How do I get started with Artificial Intelligence in my business?

Getting started is simpler than it seems

Some people think artificial intelligence is the business of big tech companies with dedicated engineering teams. It isn't. Today, an SMB can automate repetitive tasks with a €20/month subscription and an afternoon of configuration. What's missing is almost never the technology — it's knowing where to start.

Step 1: Identify the problem (before the tool)

The most common mistake is choosing the tool first and then looking for a use for it. Reverse the order. For one week, ask the team to note down, without filtering, everything that steals their time. Patterns will emerge. The right questions aren't abstract — they're concrete:

• Which task do you repeat every day that takes more than 30 minutes? (answering the same emails, copying data between systems, drafting weekly reports)
• Where do the most expensive mistakes from human error happen? (invoices entered incorrectly, data swapped during input, standard replies forgotten)
• What decisions do you make on data that nobody has time to analyze? (stuck inventory, clients at risk of leaving, campaigns with no measured return)

Pick a single process — preferably one that has a number attached to it (hours, errors, costs). Without a number, there's no way to prove the AI worked.

Step 2: Choose the right tool for that problem

There is no "best AI tool" — there is the right one for the problem you identified. Some practical pairings:

• Writing and communicating (emails, proposals, social media): ChatGPT or Claude. The paid version (~€20/month) pays off through quality and because it doesn't use your data to train models.
• Working with data and spreadsheets: ChatGPT with data analysis, or Power BI with Copilot for those already in the Microsoft ecosystem.
• After-hours support: an AI chatbot (Tidio, Intercom) trained on real frequently asked questions — not a generic script.
• Automating between applications: n8n, Make, or Zapier connected to an AI model. Example: every new lead is qualified and summarized automatically before it reaches sales.

Practical rule: if the solution doesn't save at least one hour of work per week, it doesn't justify the implementation effort.

Step 3: Pilot for 30 days, measure, then decide

The temptation is to automate everything at once. Resist. A well-designed pilot tells you in a month what a year of theoretical planning never will:

1. Define the goal before you start. "We'll save time" doesn't work. "Reduce from 4 hours to 1 hour per week spent drafting proposals" does.
2. Test with one person or a small team for 3 to 4 weeks.
3. Measure the result against the goal — time saved, errors avoided, client satisfaction.
4. Decide with data: did it work? Scale it. Didn't work? Understand why before trying something else.

Watch out for two common traps: the team giving up at the first wrong answer from the model (AI makes mistakes — the point is to catch them) and using sensitive data in tools that leverage it for training. Always check the data policy before you start.

Step 4: Empower the team to use it well

Nobody needs to know how to code. But there's a huge difference between "opening ChatGPT and typing something" and knowing how to extract consistent results. What the team needs to learn is practical:

• Writing clear instructions (that's "prompt engineering"): context, objective, desired format. It doubles the quality of the result.
• Always verify the output: AI invents things confidently (hallucinations). Whoever trusts it early will stumble sooner or later.
• Knowing what not to give it: customer data, contracts, sensitive business information — unless the tool has privacy guarantees.

There's good, free training available: OpenAI, Anthropic, and Google all have official guides. A two-hour session with the team resolves 80% of initial questions.

Where to start, today

You don't need a digital transformation plan. You need one task, one tool, and one week of testing. Pick the smallest, most measurable process you have at hand, define the goal, measure the result. The rest is decided with data — not with fear.