Episode Details

Our latest episode features Nicholas Holland (SVP of Product & AI at HubSpot) and explains how AI is actually changing go-to-market teams:

* AI cuts rep research time and turns calls into structured insight

* “AI Engine Optimization” (AEO) is becoming the new SEO

This conversation isn’t speculative—it’s a blueprint. Listen to Episode 42 on Apple Podcasts

🚨 Upcoming Workshop: Sept 18 — AI Product Strategy for Realists

Use promocode pod30 at checkout to get 30% off your registration!Join us for a live 90-minute workshop that goes beyond the hype. We’ll walk through real frameworks, raw mistakes, and how to make AI product strategy actually work—for small teams, scale-ups, and enterprise leaders.

👉 Save your seat now

AI Product Strategy FAQ, Minus the Bullsh*t

Over the past few months, we’ve been collecting the most common—and most misunderstood—questions about AI product strategy. What we found were recurring patterns of confusion, hype, and hope. This article breaks down those questions one by one with honest answers, uncomfortable truths, and hard-won lessons from teams actually building and shipping AI products.

Each section includes:

* A blunt reality check (“Uncomfortable Truth”)

* A strategic lens for tackling it

* A sticky insight to anchor your messaging

* A practical takeaway

This is not a “how AI works” explainer. This is how to make it useful—inside a real product.

Q1: How do we choose the right use case for AI in our product that actually delivers value?

Uncomfortable Truth: The best use cases might be internal—not flashy or customer-facing. If you’re just “adding AI” for the optics, you’re already off-track.

Strategic Frame: Don’t chase the cool feature—hunt down the messiest workflow and blow it up.

Always Remember: Your AI should solve a problem your users complain about—not a problem your team finds interesting.

Research This: Map the top 10 recurring tasks inside your product (or across your internal ops). Which of them have the highest time cost and lowest user satisfaction? That’s your AI opportunity space.

Real Example: Altan (natural language app builder); internal fraud detection automation; AI for helpdesk triage.

Takeaway: Pick the ugliest, least scalable problem your users hack around with spreadsheets. Then automate that.

Q4: How do we handle data privacy and ethics when integrating AI features?

Uncomfortable Truth: Most tools don’t offer true privacy—they use your data to train their models. That’s not a technical flaw—it’s a business choice.

Strategic Frame: If trust is central to your brand, bake it into the infrastructure. Build sandboxes. Offer guarantees. Publish your governance.

Always Remember: You don’t get to ask users for their data and their forgiveness.

Research This: Ask your legal, compliance, or procurement partners what requirements would be non-negotiable for adopting a third-party AI tool. Then apply those to your own product.

Example Guidance: Make “zero training from user data” a tiered feature—or your default.

Takeaway: If you’re targeting enterprise buyers, your AI feature won’t get through procurement unless you have strict privacy toggles and a clear usage log.

Q5: How do we measure the success of AI features in a product?

Uncomfortable Truth: More engagement doesn’t always mean more value. In AI, time