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Nearly eight in ten companies in LATAM that already have artificial intelligence running in production cannot show a measurable impact on the business. This is not a model problem. The models work.
According to IDB and McKinsey data, 67% of large enterprises in the region have at least one AI project in production, but only 23% report real impact on their metrics. Globally the number is even more uncomfortable: Forrester found that just 15% of decision-makers saw a concrete improvement in profitability over the past twelve months.
If you are reading this suspecting that your company spent on artificial intelligence and still has not seen the return, you are not alone. And the reason the AI ROI never shows up is almost always the one nobody looks at.
Two years ago, having a chatbot or an assistant built on an enterprise LLM set you apart. Today your competitor uses the same tools. The advantage moved: it went from "using AI" to capturing value with it.
The numbers confirm it from several angles. In PwC's 2026 Global CEO Survey, only 12% of chief executives said AI brought them both revenue growth and cost reductions; 56% had not yet seen a significant financial benefit. A Publicis Sapient study of 1,550 decision-makers found something similar: 73% use AI across most of their processes, but only 10% say AI is core to how their business operates.
Translated: almost everyone adopted. Very few turned that AI adoption into results. That distance is the real gap of 2026, and it is where AI ROI is won or lost.
Here is the uncomfortable part. Technology is not the bottleneck. The operating model that stayed the same underneath it is.
Deloitte, in its State of AI in the Enterprise 2026, found that 84% of organizations added AI capabilities without redesigning a single job or a single workflow. New tools, old processes. The Publicis Sapient study puts it plainly: 42% of executives believe AI is already capable of solving what their business needs, but that their organization is not set up to capture that value.
When you buy a model and plug it into an operation that did not change, the model does its part and the process does what it always did. The result is AI adopted in a fragmented way, with pilots that do not scale and no real impact at the enterprise level.
Because they buy tools without building the capability to use them. The model works, but the team does not know where to apply it with judgment, and the process was never redesigned around the AI. The return does not depend on the technology; it depends on the organizational capability to integrate, govern, and measure it.
There is one data point that puts everything else in order. In its 2026 report, DataCamp found that only 21% of business leaders report significant ROI from their AI investments. But among organizations with a mature, company-wide AI literacy program, that figure jumps to 42%. Nearly double.
The difference is not the model or the software. It is whether people know how to use the tool with judgment.
And training alone is not enough. The same study shows the full paradox: 82% of companies offer some form of AI training, yet 59% still report an internal skills gap. Fewer than one in three has a mature, organization-wide program. Most training is passive, video-based, and hard to apply to real work. As DataCamp's CEO put it, investing aggressively in tools without investing in capability will inevitably limit the return.
So the problem is not access to AI. It is judgment to get value from it. As the tools get easier, the risk shifts: it is no longer a lack of access, it is a lack of judgment to tell when an output is useful and when it is not.
Faced with this picture, many companies hit the brakes. Forrester projects that organizations will defer 25% of AI spend planned for 2026 into 2027, waiting to see a return before putting in more money. Gartner, in parallel, estimates that more than 40% of agentic AI projects will be canceled by 2027, largely due to unclear ROI and weak governance.
It is an understandable reaction, but wrong in its logic. Freezing the investment without touching the team's capability guarantees the same result, only later. The gap does not close by pausing spend. It closes by building the capability that was missing from the start.
When we started deploying agents in production, inside our own operation first, we expected the problems to be technical. They almost never were.
What we saw again and again was something else: pilots that looked flawless in the presentation and did not scale in production. Teams that had access to the tool but did not trust the outputs, so they went back to the old process. And, above all, projects that started without a baseline. Without a "before" number, the ROI afterward has nothing to compare against. It is not that it does not exist: it is that nobody can prove it.
When we changed the order, everything moved. Before scaling anything, we started by making the baseline metric clear and by building judgment in the team that would operate the agent. Only then did we deploy. The return stopped being a promise and became a number the client could audit. That is why we work with an outcomes model: if we share the project's risk, we have no choice but to solve capability, not just hand over the tool.
AI ROI is measured by comparing a business result against a baseline defined before implementing. You pick one process, set two or three metrics (cycle time, cost per transaction, revenue), and track them for at least a quarter. Without that starting number, the return cannot be proven.
It depends on the case, but well-scoped projects usually show measurable impact in weeks or a few months, not years. The ones that stall or never arrive almost always fail for the same reasons: no baseline, processes left unredesigned, and teams without the judgment to apply the tool.
Because buying the tool and capturing its value are two different things. If the model was added on top of the same old process and the team has no judgment to use it, the AI works but the business does not change. The return lives in organizational capability, not in the software.
Most companies in LATAM that fail to capture their AI ROI do not have a model problem or a vendor problem. They have an unmeasured distance between what they bought and what their people know how to do with it. The models work. The capability to use them with judgment, govern them, and measure them is what is scarce.
If you have AI in production and still do not see measurable impact on the business, the next step is not buying more software. It is diagnosing where the gap is. At Greencode, an Agentic Diagnostic of two to three weeks shows you exactly what separates your AI investment from a measurable ROI, and what it takes to close it before you spend another dollar.
In 30 minutes we identify the highest-impact opportunity for your business and show you exactly how it gets implemented.
