I’ve been closely following the transformation brought about by artificial intelligence in the business world. At the center of this revolution, the role of the CIO has evolved rapidly. It’s no longer enough to simply enable technology. It’s necessary to lead the change. And this is where the difference between an operational CIO and a truly transformative CIO lies.
A CIO who acts solely as a technical enabler of AI misses the most important part of the equation: its business impact. Of course, information security, data architecture, and compliance are fundamental, but they aren’t enough. True transformation occurs when AI is designed to change the way a company operates, and this requires a deep understanding of the business model.
Today, much of the value of generative AI lies in orchestrating multi-agent solutions capable of automating processes, making real-time decisions, and changing the way entire departments work. To achieve this, the CIO needs to go beyond IT. They need to master strategic design, user experience, and the service journey. Only then can technology be aligned with purpose and impact.
Such alignment is still a barrier for many. According to the study Gartner CIO Agenda 2025, 72% of CIOs worldwide state that artificial intelligence is among their strategic technology priorities. However, only 24% can demonstrate that they are generating tangible value from these initiatives. This highlights a gap between intention and execution, reinforcing the need for a more active and strategic role for CIOs in the AI journey.
Three key skills to get out of 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 transform, regulations emerge. The CIO and their team need to be prepared to rebuild whenever necessary. It’s part of the game.
In fact, a recent study by MIT Sloan Management Review in partnership with BCG points out that only 11% of the companies analyzed achieved positive financial returns with AI. What do they have in common? A strong integration between technology and business strategy, as well as clear governance and a focus on value from the outset.
How I have applied this in practice
At the company where I work as CIO, we made the decision to democratize access to AI from the outset. We built an internal platform, a true AI hub, that connects different models (including the leading LLMs on the market) into 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 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 displays ongoing projects, their phases, deliverables, and next steps. This creates transparency, engagement, and accountability.
Another area of focus is monthly AI workshops, covering topics such as autonomous agents, prompt engineering, LLM comparisons, and more. Over 400 people actively participate. Most importantly, we have a C-suite advisory board that prioritizes AI initiatives based on business returns.
This type of structure and initiative is increasingly present in Brazil. IDC Latin America AI Spending Guide 2025 estimates that Brazilian companies are expected to invest more than 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 understands AI as a strategic pillar, no longer as an isolated experiment.
AI is no longer a laboratory — it’s a value platform
If I could give one piece of advice to other CIOs, it would be: stop treating AI like a lab experiment. Choose small, potentially high-impact, fast-to-implement use cases and put them into production. Even if imperfect, these field tests will provide valuable feedback for improving the solution.
The real breakthrough occurs when the development team and end users work together. Continuous collaboration between technology and business generates more relevant, effective, and lasting solutions.
Ultimately, 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.
