Paulo Shindi Kuniyoshi, Chief Data and AI Officer, Leroy Merlin
The mandate has reached the desks of all CIOs (Chief Information Officers): the board demands an “AI in everything” strategy. This directive, driven by a combination of market euphoria and competitive pressure, represents the defining leadership test of our era. The challenge for the technology executive is not just technical, but fundamentally strategic: how can they transform boardroom hype into tangible value, especially when data shows that the journey is fraught with obstacles?
For the CIO, navigating this pressure requires the orchestration of three critical roles: the Architect of Reality, the Engineer of Trust and the Builder of Value.
1. The Architect of Reality: From Expectation to Capability Strategy
It is common for CEOs and board directors to demand a data and AI strategy. However, it is essential first to understand the business need or strategy before developing an AI strategy that addresses the specific problem.
The next step is to map and build a phased digital maturity journey, starting with an honest assessment of the company’s current capabilities processes, technology, data, and people. From this, it becomes possible to design a roadmap that prioritizes “quick wins” AI projects with limited scope but high perceived impact that address real pain points within specific departments. A common bottleneck for scaling AI is poor data quality and availability because they usually reflect flawed processes or inaccuracies in integrations between systems. Another challenge related to maturity is the scarcity of qualified professionals due to rapid technological change. In terms of technology, the leader must emphasize that AI platforms and organizational readiness need time to mature. Hasty implementation without proper foundations often fails. A phased approach allows for gradual development of both technology and capacity, leading to sustainable AI adoption.
Finally, it is essential to provide clarity and visibility regarding maturity, combined with proactive education for the board and senior leadership. This involves working on the dissemination and development of a Data & AI culture throughout the company.
2. The Engineer of Trust: Governance as an Accelerator, Not a Brake
The CIO must act as an engineer of trust, which is the main line of defense against compliance, ethical, and reputational risks. It is necessary to build the “guardrails” that enable the organization to accelerate safely.
By using data to architect reality, engineer trust, and build a portfolio of value, CIOs can transform board pressure into a measurable and lasting transformation journey. 
This means going beyond GDPR compliance. It is about establishing a robust AI Governance framework proactively to comply with the AI Act regulation. A multidisciplinary AI Ethics Committee should be established to define clear policies on data provenance, algorithmic bias, model transparency, and accountability for automated decisions. Imagine, for example, the impact of a generative AI–powered chat bot that provides incorrect information about an insurance policy, denies a service due to algorithmic bias, or interacts with frustration and a lack of empathy toward a loyal customer. The consequences unfold in customer loss (churn), remediation costs and, increasingly, in litigation, regulatory fines and loss of brand reputation.
3. The Builder of Value: From Pilot to AI Portfolio
Board pressure demands results, with the goal of avoiding the so-called “pilot purgatory,” where AI initiatives never reach production scale.
See below how to structure this journey:
● Phase 1: Discovery and validation
The focus is on validating a business hypothesis with minimal resources. The first step is to assemble a cross-functional team (squad) to answer: “Does this AI solve a real problem and generate value?” The result is an MVP, tested with users to generate learning and data about its potential.
● Phase 2: Industrialization and value
With a validated MVP, the initiative is taken to the next governance stage, before an Executive Committee for AI and Value Generation based on KPIs such as ROI, sales, productivity, and customer satisfaction. The function of this committee is to analyze the business case and decide on the investment for industrialization.
● Phase 3: Scale and transformation
The solution evolves from a project into a strategic capability of the organization. More importantly, the committee evaluates how this new capability can be used to create new revenue streams, transform existing business models, and generate lasting competitive advantages. The organization not only uses AI, it becomes an organization that innovates through AI.
Conclusion: The CIO’s Mandate in the Intelligent Enterprise
The demand for AI is massive, but the implementation challenges are real and the risks are significant. The CIO’s success will be measured by their ability to build an intelligent, resilient, and ethical company. By using data to architect reality, engineer trust, and build a portfolio of value, CIOs can transform board pressure into a measurable and lasting transformation journey.