Before You Hire (or Become) an AI TPM

A Readiness Framework

I’ve seen AI Technical Program Managers (TPMs) and enablement roles proliferate across LinkedIn. Companies are looking to hire “the AI person” who will drive adoption and transformation across their organizations. But this kind of role can mean a dozen different things – some expect one person to handle advocacy, implementation, vendor management, training, metrics, and roadmapping all at once. Others have more focused mandates with clear support structures. Either way, are these companies ready for what they’re hiring this person to do? Do you know what kind of AI enablement approach actually fits your company’s current state?

As a TPM who managed a small team within a Platform org, now an IC embedded on an ML team and a GenAI Champion within my company, I want to offer a framework to help assess organizational readiness before hiring for these roles.

Scope, Support, and Sustainability

When you look at AI Program or Enablement job descriptions, you’ll find wide-ranging responsibilities. It’s common to see roles that expect someone to:

• Advocate for AI usage across technical and non-technical teams

• Identify opportunities and develop AI solutions

• Manage vendor relationships and negotiations

• Design and deliver training programs

• Define and track success metrics

• Build and execute the organizational AI roadmap

But the key question I’ve pondered is: what’s realistic for one person to accomplish, vs what actually needs distributed ownership?

I don’t think this is unique to AI enablement. Whenever we have transformation initiatives, like with DEI, there’s someone hired to “own” it all, with overwhelming scope to solve a systemic challenge. That’s not to say these roles won’t be successful, but that they need to have proper support so they don’t burn out because transformation involves more than a single person.

There are also questions about sustainability of these roles:

1. What happens after initial adoption stabilizes?

2. How does this role evolve as AI becomes more ubiquitous?

3. Is this a transformation role with a defined end, or an ongoing function?

These answers affect whether the investment makes sense for your organization.

The AI Enablement Maturity Framework

Rather than thinking about AI enablement as a single role, it helps to think about it through stages of organizational maturity.

Stage 0: Awareness

What This Might Look Like:

Your leadership team has seen the headlines about AI and recognizes it’s important. There’s general agreement that you should “do something” with AI, but you don’t know what that looks like yet.

Key Consideration:

At this stage, you may need strategic clarity before operational execution. The risk is hiring a program manager when what you actually need is strategy development. A consultant or advisor who can help you define goals, assess capabilities, and create a roadmap might be more valuable than a full-time hire who’ll spend months just trying to get direction and buy-in.

Are you looking for strategy development or program execution?

Stage 1: Organic Experimentation

What This Might Look Like:

People are already experimenting with AI tools and your employees are already trying ChatGPT, Claude, and Copilot in their own time. Ground-level enthusiasm already exists.

What you’re probably experiencing instead are questions like: Which tools should we standardize on? Who should have access? How do we handle security/legal/privacy reviews? The biggest blockers aren’t about desire or ideas; they’re about technical enablement.

Instead of hiring a formal AI lead right away, identify your internal champions: the people already passionate and experimenting with AI. Create a space for them without killing curiosity. The company learns more this way than by dropping in an outsider to “figure it out.”

Context Matters:

The path through this stage looks different depending on your organizational structure. Larger organizations face more bureaucracy. Security reviews, legal approvals, business case requirements – all of these can stretch the timeline from idea to implementation by months. You might have more resources and specialists, but you’ll move slower.

Smaller companies can move faster with less red tape, but they have fewer in-house experts to collaborate with and may be tempted to pile everything onto one person because the team is lean.

Regardless of size, if you want to integrate AI beyond just using chatbots - connecting to your existing systems via MCP or other integrations - that takes time and technical infrastructure. Focus on enablement infrastructure before focusing on headcount.

Is a formal AI TPM hire actually needed at this stage? Or would empowering your existing champions work better?

Stage 2: Coordinated Pilots

What This Might Look Like:

Multiple teams are now running AI experiments. You’re starting to see duplication of effort, missed opportunities to share learnings, and a need for coordination. Teams are beginning to integrate AI into internal systems, and some patterns are emerging around what actually works.

This is where a formal AI TPM hire starts to make sense, but only if certain prerequisites exist.

What Makes a Hire More Likely to Succeed:

1. Existing Infrastructure

You need more than general enthusiasm from leadership. Is there actual budget allocated – not just “go figure it out and we’ll see”? Have tooling decisions been made or at least narrowed down significantly? Are security and legal frameworks for AI tool usage established, or will this person spend six months just trying to get approval to do anything?

2. Partnership Network

Who will this person actually partner with day-to-day?

Consider your in-house ML and AI experts. Do you have teams working on machine learning for your product (like recommendation systems, monetization algorithms, safety models)? Those people could be invaluable advisors for internal AI initiatives, even if that’s not their primary job. Are they available and willing to collaborate?

What about your platform or devops team? Do they have capacity to support integration work, or are they already underwater with business-as-usual requests?

Is there an L&D or training partner identified who can help with content development and rollout?

Answering these questions will help you determine if the AI TPM will just spend months trying to find partners and build relationships.

3. Realistic Scope

Be explicit about what IS in scope for this role, and equally important, what is NOT.

This person should be an orchestrator and enabler, not a doer-of-everything. They’re not going to single-handedly evaluate every vendor, build all the training materials, implement all the integrations, define all the metrics, and drive all the adoption. That’s not realistic, and expecting it sets everyone up for disappointment.

What This Role Might Focus On:

Think about this role as someone who:

• Facilitates knowledge sharing across teams running experiments

• Builds trust and credibility with both skeptics and enthusiasts

• Removes blockers that are preventing teams from moving forward

• Supports and amplifies existing champions rather than replacing them

• Helps establish “AI-by-design” thinking across the organization

• Coordinates efforts to avoid duplication while encouraging experimentation

They don’t “own AI” — they enable others to use it responsibly and effectively.

Stage 3: Scaling Adoption

What This Might Look Like:

AI adoption is working in pockets across your organization. Now you need to systematize it: spread the knowledge, establish patterns, and scale what’s working without losing momentum.

At this stage, the risk is actually that your AI TPM becomes a bottleneck if you’re not careful. If everything flows through one person, you’ll slow down instead of speeding up.

One Potential Model: Distributed Champions

One approach worth considering is a distributed champion model, similar to how some companies approach Security Champions or other specialized roles.

The structure might look like:

• Each org nominates someone to spend ~10% of their time as a local advocate, with term limits to prevent burnout and spread knowledge over time

• Champions selected based on enthusiasm but through a formal vetting process to ensure accountability and fairness

• Training program co-developed by the AI TPM and an L&D partner

• The TPM coordinates training, connects champions, and curates what works.

Champion Responsibilities:

These champions would be the first point of contact for their team’s questions, help identify opportunities where AI could help their specific team context, and escalate blockers to the AI TPM for support.

Importantly, these shouldn’t just be enthusiastic facilitators; they should have hands-on experience with AI tools and understand general best practices. They need to be practitioners who can actually guide others, not just cheerleaders.

The AI TPM’s Role in This Model:

At this stage, the AI TPM’s job evolves to:

• Supporting and coordinating champions (not managing them - they don’t report to the TPM)

• Curating and vetting suggested resources and approaches

• Connecting champions across orgs so they can share learnings

• Maintaining momentum and identifying opportunities for scaling

• Helping measure impact

At this level of maturity, it’s worth asking: how does this role change as AI becomes standard? Does it morph into general emerging tech coordination? Does it scale down as the distributed model matures? Does it sunset entirely?

This should be discussed up front, not figured out later.

Assessment Questions for Hiring Managers

Before you write that job description, take time to assess where you are. Ask yourself:

1. Role Trajectory

Is this a transformation role with a defined arc, or an ongoing function? How might this role evolve as AI becomes more standard in your organization? What’s the career path for this person beyond the initial adoption phase?

2. Existing Infrastructure

Do you have executive support that goes beyond general enthusiasm? Is there actual budget allocated for tools, training, and programs? How far along are your tooling decisions – have you selected vendors, or is evaluating and selecting tools part of this person’s job too?

3. Partnership Ecosystem

Who will this person partner with on a day-to-day basis? Are ML and AI experts in your company available and willing to collaborate? Does your platform or devops team have capacity to support integration work? Is there an L&D partnership available for training development?

Are there existing AI champions or enthusiasts, or is this person starting from scratch to build a community?

4. Scope Boundaries

What specifically is in scope for this role? What is explicitly NOT their responsibility? Are you asking for strategy development or execution of an existing strategy? Are they building AI solutions themselves or enabling others to build?

5. Organizational Maturity

What’s actually happening with AI right now in your company? Have pilots or proof-of-concepts been completed? Where do you honestly fit in the maturity model?

What’s your pace and what’s your appetite for risk versus your tolerance for security and legal approval processes?

Creating clarity with this framework will help both hiring managers and candidates.

What Candidates Should Ask

If you’re interviewing for one of these roles, look for signs of readiness:

• Clear scope and evolution path

• Identified partner teams

• Existing infrastructure (tools, policies, budget)

• Awareness that this is change management, not just tool rollout

Red flags:

• Vague org context

• Unrealistic expectations

• No clear success metrics

• Strategy and execution blurred together

You can be excellent at this job and still burn out if the foundation isn’t there. Burn out isn’t just about being overloaded by the volume of work, but also by not being able to see the results of your work.

Final Thought

Hiring an AI TPM can accelerate transformation, but only when the organization is ready to support it. Here’s the TL;DR:

Support infrastructure and partnerships matter more than individual brilliance. You can’t hire one brilliant person and expect them to transform culture alone. They need partners, budget, executive backing, and clear scope.

Distributed ownership often works better than centralized control. The most effective transformational adoption I’ve seen comes from many people owning small pieces, not everything funneling through one person who becomes a bottleneck.

Honest assessment of maturity helps set realistic expectations. If you’re at Stage 1, don’t hire for Stage 3. If you’re at Stage 0, maybe don’t hire at all yet - get clear on strategy first.

Every organization’s context is different though – does this resonate with you or what’s worked where you’re at? Does it matter if organizational readiness matches role design?

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This was also posted to my LinkedIn.