Many conversations about artificial intelligence (AI) start with the tool: which model to use, which platform to buy. In our experience, it's better to start from the other end. First, the problem you want to solve. Then, the technology that serves it. These are the steps we follow.
Step 1: Look for a problem, not a technology
Ask yourself where your team loses time or where your customers get frustrated. Some places where opportunities often show up:
- Tasks that repeat every day with few changes.
- Questions your team answers over and over.
- Documents or emails someone has to read and sort by hand.
- Scattered information that is hard to find when needed.
- Reports built by copying data from one place to another.
Write each situation down in one sentence: who lives with it, how often, and what happens when it goes wrong.
Step 2: Pick a single use case
A use case is a specific situation where AI helps get something done. To choose the first one, check these criteria:
- It's clear. You can explain it in two sentences.
- It has an owner. A person or team knows it and can weigh in on the result.
- It has data or documents available. AI needs material to work with.
- A mistake is low-cost. To start, avoid processes where an error would be serious.
- It can be reviewed. A person must be able to check what the AI produced.
Step 3: Define how you'll know it works
Before building, decide what you'll look at. You don't need complex metrics. For example: does the team spend less time on that task? Are the answers correct when someone reviews them? Are people satisfied? Write those criteria down so you can evaluate honestly later.
Step 4: Test small
Build a prototype, meaning a simple version to test the idea with a small group. Use it with real cases and collect what fails. It's better to find that out in a small test than across the whole company. Don't promise results before testing: the prototype exists precisely to learn.
Step 5: Connect it to what you already use
A standalone AI solution gets abandoned fast. What stays is what sits inside daily work: connected to your email, your management system, your chat or your documents. Plan the integrations from the design stage.
Step 6: Protect the data and be clear about who decides
Define what information the tool can see, where it is processed and who answers for the result. If personal data is involved, Law 1581 of 2012 applies. Also, AI proposes and a person decides on what matters. Writing that rule down avoids confusion.
Step 7: Get your team ready
A new tool changes habits. Explain to your team what the AI will do, what it won't, and how to report errors. The people who use the tool every day are the ones who best spot how to improve it.
Step 8: Expand with evidence
When the first case works, pick the next one using what you learned. Each case builds on the last: the data is already in order, the team is no longer wary and the integrations already exist.
Mistakes we see often
- Buying a tool and then looking for something to use it on.
- Trying to solve five problems at once.
- Not assigning anyone to review the results.
- Leaving the data until the end.
How we support you
We work with you from the first step: we understand your operation, choose a use case, build the prototype and support you through implementation and ongoing support. If you already know you want to start with WhatsApp customer service, read WhatsApp chatbots for business: what can be automated and what can't.
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