Artificial intelligence is evolving from just a piece of an innovation strategy or merely a “tool”, into the foundational element of business transformation. As organizations move from a place of using AI to experimentation using AI, the conversation surrounding AI from “possibility” to “responsibility” has shifted. That responsibility is for the most part, now at the top. Although CIOs and CTOs have always been an important part of technology adoption, the emergence of the Chief AI Officer (CAIO) reflects an organizational change in the urgency and direction associated with this work.
The CAIO must be engaged to drive AI across all departments, while ensuring that their organization is innovating with AI in a way that is ethical, compliant and scalable in the long-run. For boards and executive teams, this represents yet another opportunity to signal that AI is not merely an activity; it is a pillar of strategy.
The Rise of the Chief AI Officer
This trend is evident across all sectors, from consulting firms to healthcare organizations looking to bring on advanced, strategic leaders in AI. Some recent hiring examples include Accenture, EY, and GE HealthCare. Throughout the past year, companies like Equifax, Ashley Furniture, and Eversheds Sutherland have also added executives focused on AI to their executive leadership teams.
AI Strategy at the Boardroom Table
Executives cannot treat AI as a “future investment” anymore. In Deloitte‘s 2025 State of AI report, 62% of organizations said discussions regarding AI are becoming part of quarterly board meetings. Questions leaders are asking include:
- How do we balance budgets between experimentation and enterprise-grade production solutions?
- What governance models do we use to ensure AI projects are aligned with company values?
- How do we measure ROI without focusing too much on short-term wins?
These are less technical questions than they are strategic imperatives, with executives being the ones to make AI their innovation driver and risk vector. In other words, the vast majority of the time board members spend on innovation is spent on reviewing and approving budgetary expenditures, as opposed to working to shape a responsible adoption framework of AI.
Having said that, there is another shift when it comes to skills, as AI appears often to be “just” a new tool and not an organization imperative for the future. CEOs and CMOs alike do not necessarily need to know how to program or code but they do need to know enough about how AI frameworks work including the capabilities, the limitations and the risk to make decisions that are informed.
The companies that are most successfully setting off on journeys to adopt and develop AI are companies that are driving a fluency program for AI internally and embedding it in leadership development. For example, Schneider Electric launched a learning platform for AI targeted at senior managers, so that whatever project they are deciding to undertake that touches on sustainability or efficiency, they will be adopting AI in some capacity thereafter, with the clearest understanding possible of what AI can and cannot do.
AI Agents, Synthetic Data & Executive Readiness
In a business climate filled with ambiguity, resilience in AI often revolves around three building blocks: autonomous AI agents; synthetic data; and register-competent leaders. Autonomous AI agents are moving past the hype phase and gaining some initial traction in fields such as customer service, cyber security, and operations. However, their success is still dependent on how ready leaders are to provide supervision and to try to ensure that the implementation of these systems are undertaken responsibly.
With privacy legislation tightening in many jurisdictions, organizations are increasingly relying on synthetic data to train models that do not create exposure to the risks of disclosing sensitive information. In this way organizations can act on innovation while reducing compliance risks
All three building blocks have a leadership element. Boards of directors that can consider the implications for agency and risks associated with agentic AI and synthetic data are positioned best to create an AI strategy that strikes a balance between capability with oversight and speed with responsibility.
When adding AI to a corporate strategy, executives will do well to rely on three guiding ideas:
Coalescence instead of Isolation: AI initiatives must be driven by business goals, and not only confined to R&D
Governance and Flexibility: All AI initiatives must take into account ethical principles and data governance principles, while still permitting experimentation.
Shared ownership across functions: Not one function should own AI; multiple functions: marketing, operations, finance, HR, should share ownership.
The Future of AI in the C-Suite
Another important consideration is communication. The leadership team that regularly communicates the purpose of AI within its company’s strategy to all stakeholders (employees, investors, customers) generates trust and alignment most fully throughout the value chain.
The organizations that will be successful in the era of AI are those that understand leadership must evolve with technology. The appointment of CAIOs, the development of AI literacy across the C-suite, and the establishment of a scalable governance framework, are just some signs that AI is a business transformation mandate, not an IT project. For executives the question is not; should we have AI in the boardroom, it’s how fast can we adapt our leadership structure and take advantage of it.
