpenAPI training case study. If you need a broader rollout pattern, the Heureka AI adoption case study shows how training, ambassadors, tool governance, and hands-on pilots fit together.

Share the goals and expected benefits across the company. Then focus on team leads, managers, and tech leads, who can carry the information further into the teams.

Fold the AI rollout into the company's strategic plans and report on progress regularly: what already works, where the obstacles are, and what is coming next.

And ask for feedback regularly. Make sure AI is giving the team what you expected and not just adding more headaches.

Find the people who want to be AI drivers across roles (development, marketing, HR).

AI ambassadors help motivate the team and earn more trust than a push from the top alone.

Give ambassadors enough time and support: regular meetups, community involvement, and room to experiment.

With their newfound knowledge they can inspire others, share what is new, or train teams.

Adopting AI can help your company be more efficient, cut costs, or strengthen its competitiveness. But approach it thoughtfully and strategically, so the adoption truly brings the results you want.

Otherwise you risk an unnecessarily large investment, a demotivated team, or the loss of important data.

Still, there is no need to fear it. The point is not to stall in lengthy processes that lead nowhere, but to invest dedicated time up front in a strategy that pays you back many times over.

If you are still unsure how to adopt AI or just want some guidance early on, get in touch. We would be happy to help.