I have been closely following the transformation brought about by artificial intelligence in the business world. At the heart of this revolution, the role of the CIO has evolved rapidly. It’s no longer enough to enable technology. It’s necessary to lead the change. And this is where the difference lies between an operational CIO and a truly transformative CIO.
The CIO who acts merely as a technical enabler of AI misses the most important part of the equation: the impact on business. Of course, information security, data architecture, and compliance are fundamental topics, but they are not enough. 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 how entire departments work. For this, the CIO must go beyond IT. They must master strategic design, user experience, and service journey. Only then is it possible to align technology with purpose and impact.
Such alignment remains a barrier for many. According to the study Gartner CIO Agenda 2025, 72% of CIOs worldwide state that artificial intelligence is among the strategic priorities of the technology area. However, only 24% can prove 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’re a CIO and still stuck in the experimentation phase, my suggestion is clear: develop three fundamental competencies to turn the game around and deliver real value.
- Strategic and service design: Understanding how workflows and experiences connect is essential to building AI solutions that make sense within the business.
- Agile experimentation: Nothing replaces the ability to test fast, fail fast, and learn even faster. Models like Scrum, Lean, and Design Sprint are great allies.
- Adaptability: AI changes every day. New models emerge, APIs evolve, 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 found that only 11% of the companies analyzed achieved positive financial returns with AI. What do they have in common? Strong integration between technology and business strategy, along with clear governance and a focus on value from the start.
How I’ve applied this in practice
At the company where I serve as CIO, we made the decision to democratize access to AI from the start. We built an internal platform, a true AI hub, that connects different models (including the leading LLMs on the market) in a single interface, accessible to all 900 employees.
This measure avoids two common mistakes: the uncontrolled use of public tools (which can compromise sensitive data) and limiting AI use to isolated niches. Here, everyone has access, from customer service to leadership.
Additionally, we created a public innovation roadmap, updated twice a week, that clearly shows ongoing projects, their phases, deliverables, and next steps. This fosters transparency, engagement, and accountability.
Another initiative is monthly AI workshops on topics like autonomous agents, prompt engineering, LLM comparisons, and more. Over 400 people actively participate. Most importantly, we have a C-level council that prioritizes AI initiatives based on business return.
This kind 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 $1.9 billion in AI 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 quick 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 end-users work together. Continuous collaboration between technology and business generates more relevant, effective, and lasting solutions.
At the end of the day, good AI is AI that works in the real world. And the CIO who understands this, who builds alongside users, stops being just a technology manager and becomes a protagonist in business transformation.
