By Alejandro Inzunza, Co-founder of Pharu Analytics
The enthusiasm for artificial intelligence has reached small and medium-sized enterprises (SMEs) in Chile. A recent SAP study revealed that 58% of SMEs believe AI will have a significant impact on their industry in the coming years, and 46% plan to increase their investment in the technology during 2025. Additionally, 59% intend to train their teams in AI, reflecting a clear willingness to move toward digital transformation. At first glance, the outlook seems promising. However, beneath the optimism lies a fundamental tension: while the intent is there, clarity on how to implement AI effectively remains low.
Many SMEs (and not only SMEs) fall into a silent trap: acquiring tools before defining the problem they want to solve. AI becomes just another experiment, rather than a strategic solution. Isolated pilots are developed that never scale. Reports are produced that don’t influence decisions. Workflows remain unchanged, as if the technology had never been introduced. AI doesn’t fail in SMEs because the technology doesn’t work—it fails because it’s being forced into organizations that haven’t yet changed the way they make decisions.
The difficulty is not only technological—it’s structural. The same study shows that 36% of companies admit they don’t know how to integrate AI into their processes, and 44% of medium-sized businesses cite the lack of specialized talent as a major obstacle. Add to that the operational reality: fragmented systems, scattered data, legacy processes, and teams overwhelmed by urgent tasks. In this context, talking about AI without rethinking the business can be more of a burden than an opportunity.
Effective AI implementation doesn’t start with a tool—it starts with a question. Our Goal-Based Data Organization (GDBO) approach emphasizes starting from business objectives: identifying critical decisions, understanding what data is needed to support them, and only then designing technological solutions that add value. In this framework, data and AI are not ends in themselves—they are means to solve real problems and move forward with purpose.
The solution isn’t to add AI to the business just because it’s trending. It’s to use AI to solve and improve actual business challenges. And that requires leadership, focus, and a clear roadmap: identifying key decisions, organizing and trusting the data that supports them, and redesigning processes so that technology adds value from day one. Only then can AI stop being a promise and become a living, useful tool with real impact on the productivity, resilience, and competitiveness of our businesses.
