95% of product managers know AI is the future.

But only 5%  know how to break into it, let alone lead it.

That’s where I come in.

I’m currently a Product Director at Netflix, previously building AI systems at Meta and AWS. 

Over the past 13+ years, I’ve worked across infrastructure, platforms, and applications, leading high-stakes product development in some of the most innovative AI teams in the world.

But what I’m most proud of?

Helping 𝟭,𝟱𝟬𝟬+ 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗿𝘀 make the leap into AI roles - many landing jobs at top companies like Google, Microsoft, and fast-growing startups.

𝗛𝗲𝗿𝗲’𝘀 𝘁𝗵𝗲 𝗴𝗮𝗽 𝗜 𝗵𝗲𝗹𝗽 𝗣𝗠𝘀 𝗰𝗹𝗼𝘀𝗲:

✔ You’ve got product chops, but feel stuck trying to “speak AI.”
✔ You’ve taken a few courses, but still get rejected for lack of experience.
✔ You want to lead AI initiatives but lack the frameworks, confidence, and portfolio to show you’re ready.

𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗺𝘆 𝟱-𝘄𝗲𝗲𝗸 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗽𝗿𝗼𝗴𝗿𝗮𝗺, 𝗜 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂:

→ Build real-world, hire-ready portfolio projects
→ Learn how to scope, evaluate, and ship AI features like a pro
→ Translate your existing product skills into AI fluency
→ Walk into interviews knowing you belong at the table

This isn’t about theory.

It’s about building 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗼𝗳, 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗺𝗼𝗺𝗲𝗻𝘁𝘂𝗺, so you stop watching the AI wave and start leading it.

𝗪𝗵𝗼 𝗶𝘀 𝗶𝘁 𝗳𝗼𝗿? 

Mid-career product managers with 3–8 years of experience. 

You’re looking to future-proof your career, land high-paying AI roles, and lead with credibility, without needing to become a data scientist.

𝗪𝗵𝗲𝗻 𝘆𝗼𝘂’𝗿𝗲 𝗿𝗲𝗮𝗱𝘆, 𝗵𝗲𝗿𝗲 𝗶𝘀 𝗵𝗼𝘄 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽:

1️⃣  Become an AI product manager → https://enterprisereadyaipmroadmap.com/

Or just DM me - I’d love to hear your story.

I didn't share my work publicly for 13 years. Not because I had nothing to say...

But because I believed the work should speak for itself.

So I stayed heads down.

While my peers shared insights, opinions, and half-formed ideas,
I kept building.

Shipping systems.
Making decisions.
Learning the hard parts quietly.

Over time, I saw my peers' careers begin to take off.

They were being recognized for what they were sharing.

Later, they began being invited into rooms I could only dream of entering.

And I told myself:
 
• “That’s fine.”
• “Merit will win eventually.”
• “My time will come.”

But I soon realized that's not the world we live in.

Now, don’t get me wrong…
I’ve had a good career so far.
And I’m deeply grateful for it.

Still, sometimes I wonder...

What if I started the projects I was building in 2013?
What if I shared my thinking about AI while building AWS DeepLens?
What if I shared my learnings on building responsible AI systems while working at Meta?

No regret.
Just a thought.

Here’s the truth I learned later than I should have:

Opportunity is attracted to those who are seen.

Silence doesn’t signal depth.
It signals absence.

So if you’re building something right now…

Share it.

Share your reasoning.
Share your solutions.
Share your learnings.

Because while the work matters...

Visibility is what brings the opportunity.

Love, Jyothi

Jyothi Nookula

AI Product Leader | Coaching PMs to become AI Product Leaders | ex-Meta, Amazon, Netflix | Founder @ Next Gen PM

It’s been 5 months since my time at Netflix ended.

Here’s an honest update on how life has been going.

As some of you may know, I decided to embark on my own journey.

I knew what I was walking away from.
But I didn’t fully know what I was walking into.

At the time, all I had was a conviction I’d been carrying for years:

I wanted to help product leaders learn how to build and lead AI responsibly.

5 months in, a few things are clear.

First, the work resonated more than I expected.

Over the past year, I worked with 233 students across 9 cohorts.

We grew from one course to three:
 
• AI PM (flagship)
• Agentic AI for PMs
• PM Accelerator

Along the way, I had the chance to share these ideas on podcasts and stages.

And I’ve been able to be present for my family.

Second, consistency has been compounding.

7 months ago, I started sharing my thinking more consistently.

Since then, this community has grown by over 9,500 people.

That still surprises me.

Finally, the uncertainty never fully went away.

Some things worked faster than expected.
Others took longer.
And some assumptions I had at the beginning were simply wrong.

But I don’t regret the decision for a second.

5 months in, I’m more convinced than ever that this work matters.

And that there’s a real need for rigor, judgment, and clarity in how we build AI products.

None of this happens without community.
Without curiosity.
Without people willing to do the hard, often uncomfortable thinking.

To everyone who joined a cohort, shared context, asked hard questions, or followed along quietly, thank you.

We’ll continue to go deeper.
More rigor. More practice. More care.

Grateful for the journey so far.
Excited for what’s ahead.

Love,
Jyothi

My secret weapon as an AI PM.

Has nothing to do with my years of experience.
Has nothing to do with the Fortune 500 companies on my resume.
Has nothing to do with being early to AI.

It's the fact that I get hands-on.

Early in my career, I learned this the hard way.

I thought I could manage AI projects from a distance.

So I would:
 
• Read papers.
• Review specs.
• Rely on others to explain the models.

I was wrong.

And that distance showed up in my decisions.

The biggest breakthrough in my career came when I started getting closer to the work.

Training models.
Exploring their limits.
Seeing exactly where they break.

That exposure reshaped how I think, how I decide, and how I lead.

Anyone who has worked with me will tell you it’s been foundational.

You can’t build great AI products from the sidelines.
And you can’t develop AI product judgment without getting close to the work.

To the PM who wants to move into AI:

Don’t stay abstract.
Don’t stay distant.

Get closer to the work.
Get your hands dirty.

It will supercharge your development.

P.S. I put together a free email course that walks through this hands-on approach step by step for PMs transitioning into AI:  https://lnkd.in/gAh-gNQf

If I were a product manager in 2026 and wanted to transition into AI PM…

Here’s exactly how I would approach it.

𝟭/ 𝗦𝘁𝗼𝗽 𝘁𝗿𝘆𝗶𝗻𝗴 𝘁𝗼 “𝗹𝗲𝗮𝗿𝗻 𝗮𝗹𝗹 𝗼𝗳 𝗔𝗜”

AI PMs are not hired for knowing every model.

They are hired for knowing 𝘄𝗵𝗲𝗻 AI is the right solution and 𝗵𝗼𝘄 to lead it.

𝟮/ 𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝗔𝗜 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀

Scoping is different.
Metrics are different.
Iteration is different.

If you keep using traditional PM frameworks, you will keep feeling stuck.

𝟯/ 𝗦𝘁𝗮𝗿𝘁 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗔𝗜 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗷𝘂𝗱𝗴𝗺𝗲𝗻𝘁 𝗯𝗲𝗳𝗼𝗿𝗲 𝘆𝗼𝘂 𝗴𝗲𝘁 𝘁𝗵𝗲 𝘁𝗶𝘁𝗹𝗲

This means:
 
• Understanding trade-offs
• Working through real AI use cases & building your own
• Practicing how to translate model metrics to business outcomes

You don’t need to quit your job.
You don’t need to become an engineer.
You don’t need a perfect background.

You need clarity.

To show you what this looks like in practice, I’m hosting a FREE live masterclass on:

𝗛𝗼𝘄 𝘁𝗼 𝗕𝗲𝗰𝗼𝗺𝗲 𝗮𝗻 𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿 𝗶𝗻 𝟮𝟬𝟮𝟲

We’ll cover:
 
• What AI PM roles actually expect today
• The most common mistakes PMs make when transitioning into AI
• How to build credibility and judgment without coding
• What to focus on now vs what to ignore

If you’re serious about moving into AI PM work and want a clear, realistic path forward, this workshop is for you.

Join me live here: https://lnkd.in/eAMyKDpy

The game has changed for product managers.

You don’t need to be the most technical person in the room.
You just need the right mental models and a clear direction.

We’ll build that together.

See you there.

If you want to pivot into AI Product Management, do not do this:

(6 mistakes that quietly stall PM careers in AI)

Most advice tells PMs what to learn.
Very little tells them what to avoid.

Here is what I repeatedly see talented product managers do wrong when moving into AI:
 
1. Trying to learn how to code
2. Jumping straight into AI tools
3. Ignoring model evaluation metrics
4. Treating AI as a black box
5. Trying to design the “perfect” AI product from day one
6. Expecting models to be 100% accurate

Each one feels productive.

All of them slow you down.

Here is what works instead:
 
1. Focus on building technical fluency
2. Learn when AI adds value before touching tools
3. Connect model performance to business outcomes
4. Master the translation between models, users, and strategy
5. Ship small, evaluate, and iterate in the real world
6. Design products that account for uncertainty

After helping 1,500+ product managers transition into AI-focused roles, the PMs who succeed are not always the most technical.

They are the ones who develop judgment.

If I could ask you to do one thing this week, it would be this:

Stop collecting AI knowledge.
Start building AI product clarity.

I put together a free, practical 5-day email course to help PMs avoid these mistakes and focus on what matters.

Get it here: https://lnkd.in/gAh-gNQf

PS: Which of these mistakes feels most familiar right now?

Reply below. I read every comment :)

AI fluency in product management is often misunderstood.

It’s not about sounding technical...
It’s also not about explaining how models work in detail.

AI fluency is about knowing 𝘄𝗵𝗮𝘁 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗮𝘀𝗸, and 𝘄𝗵𝗲𝗻 𝘁𝗼 𝗮𝘀𝗸 𝘁𝗵𝗲𝗺.

Most PMs try to demonstrate AI competence the wrong way.

They talk about:
 
• LLM internals
• Vector databases
• Training techniques
• Loss functions

... which is useful knowledge.

But it’s not what hiring managers are listening for.

They’re not looking for a second engineer.
They’re listening for product judgment.

Judgment sounds like this:
 
• How does this AI feature improve the user experience?
• What signal tells us it’s working or failing?
• What trade-offs are we making between accuracy, latency, cost, and risk?
• How does this model choice align with our product strategy?
• What data does this system depend on, and where can it break?

Those questions require some technical understanding.

But not deep specialization.

You don’t need a PhD.
You don’t need jargon.
You don’t need to know every model.

You need enough clarity to guide decisions under uncertainty.

That’s what AI fluency looks like in practice.

Not knowing everything about the system.
But knowing how to steer it responsibly.

You can’t fake judgment.
And you can’t memorize it.

The only way to gain this fluency is to learn by doing.

♻️ Share this with a PM trying to improve their AI fluency.

Follow me for grounded insights on how to build AI products that work.

“Before this course, I thought AI PM wasn’t that different from PM.”

I hear this at the start of almost every cohort, so let’s clear it up...

One of my students said this to me last week.

He’s already a product manager.
Several years of experience.
Smart, capable, confident.

And he wasn’t being dismissive...

He genuinely believed AI PM was just PM with a new buzzword.

Then the classes started.

His mode of thinking changed.

The concepts were extremely unfamiliar to him.

At one point, he said:

“I’m a PM, but I’ve never evaluated data like this. I’ve never looked at model cards or data cards. I’ve never had to think about fairness, bias, or evaluation frameworks.”

And that’s the gap.

Traditional PM work trains you to reason about:
 
• User needs
• Business trade-offs
• Roadmaps and prioritization
• Deterministic systems

AI PM work forces you to reason about:
 
• Probabilistic outputs
• Data quality and provenance
• Model evaluation and failure modes
• Trade-offs between accuracy, fairness, latency, and cost
• Systems that behave differently over time

As a PM, you can ship features without ever touching data.
As an AI PM, you can’t.

You don’t need to train models.

But you do need to understand what data went in, how the model was evaluated, and where it can break.

Because AI PM is not a “slight extension” of PM.

It’s a different job that builds on PM fundamentals, but demands new judgment.

The student summed it up perfectly at the end:

“I was a PM for years, thinking there’s hardly any difference. Now I realize I was completely wrong.”

That realization is the turning point.

Not everyone will become an AI PM.

But everyone working near AI should understand why it’s different.

Because once you see the gap, you can’t unsee it.

𝗣.𝗦. If you’re a PM trying to understand what changes when you move into AI work, I put together a free 5-day email course that walks through the core mental models, trade-offs, and product judgment AI PMs need.

You can check it out here → https://lnkd.in/gAh-gNQf

The most valuable AI education does not come from theory.

It comes from people who have actually built AI products.

Most AI and PM courses promise clarity.
But they deliver more noise.

Why?

Because they are created by people who study AI...
Not by people who have shipped it.

They teach concepts.
Not decisions.

But what happens when you learn from someone who has built real AI systems?

Someone who has navigated messy data, imperfect models, and unclear requirements.
Someone who understands how AI behaves in production.
Someone who can show you how product judgment changes when outputs are probabilistic.

You get feedback like this:

“The single biggest value was the clarity this course created from the noise of general AI and PM content. It moved me from abstract thinking to concrete product strategy. I finally felt confident navigating AI complexity and aligning decisions to real business goals.”

The hardest part of becoming an AI PM is not learning the tools.

It is discovering how to think.

That is why I created AI PM.

A cohort-based program designed to help experienced product managers develop real AI product judgment.

Over 1,500 product managers have already used it to:
 
• Transition into AI-focused PM roles
• Lead and ship AI initiatives inside their current companies
• Build credible AI portfolios that hiring managers actually respect

No theory for theory’s sake.
No generic AI hype.

Just the frameworks, trade-offs, and decision models used by real AI product teams.

If you want clarity instead of confusion, and judgment instead of jargon, the next cohort starts Jan 26th.

Enroll here: https://lnkd.in/eeg_hD8B

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