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Chief AI Officer

Location: [Location or Remote] Reports to: Chief Executive Officer Team size: [Adjust based on organizational size] Compensation: [Salary range]

Bottom Line Up Front

We need someone who will build and lead a team that helps every employee in this organization use AI effectively, not someone who writes strategy decks for executives.

You will build an AI Center of Excellence team that lights a thousand small fires across the organization. You will hire AI Business Partners who embed with departments, recruit AI Ambassadors from every team, and establish the governance and training infrastructure for sustained adoption. You will measure success by how many people use AI weekly, not by how many strategy presentations you create.

If you want to be a visionary who talks about AI transformation, this is not the role. If you want to build the team and programs that drive capability across 1,000 employees and deliver daily AI wins, keep reading.

What Makes This Role Different

Most Chief AI Officer job descriptions talk about ‘driving AI strategy’ and ‘transforming the organization’ without explaining what that actually means. This section clarifies what we mean by tactical AI leadership and how it differs from other technology executive roles.

Not a Chief Information Officer Role

A Chief Information Officer manages IT infrastructure, security, and enterprise systems. This role is not about managing servers or implementing enterprise resource planning systems. This role is about making AI accessible and useful for everyone.

Not a Chief Technology Officer Role

A Chief Technology Officer leads product development and engineering teams. This role is not about building AI products for customers. This role is about enabling internal teams to use AI for their work.

What This Role Actually Is

You will build and lead the AI Center of Excellence. You will hire AI Business Partners to embed with departments, recruit and train AI Ambassadors across teams, and establish governance frameworks that enable safe experimentation. You will select the right tools, design the change enablement program, and create the training infrastructure that makes AI accessible to everyone.

Your team will light a thousand small fires across the organization, not one big fire at the executive level.

Core Responsibilities

These responsibilities are organized by where you will spend your time, not by abstract strategy categories. Notice the specific, measurable outcomes for each area.

Build AI Center of Excellence and Enablement Team (40% of your time)

Recruit, hire, and lead the team that enables AI adoption across the organization.

You will:

• Hire and manage AI Business Partners who embed with departments (1 per 200-500 employees)
• Recruit and train AI Ambassadors program (5-10% of employees as champions)
• Build training and facilitation team to deliver workshops and coaching
• Establish AI support desk and office hours structure
• Create governance and compliance team for policy and risk management
• Design and oversee the change enablement program
• Manage communications specialists for internal AI messaging

Success looks like: AI Center of Excellence team fully staffed and operating independently. 70% of employees using AI tools weekly within 12 months. Teams creating their own AI solutions without needing centralized support.

Tool Selection and Platform Strategy (25% of your time)

Select, license, and manage the AI platforms and tools that enable organizational success.

You will:

• Evaluate and select primary AI platforms (ChatGPT Enterprise, Claude, etc.)
• Establish vendor relationships and manage licensing
• Create tool approval process for new AI applications
• Design role-based access and data controls
• Plan integration roadmap with existing systems
• Monitor usage, costs, and performance metrics
• Manage vendor relationships and service level agreements

Success looks like: Clear, approved AI tool stack. Fast approval process for new tools (under 2 days). Integrated platforms that connect to company knowledge and systems. Documented cost efficiency and usage patterns.

Change Enablement Program Design (20% of your time)

Design and oversee the programs that drive adoption and capability building.

You will:

• Design training curriculum (AI 101, prompt engineering, role-specific workshops)
• Establish communications strategy and internal messaging calendar
• Create AI Ambassador and AI Business Partner programs
• Build prompt libraries and use case repositories
• Design measurement frameworks and dashboards
• Develop recognition programs for AI innovation
• Oversee pilot programs and phased rollout strategy

Success looks like: Comprehensive change program running independently through your team. Employees teaching other employees how to use AI. Documented best practices and use cases shared organization-wide.

AI Governance and Risk Management (10% of your time)

Establish governance frameworks and policies that enable safe, compliant AI use.

You will:

• Develop AI usage policies and responsible AI frameworks
• Create data classification and handling guidelines
• Establish AI ethics committee and review processes
• Design risk assessment and mitigation strategies
• Build compliance monitoring and audit processes
• Create incident response playbooks for AI-related issues
• Balance risk management with innovation enablement

Success looks like: Clear governance framework approved by Legal and Compliance. AI usage policies that enable experimentation within guardrails. Documented compliance with industry regulations and data protection requirements.

Executive Communication and Strategy (5% of your time)

Keep leadership informed about AI progress and opportunities.

You will:

• Report monthly on AI adoption metrics and business impact
• Recommend AI tool investments and budget allocation
• Present quarterly AI capability reviews to the board
• Identify competitive AI risks and opportunities
• Connect AI initiatives to business objectives

Success looks like: Executives understand what is working and why. Board sees clear return on AI investment through adoption metrics and business outcomes.

First 90 Days Deliverables

Strong candidates will know exactly how to approach these deliverables. Weak candidates will want to ‘assess the landscape’ for six months before doing anything.

Month 1: Discovery and Foundation

• Complete organizational AI readiness assessment and use case discovery
• Define AI Center of Excellence structure and roles needed
• Select and license primary AI platform (ChatGPT Enterprise, Claude, etc.)
• Create initial AI usage policy and governance framework (approved by Legal)
• Begin hiring process for first AI Business Partners
• Establish pilot group and deploy tools to early adopters
• Present initial findings and 12-month roadmap to executive team

Month 2: Team Building and Program Design

• Hire first 2-3 AI Business Partners
• Recruit and train initial AI Ambassador cohort (20-30 people)
• Design training curriculum and change enablement program
• Create communications strategy and launch internal messaging
• Establish AI support desk and office hours schedule
• Deploy tools to first pilot department with full support
• Document first quick wins and success stories

Month 3: Scale and Measurement

• Complete AI Center of Excellence team hiring plan
• Roll out to additional departments with AI Business Partner support
• Launch formal training programs through your team
• Establish measurement dashboards and reporting cadence
• Create prompt libraries and use case repositories
• Present 90-day results showing adoption metrics and business impact
• Deliver detailed next quarter implementation plan

Required Qualifications

These qualifications focus on what someone has actually done, not just what they know. Look for evidence of hands-on work, not just strategic leadership.

Must Have

• Hands-on AI tool experience: Daily active use of ChatGPT, Claude, or similar tools for at least 12 months. Can demonstrate advanced prompt engineering, custom tool creation, and practical implementation knowledge. Must be able to coach others on effective AI use.

• Team building and leadership: Proven track record of building and leading cross-functional teams. Experience hiring, developing, and managing diverse talent. Ability to create new roles and organizational structures.

• Change enablement expertise: Demonstrated success leading organizational change programs. Experience designing and implementing training, communications, and adoption strategies. Background in change management or transformation initiatives strongly preferred.

• Program design and execution: Ability to design comprehensive programs from strategy through implementation. Portfolio of deployed initiatives with measurable business impact and sustained adoption.

• Cross-functional collaboration: Experience working with and influencing stakeholders across IT, Legal, HR, Finance, and business units. Comfort navigating complex organizational dynamics and building consensus.

Strongly Preferred

• Experience in [your industry] with understanding of [industry-specific processes]
• Track record of building Centers of Excellence or enabling functions from scratch
• Background in organizational development, learning and development, or transformation consulting
• Technical background (engineering, data science, software development) with ability to evaluate AI platforms
• Experience deploying technology in regulated environments with compliance requirements
• Previous P&L responsibility or budget management experience (managing multi-million dollar programs)
• Network of AI practitioners and ability to attract top talent to the team

Not Required

• PhD in machine learning (nice to have, not necessary)
• Experience building AI models from scratch (this is not a research role)
• Software engineering background (helpful but not essential)
• Previous C-suite experience (strong directors and vice presidents encouraged to apply)

Success Metrics

These metrics focus on organizational adoption and business impact, not personal visibility or strategy documents produced.

Primary Metrics (reviewed monthly)

• Adoption breadth: Percentage of employees using AI tools weekly
• Adoption depth: Number of departments with deployed AI solutions
• Business impact: Total hours saved across organization through AI use
• Quick wins delivered: Number of deployed solutions with measured impact
• Training reach: Number of employees trained on practical AI use

Secondary Metrics (reviewed quarterly)

• Capability development: Percentage of teams creating their own AI solutions
• Tool effectiveness: Average time savings per deployed solution
• Risk management: AI-related incidents and resolution time
• Cost efficiency: AI tool costs versus measured productivity gains
• Employee satisfaction: Team confidence and comfort with AI tools

What We Will Not Measure

• Number of strategy presentations created
• AI roadmap documents completed
• Executive meetings attended
• Industry conferences spoken at
• Thought leadership articles published

These activities may happen, but they are not how we measure your success.

Compensation and Benefits

Be transparent about compensation range and what influences the final offer. Strong candidates want to know this upfront.

Base compensation: $[X] to $[Y] depending on experience Total compensation: $[X] to $[Y] including bonus and equity

What influences compensation:

• Proven track record of grassroots AI adoption (not just strategy work)
• Industry-specific experience and knowledge
• Size and complexity of previous organizations
• Demonstrated quick win deployment capability
• Strength of references from non-technical stakeholders

Benefits:

• [Standard benefits package]
• [AI tool budget and access]
• [Professional development budget]
• [Other relevant benefits]

Before You Hire: Consider These Questions

Hiring a Chief AI Officer is a significant investment of time and money. Make sure this is the right approach for your organization.

Do you actually need a Chief AI Officer?

Consider hiring if:

• Your organization has 1,000+ employees
• You have budget for $300,000 to $500,000 total compensation
• You can wait 6 to 12 months for recruitment and onboarding
• You need industry-specific AI expertise
• You want to build permanent internal AI capability

Consider alternatives if:

• Your organization has fewer than 1,000 employees (may need AI capability, not a full executive)
• You need AI leadership immediately (recruitment takes months)
• You want to test AI adoption before committing to an executive hire
• You are unsure what success looks like for this role
• Budget is constrained but AI leadership is still needed

What are the alternatives?

Digital Chief AI Officer platforms provide executive-level AI expertise immediately, cost significantly less than hiring, scale with your needs, and give you time to learn what you actually need from this role.

AI consultants can provide project-based support for specific initiatives without the commitment of a permanent executive.

Upskilling existing leaders may work if you have someone with the right skills and interest who can take on AI leadership as part of their current role.

How will you know if this hire is successful?

Define success criteria before you hire. If you cannot clearly articulate what this person will deliver in 90 days, 6 months, and 12 months, you are not ready to hire for this role.

How to Apply

[Your standard application instructions]

In your application, please include:

1. A specific example of building and leading a team that enabled organizational change
2. Your approach to designing an AI Center of Excellence for our organization (structure, roles, first 90 days)
3. An example of a change enablement program you designed and the adoption metrics you achieved
4. How you would measure success for this role in 6 months and 12 months
5. The last prompt you wrote and why it worked (we want leaders who use AI daily, not just talk about it)

We care more about what you have built and led than where you worked or what degrees you hold.

Implementation Notes

When implementing this job description:

1. Customize the compensation range based on your geographic location, organization size, and market conditions. Research current Chief AI Officer compensation in your industry.

2. Adjust the team size and scope based on your organization. A company with 1,000 employees needs a different approach than a company with 10,000 employees.

3. Add industry-specific context throughout the description. A Chief AI Officer in healthcare faces different challenges than one in manufacturing or financial services.

4. Modify the success metrics to align with your business objectives. Add industry-specific metrics that matter to your organization.

5. Be honest about your AI maturity in the role description. Candidates want to know if they are building from scratch or scaling existing efforts.

6. Include your AI tool stack so candidates know what platforms and tools you currently use or plan to use.

7. Clarify the reporting structure and make clear this role has executive authority to drive change across departments.

Remember this is a starting point - adapt it based on your:

1. Organization size and structure
2. Industry and regulatory environment
3. Current AI maturity and existing initiatives
4. Geographic location and talent market
5. Budget and compensation philosophy
6. Timeline and urgency for the hire
7. Specific business challenges AI should address