The Productivity Mirage: Why Using AI Isn't Growing Your Profits (and How to Fix It)

There’s a paradox frustrating executives across Latin America. Year after year, artificial intelligence adoption grows: more tools, more licenses, more pilots. And year after year, the financial results of that investment still fail to show up.

It’s not a perception. It’s a data point. The Latin America in the Intelligent Age report from the World Economic Forum and McKinsey & Company, published in January 2026, puts numbers on the problem. And the numbers are uncomfortable.

The Diagnosis: A Lot of Adoption, Little Impact

AI is being used. That’s not the problem. According to the regional survey conducted for the report (129 organizations of all sizes and sectors), adoption is growing in areas like customer service, software engineering, and personalization. ChatGPT has higher penetration in Brazil than in the United States. Chatbots are proliferating. Code copilots are multiplying.

But when you look at the real economic impact, the picture changes completely.

23% of Latin American organizations generate some economic value with AI. Only 6% report that value as significant (more than 5% improvement in EBIT). — AI capabilities survey, WEF & McKinsey, Oct. 2025, n=129

Put another way: 3 out of 4 companies using AI aren’t seeing a tangible return on their finances. And among small and mid-sized businesses, the situation is worse: 59% report generating no value at all with the technology.

Sound familiar? If your organization invested in AI tools and the quarter-end numbers didn’t move, it’s not that “it doesn’t work.” It’s that the approach is wrong.

The Individual Productivity Trap

Here’s the root of the mirage: most companies are using AI to make individual people work a little faster. An email drafted in 30 seconds instead of 5 minutes. An automatically generated summary. Autocompleted code.

That’s not transformation. That’s convenience.

Real value creation doesn’t come from individual use of AI tools, but from a complete reformulation of core business processes and business models.

The report says it plainly: the organizations generating real impact with AI are the ones integrating the technology into their core business processes, not the ones handing out copilot licenses and waiting for magic to happen.

Only 10% of respondents say their AI strategy is closely tied to their overall business strategy. That means the remaining 90% are treating AI as a side initiative, disconnected from the goals that actually matter.

The Gap Between Those Who Generate Value and Those Who Don’t

The WEF and McKinsey report identified six dimensions that determine whether an organization turns AI into results or into an expense. The three where the biggest gap appears between impact-generating companies and the rest are revealing.

1. Talent: the biggest gap

Companies that see no results lack career paths for AI professionals, have no visibility into their teams’ skills, and fail to attract or retain specialists. Latin America’s average AI talent score is 23 out of 100, versus 52 for global leaders.

2. Operating model: the decisive difference

Organizations with positive impact launch AI projects in 6 to 9 months. Those with no impact take more than a year. The difference isn’t technological — it’s organizational: agile teams with a clear mandate, cross-functional collaboration between AI professionals and the business, and defined execution processes.

3. Adoption and scaling: from pilot to process

The biggest gap versus global leaders is in the ability to measure and communicate AI’s value, and above all in integrating AI solutions into business processes. Most initiatives stay stuck at the proof-of-concept stage and never reach scale.

$1.1–1.7 trillion a year is the economic value AI could generate in Latin America if adopted comprehensively. Inaction, on the other hand, threatens a prolonged stagnation. — McKinsey & Company analysis, 2025

5 Keys to Turning AI Into Real Profits

Knowing what’s wrong isn’t enough. You need a concrete path. Based on the report’s 10 strategic actions and the patterns of organizations that are actually generating value, these are the five levers you can pull today.

1. Tie every AI initiative to a measurable financial outcome

Stop approving AI projects for “innovation” and start demanding a business case with clear metrics: cost reduction in which process, revenue increase in which line, retention improvement in which segment. Organizations that generate impact measure and communicate AI’s value systematically. If you can’t put a number on it, don’t start the project.

2. Target core processes, not peripheral tasks

The most common mistake: applying AI to draft emails or summarize documents, while the processes that actually move the needle (credit approval, supply chain, dynamic pricing, fraud detection) keep operating the same way they did ten years ago. The sectors leading regional impact, like financial services, do so because they integrate analytical and generative AI into their business processes end to end.

3. Train your team by role, not with generic courses

Talent is where Latin America has the biggest gap versus global leaders. But the solution isn’t hiring more data scientists. It’s developing AI skills tailored to each role: sales teams that know how to use propensity models, operations that understands process automation, finance that masters predictive analytics. A role-based development plan, with clear career paths, is what separates retaining talent from losing it to the multinational next door.

4. Execute fast: from idea to value in under 9 months

Latin American organizations that generate impact launch AI initiatives in 6 to 9 months. Those that don’t take more than a year. The difference is in the operating model: small teams with autonomy, budget assigned from the start, agile iteration, and an executive sponsor pushing adoption. If your AI project needs 14 months of approvals to reach production, the problem isn’t the AI.

5. Fix your data before buying more tools

Companies that generate no impact with AI are more than three times as likely to show low data maturity compared to those that do generate impact. It doesn’t matter how much you invest in models if your data sources are fragmented, unintegrated, and ungoverned. The foundation of any AI strategy that works is a coherent, accessible, quality data base.

From Paralysis to Action

Latin America has a concrete window of opportunity. AI could increase the region’s productivity by between 1.9% and 2.3% a year — a radical shift for economies where productivity has grown barely 0.4% a year over the last 25 years.

But that window won’t wait. While companies in the region keep piling up pilots that never scale, global leaders are integrating AI across their entire value chains.

The good news: you don’t need to build your own language model or your own data center. What you need is more mundane and more powerful: a business-connected strategy, data in order, talent developed by role, agile execution, and the discipline to measure every initiative against real financial results.

The productivity mirage breaks the moment you stop asking “how do we use more AI?” and start asking “in which business process can AI generate the greatest economic impact, and what do we need to make that happen in the next 6 months?”

That’s the right question. And the answer, according to the data, could be worth trillions.