Artificial intelligence is no longer reserved for large corporations. Today any organization can use it to answer inquiries, summarize documents or extract data from invoices. Even so, many companies don't know where to begin, or they begin in the wrong place: choosing a tool before they are clear on what they want to solve.
This guide lays out an orderly path, in five steps, to take that first step with low risk and results you can measure.
1. Start with a problem, not a tool
The useful question isn't "which artificial intelligence tool should we buy?" but "which task takes up more of our time than it should?". Artificial intelligence works best on work that revolves around text, documents and repetitive decisions. Some common examples:
- Answering the same customer questions over and over.
- Reading emails or forms and sorting them by topic or urgency.
- Extracting data from invoices, contracts or requests to move it into another system.
- Summarizing meetings, reports or long case files.
- Drafting a first version of replies, quotes or descriptions.
Make a short list with your team. The people who do the work every day usually know exactly where time is being lost.
2. Pick a small, measurable first case
From that list, choose just one case to start with. A good first project meets four conditions:
- It happens often: it comes up many times a week, so any improvement shows.
- It follows fairly clear rules: a new hire could learn to do it with good instructions.
- A mistake isn't serious: if the artificial intelligence gets it wrong, someone catches it before it causes a problem.
- It can be measured: you know how long it takes today or how many errors it produces.
Resist the temptation to start with the most complex process in the company. A small case that works builds confidence, know-how and the argument for the next one.
3. Check your data before the model
The quality of the result depends on the quality of the information the artificial intelligence works with. Before choosing a solution, answer three questions:
- Where does the information live? In emails, spreadsheets, an accounting system, shared folders… Knowing this determines how easy it will be to connect.
- Is it complete and organized? If the data is duplicated, outdated or spread across different versions, it's worth cleaning it up first.
- Who can see it? Some information should never leave the organization.
That last point deserves special attention. Before pasting customer or employee data into a public tool, review its terms of use: what it does with the information, where it stores it and whether it uses it to train its models. In Costa Rica, the processing of personal data is also governed by Law No. 8968 on the Protection of Individuals with regard to the Processing of their Personal Data.
4. Try what already exists before building
You don't always need custom development. For a first step, it is often enough to use:
- General-purpose assistants, useful for drafting, summarizing or analyzing documents on an occasional basis.
- Artificial intelligence features in the software you already use, such as email, office tools or your customer service system.
These options let you validate the idea quickly and with little investment. Building your own solution starts to make sense when it needs to connect to your systems, handle high volumes, work with confidential information in a controlled environment or follow rules specific to your business. At that point, artificial intelligence is usually combined with process automation so work flows from one system to another without manual steps.
5. Keep a person in the loop
Artificial intelligence models can be wrong with complete confidence: making up a fact, misreading an instruction or misclassifying an unusual case. That's why, especially at the beginning, you should:
- Define what a person reviews before a result reaches a customer or a system.
- Set clear usage rules for the team: what information can be used, in which tools and for which tasks.
- Log the errors that come up: they are the best guide for adjusting instructions or deciding that a case isn't worth automating.
Over time, as the system proves reliable, the review can become more selective.
Common mistakes when getting started
- Trying to automate everything at once. It spreads the effort thin and makes it impossible to know what worked.
- Not measuring the starting point. Without knowing how long a task used to take, there's no way to show the improvement.
- Leaving the team out. A tool people don't understand or don't want to use won't be adopted.
- Trusting without checking. Artificial intelligence is a powerful aid, not a substitute for the judgment of people who know the business.
How to tell if it worked
After a few weeks, compare against the starting point: how long the task takes now, how many errors appear and how the team using it feels about it. If the results are good, you'll have a documented case to scale the solution or apply what you learned to the next process on the list.
Getting started with artificial intelligence doesn't require a big budget or a months-long project. It requires choosing the first problem well, taking care of your data and measuring. To learn more about how it applies to an organization, see our approach to applied artificial intelligence.