Costa Rica leads Latin America in artificial intelligence adoption. According to Microsoft, 28.5% of the country's working-age population already uses AI in their daily lives, making it the highest adoption rate in the region. Every week brings a new tool, a new assistant, or a new "copilot" promising to automate entire business processes. The excitement is understandable, and for good reason: no organization wants to be left behind.
However, there is one statistic that rarely becomes part of that conversation. According to MIT's The GenAI Divide study, only 5% of enterprise AI pilot projects generate measurable and positive business impact. The remaining 95% fail to create meaningful organizational change. The problem is not that AI technology doesn't work. Gartner found that 63% of organizations either lack the data management practices required for AI or do not even know whether such practices exist. At the same time, Informatica's CDO Insights report found that only 12% of organizations have data that is sufficiently accessible, reliable, and ready for AI initiatives.
In other words, companies are not failing because they selected the wrong AI solution. They are failing because they are trying to build on a foundation that was never prepared for AI in the first place.
We've Seen It Firsthand
This is not just theory for us. We have worked on projects where two different systems within the same organization described the same business information in different ways. The people who used those systems every day knew which source to trust in each scenario because of experience, habit, or simply because "that's how we've always done it." Yet that knowledge was never documented anywhere.
The issue was not that the data was incorrect. The issue was that the relationship between those systems, and the rules that determined which source should be trusted in each situation, existed only in people's minds. Artificial intelligence does not have access to institutional knowledge that lives solely in experience. It can only work with information that has been documented, structured, and connected.
Before Anything Else, Do This
Start With a Specific Use Case, Not "AI for the Entire Company"
The idea of transforming an entire organization through AI is appealing, but it is rarely realistic. Choosing a specific problem, such as an HR assistant that answers employee questions or a solution that helps generate reports for a single department, allows organizations to test, measure, and improve without putting the entire business at risk. The most successful AI initiatives usually begin with a clearly defined business challenge before expanding to broader use cases.
Organize and Add Context to Your Data, Don't Just Clean It
Having accurate and complete data is important, but it is not enough. Someone must define what each piece of data means, who can use it, and how it relates to other information across the organization. That context is what enables AI to reason about the business rather than simply produce likely answers.
Without context, AI has nothing reliable to anchor itself to. And when it lacks that foundation, it starts filling in the gaps. This is what we refer to as an AI hallucination: a confident response that is simply not true. The problem is not that AI is flawed; the problem is that it was never provided with the context necessary to understand the business environment correctly.
The Question Worth Asking
If your organization is planning to adopt AI this year, the most important question is not, "Which AI tool should we buy?" The better question is: could someone in our company clearly explain, in writing, how our data connects together and which sources can be trusted?
If the answer is no, that should be your first project before any AI initiative. At Dkodde, we help organizations build that foundation so that when they take the leap into AI, it is not a leap into the unknown. Using platforms such as Microsoft Fabric, we create modern, AI-ready data foundations that allow intelligent agents and AI solutions to generate meaningful, reliable, and lasting business impact.