In recent months, artificial intelligence has become the answer to almost every question. If a process is slow, people think about AI. If a task takes too long, people think about AI. If a company wants to innovate, people think about AI. This is understandable, because few technologies in recent history have demonstrated such a clear ability to automate tasks, reduce operational time, and support decision-making. The risk, however, is that enthusiasm leads many companies to focus immediately on the solution without fully understanding the problem.

When it comes to supply chain, logistics, and transport, the real question should not be which AI tool to adopt, but where to apply it to achieve the greatest impact. AI can speed up a process, support a decision, and even perform tasks autonomously, but it cannot create value if it is introduced at a random point within the organization without a clear understanding of the dynamics generating inefficiencies.

For this reason, Business Intelligence is often the real starting point for any AI strategy. Before suggesting actions, automating tasks, or identifying improvement opportunities, companies need to build a clear and shared understanding of what is happening within the organization. Only when data is collected, organized, and transformed into readable information does it become possible to understand which processes deserve attention and which interventions can generate the greatest return.

In this sense, Business Intelligence and Artificial Intelligence are not competing technologies but complementary ones. The former allows you to see, while the latter helps you interpret and act. BI is the radar that highlights where inefficiencies are emerging, where costs are accumulating, and where opportunities for improvement are hidden. AI is the engine that helps understand root causes, simulate scenarios, identify correlations, and suggest possible actions. Without the radar, however, the engine simply risks driving the company faster in the wrong direction.

It is no coincidence that the organizations achieving the most significant results with artificial intelligence have followed a very different path from the one often portrayed. They did not start with technology, but with processes. They used data to understand where bottlenecks existed, measured inefficiencies, identified the areas with the greatest economic impact, and only then introduced AI tools where they could deliver concrete and measurable benefits.

Gartner also highlighted this in a recent analysis for technology company CEOs, showing that the fastest-growing organizations are those able to integrate artificial intelligence both into their products and into their business operations, creating the right balance between innovation and execution.

For many companies, the real risk is not adopting AI too late. The real risk is investing in AI without first gaining a sufficiently deep understanding of their data and processes. In that case, artificial intelligence will simply amplify existing problems, making them faster and even harder to detect.

Before asking which AI solution to adopt, it may be worth taking a step back and looking closely at your supply chain. Because the most important question may not be, “How can we use artificial intelligence?” but rather, “Do we really know where we’re losing money?”