CIO as the AI Catalyst: From Experimentation to Impact on Results

I have been closely monitoring the transformation driven by artificial intelligence in the business world. At the heart of this revolution, the role of the CIO has been rapidly evolving. It is no longer enough to merely enable technology. One must lead the change. And this is precisely what distinguishes an operational CIO from a truly transformative one.

The CIO who acts solely as a technical enabler for AI misses the most critical part of the equation: business impact. Of course, information security, data architecture, and compliance are fundamental topics, but they are not sufficient. True transformation occurs when AI is designed to change how the company operates, and this requires a deep understanding of the business model.

Today, much of the value of generative AI lies in the orchestration of multi-agent solutions, capable of automating processes, making real-time decisions, and changing the way entire departments work. To achieve this, the CIO must look beyond IT. They must master strategic design, user experience, and service journey. Only then can technology be aligned with purpose and impact.

Such alignment remains a barrier for many. According to the Gartner CIO Agenda 2025, 72% of CIOs worldwide state that artificial intelligence is among their technology department's strategic priorities. However, only 24% can demonstrate that they are generating tangible value from their initiatives. This highlights a gap between intention and execution, reinforcing the need for a more active and strategic role for the CIO in the AI journey.

Three Key Competencies to Move Beyond the Lab

If you are a CIO and are still stuck in the experimentation phase, my suggestion is clear: develop three fundamental competencies to turn the tide and deliver real value.

1. Strategic and Service Design: Understanding how workflows and experiences connect is essential for building AI solutions that make sense within the business.
2. Agile Experimentation: Nothing replaces the ability to test quickly, fail fast, and learn even faster. Models like Scrum, Lean, and Design Sprint are great allies.
3. Adaptability: AI changes every day. New models emerge, APIs evolve, and regulations appear. The CIO and their team must be prepared to rebuild whenever necessary. This is part of the game.

In fact, a recent study by MIT Sloan Management Review in partnership with BCG indicates that only 11% of the companies analyzed managed to achieve a positive financial return from AI. What do they have in common? A strong integration between technology and business strategy, along with clear governance and a focus on value from the outset.

How I Have Applied This in Practice

At the company where I serve as CIO, we made the decision to democratize access to AI from the very beginning. We built an internal platform, a true AI hub, that connects different models (including the major market LLMs) through a single interface, accessible to all 900 employees.

This measure prevents two common mistakes: the uncontrolled use of public tools (which can compromise sensitive data) and the limitation of AI use to isolated niches. Here, everyone has access, from customer service to leadership.

Furthermore, we created a public innovation roadmap, updated twice a week, which clearly shows the ongoing projects, their phases, deliverables, and next steps. This generates transparency, engagement, and accountability.

Another initiative involves monthly AI workshops on topics such as autonomous agents, prompt engineering, LLM comparison, among others. Over 400 people actively participate. Most importantly, we have a C-Level council that prioritizes AI initiatives based on their business return.

This type of structure and initiative is becoming increasingly common in Brazil. The IDC Latin America AI Spending Guide 2025 estimates that Brazilian companies will invest over US$ 1.9 billion in artificial intelligence solutions this year. The main focuses are process automation, customer service, data analysis, and decision support. In other words, the local market already sees AI as a strategic pillar, no longer as an isolated experiment.

AI is No Longer a Lab—It's a Value Platform

If I could give one piece of advice to other CIOs, it would be: stop treating AI as a lab experiment. Choose small use cases with high potential impact and rapid implementation, and put them into production. Even if imperfect, these field tests will provide valuable feedback to improve the solution.

The real leap occurs when the development team and the end-users work together. Continuous collaboration between technology and business generates more relevant, effective, and lasting solutions.

In the end, good AI is AI that works in the real world. And the CIO who understands this, who builds alongside the users, ceases to be just a technology manager and becomes the protagonist of business transformation.