Artificial intelligence
The rise of artificial intelligence (AI) is profoundly transforming organizations. According to a PwC survey, many companies do not clearly identify a single person to steer AI: some start with one-off projects, while others directly appoint an executive to drive the company-wide AI strategy. Faced with the explosion of general models and the democratization of generative tools, the question arises: should a “Chief AI Officer” position be created? This 1500+ word article explores the arguments for and against the creation of a CAIO, compares the role to existing functions and offers recommendations.
AI adoption is no longer limited to a few prototypes: according to a survey conducted in early 2026, 93.6% of organizations have AI projects in production, and 97.3% derive measurable value from them. AI is thus becoming a strategic lever, and no longer a mere tool for experimentation. In this context, companies need a unified vision to align initiatives, guarantee data quality, ensure compliance and manage risks.
Traditional roles – chief information officer (CIO), chief technology officer (CTO) or chief data officer (CDO) – focus on infrastructure, technology or data respectively. Forbes observes that the position of CAIO is emerging to transform AI into a strategic advantage and act as an interface between legal, security, data and product teams. The CAIO has broader cross-functional authority than the CTO in terms of business transformation, but a narrower technological scope. His role is to define the AI roadmap, allocate resources and set safeguards (responsible AI).
Data and risk management is a priority. PwC points out that no existing role has both the strategic vision and technical understanding needed to select AI technologies, integrate them into the business and mitigate risk. According to the SHRM study, unifying the AI vision improves data governance and strengthens regulatory compliance, but the lack of a driver leads to siloed initiatives and uncertainty over accountability.
Unified vision and coordination: the CAIO puts an end to scattered initiatives by defining priorities, aligning investments and selecting appropriate technologies. It facilitates collaboration between business, technical and legal teams.
Risk management and compliance: ensures that projects comply with data protection regulations and ethical principles. The CAIO works with legal experts to set up appropriate governance frameworks, and acts as a privileged interlocutor with data protection authorities.
Accelerating innovation: by centralizing AI skills, the company creates a “center of excellence” capable of rapidly deploying high-impact projects and adapting models to local needs. Organizations with a CAIO report achieving faster and more sustainable value from their investments.
Culture and change: the JD Supra study notes that 93.2% of organizations cite cultural challenges and acceptance of change as obstacles to success. The CAIO, through its visibility, embodies a transformation project and raises teams’ awareness of responsible AI adoption.
Conflicts with the CIO/CTO/CDO: the addition of a new position can create rivalries. The CAIO may overlap with the CTO (technology) or CDO (data management). Clear governance and a precise definition of responsibilities are essential.
Cost and bureaucracy: creating an executive position adds a significant budget line. Smaller organizations may not have the necessary resources and prefer to integrate IA responsibilities into existing positions.
Role evolution: an article in CIO Magazine points out that the first CAIOs served mainly as symbols of the company’s intent. Now, the role is becoming more operational and production-oriented. The article suggests that the CAIO may fade away as AI becomes ubiquitous. The company therefore needs to plan a trajectory for integrating these responsibilities into the organization.
Reporting lines: according to Forbes, the place of the CAIO varies – some report to the CEO, others to the CIO/CTO. Without sufficient autonomy, the CAIO risks being relegated to the role of expert rather than decision-maker.
To assess the relevance of a CAIO, it is useful to compare its responsibilities with those of existing roles.
| Role | Main scope | Authority over AI | Strengths | Risks |
|---|---|---|---|---|
| CIO (Chief Information Officer) | Management of infrastructure, networks and systems | Partial: sometimes oversees infrastructure-level AI initiatives | Mastery of existing technologies | May lack product/business vision; priority given to stability |
| CTO (Chief Technology Officer) | Technological innovation, architecture and R&D | Partial: leads certain technical AI projects | Excellent technical expertise, close relationship with developers | May neglect business integration and compliance; lack of mandate for data governance |
| CDO (Chief Data Officer) | Data strategy, quality and governance | Central to data policy; collaborates with AI projects | Mastery of RGPD issues and data management | Less focused on models and uses; risk of silos |
| CAIO | AI strategy, integration and responsible innovation | Total: responsible for aligning AI with corporate strategy | Cross-functional vision, business coordination, risk management and compliance | Risk of duplication with CIO/CTO; emerging and poorly understood role; dependent on CEO support |
This comparison underlines that the CAIO is not a direct competitor to other frameworks, but a complement to orchestrate AI at enterprise scale.
The decision depends on the maturity of the organization, the nature of the projects and the availability of in-house skills. Here are a few scenarios:
Experimental phase: when the organization is testing AI in a limited scope, a steering committee under the responsibility of the CIO or CDO may suffice. The focus should be on data governance, compliance and skills acquisition.
Deployment phase: when several business units launch AI projects and the company relies on complex models (LLM, vision, etc.), the creation of a CAIO position becomes relevant. It will help avoid redundancies, pool models and guarantee strategic alignment.
Industrialization phase: when AI becomes an integral part of products and services, the question arises as to whether the CAIO should remain. Some experts believe the role can merge with the CIO or CDO when AI is no longer a separate domain.
Highly regulated sectors (finance, healthcare, public sector): a CAIO is recommended to oversee compliance with specific requirements (e.g. BCBS 239, DORA regulations, healthcare law). The CAIO must collaborate with legal experts and the DPO to ensure traceability and explicability of models.
Technology-intensive sectors (e-commerce, media, industry 4.0): a CAIO can accelerate production start-up by identifying high-value use cases and optimizing infrastructure. It also ensures that algorithms are responsible and that consumers are protected.
Large organizations: organizational complexity justifies a dedicated role, able to arbitrate between departments and negotiate budgets. The CAIO acts as an AI sponsor on the executive committee.
SMEs and SMIs: the creation of a CAIO is not systematic. The functions can be performed by the CIO or by a data/innovation manager. According to the Privacy 2026 barometer, 35% of participants are SMEs and 41% are ETIs; only 31% have designated a DPO as the AI governance pilot. A CAIO could therefore be an asset in structuring this governance.
Clearly define the mandate: specify the scope (data, models, platforms) and establish a governance charter shared with the CIO, CTO and DPO. The mission must include strategy definition, project prioritization and risk management.
Establish performance indicators: measure value creation, speed to production, model quality and ethics. These indicators must be shared with senior management.
Strengthen cross-functional collaboration: the CAIO must work with business departments to identify needs and ensure that solutions meet expectations. Team training and ethics awareness are essential.
Ensure robust data governance: implementation of a data catalog, security policies, annotation and audit procedures. These actions meet the need to comply with the RGPD and CNIL recommendations.
Preparing for the future: anticipating the integration of the role into the structure. If AI becomes ubiquitous, the CAIO’s mission may be absorbed by a chief digital officer or chief transformation officer.
Frequently asked questions :
What is a Chief AI Officer? This is an executive responsible for defining the company’s AI strategy, coordinating AI projects, managing risk and ensuring regulatory compliance. He or she collaborates with business, IT, data and legal departments to maximize the value of AI.
Why create a CAIO post? The multiplication of AI projects demands a unified and responsible vision. A CAIO aligns investments, pools resources, accelerates innovation and ensures compliance with regulatory requirements such as RGPD.
Is the CAIO’s role sustainable? The position is evolving: initially symbolic, it is becoming more operational. In the long term, its missions could be integrated into other functions once AI is fully infused into the organization.
Can we do without a CAIO? For small organizations or those where AI is marginal, the responsibilities can be given to existing managers. However, in highly regulated sectors, or when AI becomes strategic, a CAIO is recommended.
The creation of a Chief AI Officer position is not a one-size-fits-all solution, but a response to the growing complexity of AI and the importance of coordinating initiatives. Organizations that do so benefit from a strategic vision and improved data governance. However, success depends on defining a clear mandate, collaborating with other executives and gradually integrating AI into the company’s DNA.