The AI paradox in LATAM 2026: high adoption, low real value (and what to do in your SMB)

Why is so much AI being used in the region and so little result showing up? Only 23% of organizations in Latin America report any economic value from using it, and just 6% report significant value, according to the World Economic Forum. The same analysis notes that six out of ten SMBs in the sample generate no measurable value. The useful question is no longer whether to adopt or not: it became which workflow to redesign, which case to start with and how to prepare the team.

How big is the gap?

The samples and the definitions are not equivalent across studies, so it is not a good idea to add them up. Even so, they all point to the same thing: using AI became common and capturing value with it is still rare. The WEF attributes that to most organizations using it to gain productivity on isolated tasks instead of rethinking the process.

This is for anyone who sees the team using AI every day and does not see the change in the business.

The adoption funnel shows how many attempts are lost before becoming measurable results.

Why do so many use it and so few capture value?

Access leveled out, judgment did not

Opening a ChatGPT, Claude or Copilot account takes minutes and requires nobody's permission. Deciding which part of the operation it belongs in, on the other hand, takes judgment and time. At an SMB that decision usually falls on the owner, who already has fifteen open matters, and it ends up being settled by accumulation instead of by priority.

The information is not ready

Gartner projects that, through 2026, 60% of AI projects that are not backed by ready data will be abandoned. At SMBs in the region, knowledge lives in people's heads more than in a system, and a tool that reads scattered information returns scattered answers.

People run trials with no number to look at

Almost nobody writes down how the process stands before putting AI into it. Without that number there is nothing to compare against later: if putting a proposal together used to take three days and nobody wrote it down, nobody can say whether it takes less today. The discussion ends up being settled by impressions, and impressions almost always favor the new tool. BCG puts the organizations that achieve substantial financial value at around 5%: getting there is uncommon, and without a measured starting point you have no way to know whether you are in that group. Before scaling anything, write down how the process stands today and what result you are going to count as success.

What do the ones that do capture value do differently?

Five practices, none of which can be bought. They are operational recommendations from this guide based on the evidence above, not traits measured one by one in that regional 23%.

Working one single use case per quarter, instead of five in parallel. Measuring the starting point before you begin, in time, errors or conversion. Training most of the team and not two people: the organizations BCG calls future-built train more than half of their workforce, against 20% at the laggards. Putting someone other than the owner in charge of the topic. And making a dated decision at day 90: scale, adjust or close.

These five practices help turn AI adoption into sustained business value.

What can you do this week?

1. Review which tools you are paying for

Make the list of active AI licenses and for each one answer three things: whether there is a prioritized use case tied to that tool, whether someone is in charge and whether there was real training. When two of the three answers are no, that tool is not adding capability, it is adding fixed cost.

2. Pick one case and put a number on it

Pick a single case, and make it a pain your team recognizes without having to think about it. For example: proposals that take too long, clients nobody followed up with, meetings that end without decisions, or the work that stalls when one of two people is missing.

3. Write down the starting point and the 90-day goal

If you cannot write that number down today, the case is not ready to be tested yet. What it needs is a diagnostic, not a pilot.

It is also worth saying what does not work, because it costs time and money. Waiting for the definitive tool to come out is no use: the ones that serve an SMB already exist. Copying a large company's setup is no use either: the comparison that matters to you is against your own company 90 days ago. Testing several tools at once leaves you not knowing which one moved the result. And training a single person, hoping the rest learn by contagion, ends with the whole team depending on that person.

The companies that do get results do three similar things. They decide out loud what they are not going to automate for now, so that part is settled and not forgotten. They measure how the process stands in the first week and not in the fourth, when nobody remembers anymore how it used to be. And when it has to be discussed among partners, they discuss it with the numbers from their own trial, not with market figures.

Which group is your company in?

VegasiO's Discovery web takes 5 minutes and places your case: whether you are capturing value or using AI without capturing it. At the end it tells you whether the next step is a Diagnóstico de Adopción IA, Capacitación IA para ti y tu equipo, an Implementación de IA, or consolidating what you already have before moving ahead.