Most companies in the region are already experimenting with artificial intelligence. The problem is that few have managed to turn that experimentation into business results. These are the decisions that can help close that gap.
Artificial intelligence is already inside Latin American companies. It is being used to generate content, automate tasks, analyze information, serve customers, and support decision-making.
But adopting AI and capturing value from it are two different things. A study by the World Economic Forum and McKinsey & Company, based on a survey of 129 organizations in Latin America, found that only 23% generate some economic value from AI, while just 6% report significant value creation.
1. Start with a business problem, not a tool
One of the most common mistakes is starting with the technology:
Where can we implement AI?
The starting point should be different: What business problem do we need to solve?
It could be reducing fraud, increasing conversion, shortening customer service times, anticipating customer churn, or improving a team’s productivity. Once the problem is clearly defined, it makes sense to evaluate what type of AI can actually solve it.
What should companies do?
Choose a specific business metric and build the use case around it.
If the goal is to reduce customer churn, for example, the initiative should not simply be “implement AI in customer service.” It should aim to identify which customers are most likely to leave and trigger a specific action to retain them.
2. Bring AI into the entire process
Another difference lies in the level of integration. Someone who uses AI to draft an email may work faster. But that does not mean the company has transformed its sales process.
The impact increases when AI becomes part of an end-to-end workflow and changes how decisions are made or tasks are executed.
For example:
- Sales: identify leads with the highest likelihood of conversion and prioritize sales efforts.
- Customer service: classify inquiries, automatically resolve the simplest ones, and escalate complex cases.
- Operations: anticipate failures before they affect production.
- Risk: detect fraud patterns and act before a transaction takes place.
Capturing value requires rethinking core processes and business models, rather than simply looking for small productivity gains. Map the entire process, identify where AI can change a decision or remove friction, and redesign the workflow around that capability.
3. Choose a few use cases and drive them to results
Having a long list of pilots does not mean having an AI strategy. In fact, it can mean the opposite.
When a company tests too many use cases at once, resources become scattered, making it harder to take any one of them to scale.
Use cases should be prioritized based on three criteria:
- Impact: how much it can move a relevant business metric.
- Feasibility: how easy it is to implement with the available technology and data.
- Scalability: whether it can be extended to other areas or processes once it works.
The goal should be to identify two or three use cases that work and turn them into real business capabilities.
4. Put someone in charge of the outcome
AI should not be the sole responsibility of the technology team. When a project aims to reduce costs, increase sales, or improve the customer experience, the business area accountable for that outcome should also be involved in the AI initiative.
Every use case should have: an owner + a metric + a deadline.
This makes it possible to answer three questions from the start:
- Who is responsible for delivering the result?
- How will we know whether it worked?
- When will we decide whether to scale or stop the initiative?
Without that structure, a pilot can remain active for months without proving whether it is actually creating value.
5. Measure before scaling
The goal of a pilot should be to prove that it works for the business.
That is why, before implementing a solution, companies should establish a baseline: how much the process costs today, how long it takes, what its conversion rate is, or how many errors it generates. They can then compare what changed after introducing AI.
Define the expected outcome before implementing the solution and measure it afterward. Without a business metric, there is no way to know whether AI is creating value.
6. Don’t build from scratch what already exists
Capturing value with AI does not necessarily mean developing a proprietary model. For many companies, particularly those without large technology teams, it may be more efficient to evaluate existing solutions, integrate them into their systems, and adapt them to their needs.
This can reduce implementation time and allow companies to focus investment on what can actually become a competitive advantage: their data, processes, and the way the organization uses technology.
The next step is not adopting more AI
Latin America has a significant opportunity ahead. The report estimates that AI could generate between US$1.1 trillion and US$1.7 trillion in additional economic value per year across the region, but capturing that potential requires moving from experimentation to much more disciplined execution.
That is where the real work begins: identifying the opportunity, designing the use case, redesigning the process, implementing it, measuring its impact, and scaling it.
That is the journey that separates a company that uses AI from one that captures value with AI. And that is the real gap Latin American organizations still need to close.