29% of SMBs Use AI at the Core of Their Business. What About the Other 71%?

The difference between playing with AI and working with AI

When the OECD report on AI adoption in small and medium-sized businesses landed on my desk, one number stood out immediately: only 29% of SMBs using generative AI have integrated it into their core operations. The remaining 71% are, at best, experimenting. At worst, paying for subscriptions that move no real business metric.

This is no surprise to anyone who follows the market closely. I work with SMBs in Brazil, Italy, and the US, and the pattern repeats with minimal variation: a manager discovers ChatGPT, the team starts using it to draft emails, someone generates a polished presentation, and the project stops there. No process is redesigned. No metric is defined. No one owns it.

This gap is not technological. It is a management problem.

What the OECD is actually saying

The report is not pessimistic about the technology; quite the opposite. It documents a real adoption curve, with SMBs across multiple countries incorporating generative AI tools at an increasing pace. The structural problem it identifies is this: most companies treat AI as a peripheral tool, used in fragmented ways by individuals, with no connection to workflows that affect revenue, cost, or time.

For an SMB decision-maker in Brazil, where pressure on productivity is constant and margins leave no room for inefficiency, this data carries a direct message: your competitors who have reached that 29% are likely already reaping measurable competitive advantage. And the window to close that gap is now.

The endless pilot trap

There is a phenomenon I call internally the "endless pilot": a company tests a tool, users approve, but the initiative never leaves the experimental phase. There is no decision on scale, no formal training, no minimum governance over what can and cannot be fed into these platforms.

In Brazilian SMBs, this problem is amplified by two factors: lean teams that lack the bandwidth to absorb change without structure, and a management culture that still treats technology as a cost rather than a strategic lever.

The path of the 29%: three decisions that make a difference

Companies that move past the experimental phase and reach real integration share a few things in common. It is not a larger budget. It is execution discipline.

First: they choose a workflow, not a tool. The right question is not "which AI will we use?" It is "which specific process, if accelerated or automated, delivers verifiable return within 90 days?" It could be lead triage, commercial proposal generation, or first-level customer support. One process. One owner. One metric.

Second: they assign human accountability. Every AI implementation that works has a person responsible for the outcome, not for the tool, for the outcome. That role does not require a data engineer. It requires someone with the authority to change the process, train the team, and report progress to leadership.

Third: they treat governance and training as part of the rollout, not as bureaucracy to handle later. Which data can be shared with external platforms? How does the team validate output before using it? Which use cases are off-limits? These questions need answers before deployment, not after an incident.

The cost of waiting

In the Brazilian context of 2025, with currency pressure, rising labor costs, and increasingly digital competition, the cost of inaction far outweighs the risk of experimenting with structure. SMBs that delay real AI integration are not being cautious; they are ceding ground.

The OECD report is, at its core, an opportunity map. It says that most of the market is still at the surface. For those who decide to go deeper with method, the competitive space is open.

What to do next week

If you lead an SMB and want to move from the 71% to the 29%, the initial move is straightforward (not easy, but straightforward):

Map the three internal processes that consume the most repetitive time from qualified people. Choose the one with the greatest direct financial impact. Define who owns it. Set a success metric for 60 days. And treat training and governance as project deliverables, not future to-dos.

Generative AI does not transform businesses simply by existing. It transforms when integrated with intention, accountability, and measurement. The OECD report confirms what I see in the field every day: the technology is available. The decision to use it seriously is still a human one.