Do you have a VP of AI?
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AI is changing quickly, and keeping up with it cannot just be everyone's responsibility. When it belongs to everyone, it often belongs to no one.
Organizations should appoint a VP of AI: someone who is responsible for staying current with AI developments, experimenting with new tools and approaches, sharing what they learn, and helping bring the whole organization up to a strong AI baseline.
✅ Figure: Good example - A VP of AI on LinkedIn, responsible for driving AI adoption across the organization
What does a VP of AI do?
A VP of AI should:
- Stay current – Keep up with important AI models, tools, capabilities, and industry changes
- Experiment – Regularly test new AI tools and identify where they can provide real value
- Share knowledge – Communicate useful discoveries, workflows, and lessons with the organization
- Set the baseline – Define the minimum level of AI knowledge and usage expected across teams
- Help teams adopt AI – Find opportunities where AI can improve productivity, quality, or customer outcomes
- Be the go-to person – Give employees a clear person to approach with AI questions, ideas, and opportunities
- Set standards – Establish guidance around approved tools, security, privacy, quality, and responsible AI use
- Measure progress – Track whether AI adoption is actually producing useful business outcomes
Tip: The VP of AI does not need to personally implement every AI solution. Their job is to make sure the organization is learning, experimenting, and improving rather than leaving AI adoption to chance.
Build an AI baseline
Having a few employees who are excellent with AI is not enough.
The goal should be to continuously raise the organization's AI baseline, so useful knowledge moves from individual early adopters to everyone who can benefit from it.
For example, when the VP of AI discovers a workflow that saves hours of repetitive work, that knowledge should be documented, demonstrated, and shared rather than remaining with one person or team.
Different teams experiment with AI independently, knowledge is not shared, and nobody knows which tools or practices the organization recommends.
❌ Figure: Bad example - AI adoption is fragmented, with no clear owner or shared direction
The VP of AI regularly evaluates new developments, shares useful findings, establishes recommended practices, and helps teams apply them to their work.
✅ Figure: Good example - A VP of AI drives consistent AI adoption and knowledge sharing across the organization
From VP of AI to Chief AI Officer
As AI becomes more important to an organization's strategy, the role may naturally become a C-suite position: the Chief AI Officer (CAIO).
A VP of AI can initially focus on adoption, education, experimentation, and standards. As AI becomes embedded in products, operations, strategy, and decision-making, executive-level ownership becomes increasingly valuable.
The important part is not the exact title. It is having one clearly accountable leader whose job is to ensure the organization keeps moving forward with AI.