Chief AI Officer Job Description Template

Role Summary

The Chief AI Officer (CAIO) is a C-suite executive responsible for [COMPANY]'s artificial intelligence strategy, governance, and cross-functional adoption. Reporting to [CEO / CTO / Board], the CAIO defines how AI creates business value, ensures AI systems meet regulatory and ethical standards, and drives AI literacy across the organization. This is not a pure technology role — the CAIO bridges AI capabilities with business strategy, risk management, and organizational change.

Reporting Structure

• Reports to: [CEO for standalone AI mandate / CTO for technology-integrated mandate]
• Direct reports: VP of AI/ML, AI Ethics Lead, AI Platform Engineering Lead
• Key stakeholders: CTO, CDO/CDAO, General Counsel, Business Unit Heads, Board of Directors

Core Responsibilities

1. AI Strategy & Roadmap

• Define the 1–3 year AI strategy tied to business objectives.
• Prioritize AI use cases by impact and feasibility.
• Set success metrics and KPIs for AI initiatives.
• Present AI strategy and progress to the board quarterly.

2. AI Governance & Compliance

• Establish AI governance policies, model review boards, and approval workflows.
• Ensure compliance with the EU AI Act, NIST AI RMF, and industry-specific regulations [ADAPT].
• Own the organization's AI risk register.
• Coordinate with Legal and Compliance on AI-related regulatory obligations.

3. Model Lifecycle Management

• Oversee the end-to-end ML pipeline from data acquisition to production inference.
• Define standards for model versioning, monitoring, drift detection, and retirement.
• Ensure reproducibility and auditability across all production models.

4. Responsible AI & Ethics

• Build and enforce bias testing, fairness metrics, and transparency standards.
• Establish processes for AI incident response and model failure management.
• Ensure AI decisions are explainable to customers, regulators, and the public.

5. Vendor & Platform Strategy

• Evaluate and select foundation models, AI platforms, and tooling.
• Manage enterprise AI vendor relationships and negotiate agreements.
• Prevent vendor lock-in through architectural standards and abstraction layers.

6. Cross-functional AI Adoption

• Drive AI literacy programs across non-technical business units.
• Build centers of excellence and identify high-value AI use cases in operations, finance, HR, and customer experience.
• Measure and report on organization-wide AI adoption metrics.

Required Background

The ideal candidate brings one of three proven career tracks:

• Track 1 — ML/AI Leadership: 10+ years in machine learning or AI, including 3+ years leading AI teams at the VP or SVP level. Deep technical understanding of model development, deployment, and governance.
• Track 2 — Management Consulting / Strategy: 10+ years in technology strategy consulting (McKinsey, BCG, Bain, or equivalent), with a specialization in AI transformation and digital strategy. Strong executive communication and board-facing experience.
• Track 3 — CTO / VP Engineering: 12+ years in engineering leadership, with significant AI/ML program ownership. Experience scaling AI from R&D to production. Understanding of both the technical and organizational dimensions of AI adoption.

Preferred Qualifications

• Advanced degree in Computer Science, AI, or a related field.
• Experience in [INDUSTRY — financial services, healthcare, etc.].
• Familiarity with AI regulatory frameworks (EU AI Act, NIST AI RMF, FDA AI/ML).
• Published research or public speaking on AI strategy.
• Experience managing AI budgets exceeding $[X]M.

Compensation

Base salary: $[220,000–350,000] depending on company stage and location.
Total compensation: $[350,000–600,000+] including equity, bonus, and benefits.
See detailed benchmarks: ctaio.dev/en/salary/chief-ai-officer-salary/