Chief AI Officer (CAIO)

Title	Chief AI Officer (CAIO)
Department	Executive Leadership / Technology / AI Strategy
Reports to	[Chief Executive Officer / Chief Operating Officer / Chief Technology Officer]
Location	[Remote / Hybrid / On-site]
Employment type	Full-time
Salary	[Salary range. Postings with a range perform significantly better, and several states require one.]

Position overview

The Chief AI Officer (CAIO) provides executive leadership for [Company]'s enterprise artificial intelligence strategy, adoption, and governance. This role owns the vision for how AI is built, bought, deployed, and controlled across the organization, translating business objectives into a coherent AI roadmap.

The CAIO partners closely with executive leadership, technology, data, product, legal, compliance, privacy, cybersecurity, and business units to ensure AI investments deliver value while remaining safe, ethical, and aligned with regulatory requirements. The role balances speed of innovation against responsible deployment.

As a senior executive and public face of the organization's AI program, the Chief AI Officer builds the talent, platforms, and governance structures needed to scale AI responsibly and represents AI strategy to the Board, customers, regulators, and partners.

Key responsibilities

Enterprise AI strategy

• Define and own the enterprise AI vision, strategy, and multi-year roadmap.
• Prioritize AI investments and use cases against business value and risk.
• Establish the operating model for building, buying, and integrating AI.
• Set enterprise standards for AI platforms, tooling, and data foundations.
• Present AI strategy, progress, and outcomes to executive leadership and the Board.

AI adoption and value delivery

Drive responsible AI adoption across the business, including generative AI, machine learning, and AI agents. Focus on:

• Identifying and scaling high-value use cases across functions
• Measuring return on AI investment and adoption outcomes
• Enabling teams with platforms, patterns, and reusable capabilities
• Building AI literacy and change management across the workforce

Responsible AI and governance

• Establish responsible AI principles, policies, and enterprise standards.
• Sponsor or chair the AI Governance Committee and approval processes.
• Ensure AI systems undergo appropriate review before deployment.
• Partner with risk, legal, privacy, and security to embed controls by design.
• Maintain an AI use-case inventory and executive reporting.

Regulatory alignment

Ensure the AI program aligns with the EU AI Act, the NIST AI Risk Management Framework, ISO/IEC 42001, ISO/IEC 23894, the OECD AI Principles, sector regulation, and emerging state and global AI legislation.

Data and technology foundations

• Partner with data and technology leaders on data readiness and quality.
• Set direction for AI infrastructure, model management, and MLOps.
• Guide platform and vendor decisions for enterprise AI capability.
• Ensure security and privacy requirements are built into AI systems.

Talent and organization

Build and lead the AI function, including data scientists, machine learning engineers, AI product leaders, and responsible AI specialists. Develop hiring plans, capability models, and career paths, and cultivate a culture of responsible innovation.

External engagement

Represent the organization's AI strategy to the Board, customers, partners, regulators, industry bodies, and the public, and monitor the AI market for opportunities, threats, and emerging practice.

Required qualifications

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Systems, Business, or a related discipline. Master's degree or MBA preferred.
• 15 to 20+ years of progressive experience across technology, data, analytics, or AI, including senior leadership roles.
• Demonstrated experience defining and executing an enterprise AI or data strategy at scale.
• Experience briefing executive leadership and Boards of Directors.
• Strong understanding of AI and machine learning technologies, including generative AI and large language models.
• Experience balancing innovation with responsible AI governance and regulatory requirements.

Preferred certifications

One or more of: AIGP, ISO/IEC 42001 Lead Implementer or Lead Auditor, PMP, CDPSE, TOGAF, and executive AI or data leadership programs from recognized institutions.

Technical knowledge

Enterprise AI strategy, generative AI, machine learning, large language models, AI agents, responsible AI, AI governance, data strategy and governance, MLOps and model lifecycle management, AI platform architecture, product management, vendor and platform evaluation, AI security and privacy, and regulatory alignment.

Essential competencies

Executive leadership, strategic vision, executive and Board communication, change leadership, commercial judgment, ethical decision making, cross-functional influence, talent development, and comfort operating amid ambiguity.

Success measures: first 12 months

• Publish an enterprise AI strategy and prioritized roadmap.
• Stand up responsible AI principles, policies, and governance.
• Launch or scale a portfolio of high-value AI use cases.
• Establish an AI use-case inventory and executive reporting.
• Build the AI operating model and core team.
• Define enterprise AI platform and data foundations.
• Align the AI program to applicable AI regulation.
• Raise organizational AI literacy and adoption.

About [Company]

[Two or three sentences about your organization, the maturity of your program, and what the first year looks like. Candidates in this field respond to honesty about whether they are joining a build or an established function.]