The Curriculum I Wish I Had as an Internal AI Product Manager
And why I believe we should build it together
#beyondAI
A year ago, I wrote about why we need a curated learning repository for AI Product Managers. Today, I want to show you how we can build it together.
Over the past year, I’ve written dozens of articles on internal AI Product Management and read at least twice as many from others doing the same. Topics range from shadow governance to MVP value measurement, from cross-functional buy-in to the traps of over-promising AI capabilities.
But here's the challenge: all that wisdom is scattered.
Some of it sits in blog posts.
Some in LinkedIn threads.
Some in decks you’ll never see.
What’s missing is a place that pulls this knowledge together. Not into a theoretical textbook, but into a learning path grounded in practice.
So I created a structure for it. A curriculum.
The Curriculum I Wish Had Existed
A few months ago, I published A Curriculum for Internal AI Product Management. It’s designed like an M.Sc.-level program, but with one big difference: it’s for internal AI Product Managers. The ones navigating the complexity of building and scaling AI solutions inside large organizations. Not on a greenfield. Not in startups. But deep inside orgs with legacy tech, process silos, conflicting goals, and real impact on the line.
The curriculum covers topics like problem discovery in internal teams, AI solution prototyping and rollout, adoption metrics beyond usage, compliance, explainability, governance, and collaboration with data science, legal, and business units.
It’s a map, not a manual. And like every good map, it becomes more useful the more people contribute to it.
Why AI PMs Should Lead This
AI Product Managers are in a unique position to lead this kind of learning effort because they sit at the intersection of multiple disciplines. They work across engineering, data science, business, legal, operations, and compliance, often within the same week, sometimes within the same meeting.
That vantage point gives them an unusually broad perspective. Unlike specialists who go deep in one field, AI PMs constantly connect dots between teams, technologies, processes, and real-world problems. They translate between mindsets, resolve contradictions, and shape solutions that are not only technically sound but also usable, trustworthy, and aligned with business needs.
This role forces them to see what’s missing. It forces them to ask the uncomfortable questions others avoid. It also gives them visibility into what knowledge gaps slow down progress, where alignment breaks down, and what kinds of insights actually move things forward.
So when it comes to building a curated learning path, one that reflects the realities of shipping AI inside complex organizations, AI PMs are not just participants. They’re the ones best positioned to lead.
They know what’s essential, what’s overhyped, and what truly helps teams deliver impact.
How You Can Contribute
I’m now opening up this curriculum to contributions from anyone working in or around internal AI product delivery.
There are three easy ways to get involved:
1. Match your content to a course
Each class in the curriculum includes a short description. Based on that, you can send me your articles, articles you’ve read and recommend, talks, tutorials, or even short courses. Just let me know which class your contribution fits best. If it aligns, I’ll link it directly from the curriculum and credit your work. This way, learners not only follow a structured path but also benefit from real-world perspectives.
2. Suggest a new course
This curriculum reflects how I see internal AI Product Management, but it’s not set in stone. If you think there’s a course missing, whether it’s a topic you’ve struggled with, something you wish you had learned earlier, or an area you teach often, let me know. If it’s helpful for internal AI PMs, I’m open to adding it.
3. Propose an elective
The curriculum includes elective modules for areas like new technologies, frameworks, or methods that are still evolving. If you’ve developed expertise in something like enterprise AI agents, prompt ops, data-centric evaluation, or domain-specific architectures, you can propose a new elective. You can even co-create it with me.
How to Contact Me
I regularly share my thoughts on LinkedIn and on Substack, that’s where you’ll find my articles, reflections, and frameworks on internal AI Product Management. If you want to contribute to the curriculum or share a relevant piece of work, the best way to reach me is via the comments under my posts or articles.
I'm not very active in DMs. Not because I don’t value the input, but because there’s simply too much to respond to one-on-one. Everything I’m building here happens next to my full-time role as a Lead AI Product Manager and Strategist, and as a Co-Founder of the AI Center of Excellence at Vodafone. And beyond work, there’s also a personal life I care deeply about and try to protect.
So if you want to send me a suggestion - whether it’s an article, a new course idea, or a contribution to one of the electives - please comment under my latest LinkedIn post or Substack article, no matter which one. That’s where I’m most likely to see it and respond. Even though notifications are off, I check the comments regularly because they’re part of my publishing routine.
I know it’s not the most convenient setup. But it helps me stay focused - both in my work and outside of it.
Maybe one day I’ll find a better way. Let’s see.
PDF Download: A Visual Overview of the Curriculum
To make things easier to explore and share, I’ve also created a PDF version of the curriculum. It includes:
An overview of all semesters and course modules
Core and elective topics
A clear structure that reflects the internal AI PM journey
I’ll be sharing it on LinkedIn soon, so feel free to download, share, or reference it as you explore the curriculum or think about contributing.
Help Spread the Word
If you believe in the value of this curriculum and the idea of a curated learning path for internal AI PMs, here’s how you can help it grow:
1. Share the article. Share it with your network, especially with people working in AI, product, data, or digital transformation roles inside large organizations.
2. Tag someone. Know someone who’s written something brilliant? Tag them. One mention can bring in a whole new module.
3. Leave a comment. Even a short note saying “this is needed” helps others take notice. And if you disagree with something or have ideas for improvement, even better.
4. Add your voice. Send me your article, video, course, or story and tell me which class it fits. Or propose a new one. I’ll take care of linking it where it belongs.
The more we co-create this, the more useful it becomes. Not just for learners, but for all of us trying to make AI work where it matters most.
JBK 🕊️
Let's do this!