AI technologies, particularly generative AI, have the potential to revolutionalise businesses from frontline decision making to back-of-the-house operations. Most companies started exploring AI use cases in 2024, and some had taken significant steps towards active deployment and integration of AI into their existing businesses and workflows, in 2025.
Amongst the first considerations will be the operating model, which will largely depend on the business objectives the management wants to achieve with AI. Based on a poll ran by Kerry Consulting in August 2025, 44% believe that the Chief AI Officer should sit within the CEO’s remit. Interestingly, only 11% mentioned the CAIO should report to the Chief Data Officer.
Importance of a top-down, enterprise-wide strategy
For some companies which are more ahead of the curve, they have established AI councils where the participants are usually part of the key core management team. The Chief AI Officer or the CEO (in absence of the Chief AI Officer) typically chairs the meeting. The AI council is ultimately responsible for the top-down, enterprise-wide AI strategy. When well-planned and executed, this holistic approach to AI yields multiple benefits:
- Reduction in duplication of efforts. Without a holistic strategy, different business units start to do their own ground up AI work which costs more, and typically yields poorer results.
- Better AI governance as there is a standardised framework around vendor selection, tool usage, and data governance. Setting the right parameters allow for employees to explore new technologies and solutions more freely and safely.
- Clarity in the tone from the top. There is a real fear amongst employees who do not understand AI that it will eventually take over their jobs. If the company is open about its AI strategy (including what it means for the workforce), the employees will be able to adjust and transition accordingly. Trust is then established, and that in turn promotes a healthier work environment.
Responsibilities of a Chief AI Officer
We have observed a growing need for a Chief AI Officer (CAIO) in corporations, especially those undergoing digital transformation or heavily leveraging AI for competitive advantage. While some organizations may still consolidate this role under a Chief Data Officer (CDO) or Chief Technology Officer (CTO), the distinct strategic importance and complexity of AI often justifies a standalone executive function.
Differences Between Chief AI Officer (CAIO) and Chief Data Officer (CDO)
| Aspect | Chief AI Officer (CAIO) | Chief Data Officer (CDO) |
|---|---|---|
| Primary Focus | Strategic development, deployment, and governance of AI across the organization | Data governance, data quality, architecture, and compliance |
| Key Responsibility | Driving enterprise-wide AI adoption and innovation | Ensuring data availability, accuracy, integrity, and security |
| Scope | AI/ML models, generative AI, automation, AI ethics, AI tooling, AI ops | Data infrastructure, master data management, data lakes/warehouses, data privacy |
| Goals | Business transformation via AI; operational efficiency, new revenue via AI solutions | Ensure data is a trusted, usable asset to support BI, analytics, and regulatory needs |
| Technical Skills | AI/ML systems, model lifecycle management, AI infrastructure, emerging AI trends | Data modeling, data governance frameworks (e.g., DAMA), compliance (GDPR, HIPAA) |
| Ethics & Risk | Responsible AI, fairness, explainability, model bias mitigation | Data privacy, consent management, data lineage and traceability |
| Collaboration | Works closely with Product, IT, Legal, HR, and CDO | Works closely with IT, Security, Compliance, and CAIO |
Deciding on your Chief AI Officer
CEOs are looking for AI chiefs who are able to cut through hype about genAI, and truly understand how it can enable the business. These Chief AI officers typically love the technical aspects of the technology and excel in deploying the technology in a commercial manner in a corporate world. That commercial acumen on translating deep tech into business is key.
With newly created roles, there is usually a conundrum between looking internally or externally for the right candidate. Whether to appoint a Chief AI Officer (CAIO) internally or hire externally depends on several factors including your company’s AI maturity, culture, industry, and strategic goals.
| Key Consideration | Internal Candidate | External Candidate | Insights |
|---|---|---|---|
| Company AI maturity | Medium–High | Low–Medium | Internal leaders thrive if AI is already embedded. |
| Need for fresh AI vision / disruption | Low | High | External hires bring broader, diverse experience. |
| Understanding of business model | High | Low–Medium | Internal leaders typically understand org dynamics better. |
| Speed to impact | Fast | Slower | Ramp-up time is shorter for internal appointments. |
| Cultural alignment / stakeholder trust | High | Variable | Cultural fit is a common risk for outside hires. |
| Global AI networks / external credibility | Limited | Strong | External candidates often bring broader industry recognition. |
| AI-specific leadership experience | Variable | Proven | Depends on internal talent pool maturity. |
| Ability to build AI talent pipeline | Developing | Established | External leaders may bring their team or vendor relationships. |
Our Data & AI Team:
Sherry Zerh
Senior Director, Technology
Shreeya Bhan
Senior Consultant, Technology