🤯 Why Most AI Roadmaps Fail — And What I Do Differently

Ankit Tomar,

(A Real-World Take from the Product Trenches)

“The moment AI enters the roadmap, it hijacks the whole conversation. And that’s where things start breaking.”

👀 A Real Story

A while back, I joined a large telco to lead their AI product strategy.

The goal? Build a recommendation engine to help upsell data plans and cross-sell OTT subscriptions.
Sounds doable, right?

Except… something didn’t feel right.

I kept asking:
“Where’s the market research? Do we have any user data? Demographics? Segments?”

Everyone sort of… nodded.
But no one had real answers.

It had already been decided:
“We’ll build an AI system to recommend services — and users will buy them.”

No strategy.
No insights.
Just a model-first mindset.

This is exactly the trap I see so many AI product teams fall into.

🧨 The Trap: When AI Becomes the Roadmap

When AI gets mentioned, it takes over.

Suddenly:

  • All the focus is on models and accuracy
  • Teams shift to metrics like F1 and precision
  • Everyone forgets: there’s a whole product to build

AI isn’t the product. It’s a feature. A really powerful one — but still just a part of the full picture.

🔍 What I’ve Learned (The Hard Way)

Here are a few patterns I’ve seen repeatedly across AI product teams — and what I now do differently every time.

1️⃣ Poor Ownership of the Data Product

Most product leaders assume the data science or engineering team will “handle the data.”
But here’s the truth:

If your model fails, it’s not just on the DS team — it’s on you, the product leader.

  • Data is messy
  • It’s often missing, biased, or outdated
  • And without the right governance, it’ll silently tank your model

✅ Now, I treat the data product just as seriously as the model or the UI.

2️⃣ No Plan for Data Access or Delays

AI needs data — and access to that data isn’t always easy.
Internal silos, compliance, messy pipelines… the list goes on.

Most AI roadmaps forget this.

✅ I make it a habit to ask early:

  • Where is the data?
  • Who owns it?
  • What will block us?

Then I use influence — not just process — to unblock it fast.

3️⃣ Forgetting That AI Is Just a Feature

I’ve seen entire teams spend quarters tuning models — while ignoring the experience around it.

But let’s be honest:

  • The user doesn’t care about your F1 score.
  • They care about what they see, what they understand, and what they can act on.

✅ I focus equally on:

  • What the user sees
  • How they trust the AI
  • What happens when the model is wrong

Simple always wins. Confident ≠ correct — and your UI should reflect that.

4️⃣ No Expectation Management for Uncertainty

AI isn’t always predictable.
Sometimes you won’t hit the target accuracy.
Sometimes the model drifts.
Sometimes… it just gets weird.

That’s okay — if you planned for it.

✅ I build:

  • Clear fallback options (rules, overrides, alerts)
  • Transparency into how confident the model is
  • Space for the human to stay in the loop

It’s better to be transparent than to pretend your AI is smarter than it is.

🎯 So What Do I Do Differently?

After working on multiple AI products, here’s how I build differently now:

✔ I Start With the Problem — Not the Model

Always. I ask:

  • What’s the actual pain point?
  • Who are we solving for?
  • What changes if this works?

If we can’t answer that, we don’t need a model — we need research.

✔ I Treat Data Like Infrastructure

Good AI starts with good data.
So I invest in data quality, access, labeling, and pipelines — just like we do with code.

✔ I Anchor AI to Real KPIs

Not just model metrics.
I ask:

  • Does this save time?
  • Increase revenue?
  • Improve user experience?

If we can’t tie the AI to real impact, we’re not ready to build it.

✔ I Keep AI in Its Place

AI is an enabler — not the hero.
It should feel like magic under the hood, not noise in the experience.

The goal is trust, not just performance.

🧠 Final Thought

AI products fail when we forget the basics of product leadership.

If you’re leading AI work, remember:

  • Start with the user
  • Validate the data
  • Plan for uncertainty
  • Build the whole experience — not just the model
  • And own the outcome, not just the algorithm

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Product Management

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