AI is becoming ubiquitous in product and delivery teams. From drafting user stories to generating timelines, it’s helping us move faster than ever. But with that speed comes a growing concern: are we starting to confuse AI-generated polish with actual human competence?

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Across teams and industries, we're seeing patterns emerge—decisions being accepted without scrutiny, strategies shaped by algorithms rather than user insight, and polished outputs masking shallow understanding.

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This isn't just about AI adoption. It’s about the subtle ways it's reshaping how we work, and the risks we run when we stop asking the hard questions.

The Trap We Didn’t See Coming

There’s no question AI is turbocharging how product teams operate. Tools like Copilot, Perplexity, ClickUp AI, and Dovetail are helping us ideate faster, document cleaner, and predict better. It feels like we've levelled up overnight.

But something uncomfortable is happening underneath.

We’re seeing junior PMs ship specs they don't fully understand. Roadmaps are being populated by trends rather than customer truths. And worst of all, we're losing the instinct to ask, "Does this make sense?"

When AI handles the hard bits, it's easy to start believing we’re smarter than we are. The work looks polished. The language sounds confident. But the depth? The domain understanding? Often missing.

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The Illusion of Competence in Action

Here are just a few real stories that have emerged across teams:

• The Copy-Paste PM: Delivered weekly market insights and feature specs faster than anyone else. Later discovered to be prompting ChatGPT with basic inputs and forwarding unedited outputs. When questioned on rationale, couldn’t explain key trade-offs.
• The Phantom Roadmap: A team proudly showcased a quarterly roadmap. But it turned out the features addressed problems their users didn’t have. The entire thing was reverse-engineered from AI-generated industry themes.
• The Dependency Spiral: Delivery timelines looked pristine. Until teams realised that cross-functional dependencies hadn’t been manually verified. When real blockers emerged, no one had the mental model to resolve them.

These aren’t failures of AI. They’re failures of how we’ve chosen to integrate AI into the way we work.

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What We're Really Risking

We often talk about AI replacing roles. But in product teams, the bigger risk is this: AI is replacing thought before replacing people.

• Customer empathy is fading: When personas and pain points are summarised by tools, fewer PMs are sitting in on real user sessions. We lose the visceral understanding that drives urgency and innovation.
• Strategic thinking is hollowing out: AI can suggest what to build, but not why it matters in the broader business context. That “why” muscle is weakening.
• Collaboration is getting lazier: When AI drafts everything from stories to standups, fewer real conversations happen. And with it, the shared understanding that keeps teams aligned.

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Getting Back to the Work That Matters

So, what do we do? Ban AI tools? Not at all.

We need to reframe how we use them. AI isn’t our co-pilot. It’s our intern: fast, tireless, but lacking context and judgment. We still need to drive.

Here’s how:

1. Instil a verification culture: Every AI-generated artifact must be scrutinised. Not rubber-stamped. Make it a norm to challenge assumptions and trace logic.
2. Train before you automate: Ensure team members know how to do the work manually before letting AI do it. Think of it like pilots training on simulators before flying with autopilot.
3. Mandate customer proximity: AI can analyse sentiment. But only humans can feel frustration in a user's voice or see hesitation in a user's click. Prioritise direct exposure.
4. Reward critical thinking, not velocity: It’s easy to celebrate how many pages or plans got shipped. Start celebrating thoughtful decisions, strategic trade-offs, and clarity in the face of ambiguity.

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Final Thoughts

AI is here to stay. That’s a good thing. But as product and delivery leaders, we must ask ourselves: Are we building teams that understand problems deeply or just teams that prompt well?

The next generation of great product teams won’t be defined by how fast they use AI tools. They’ll be defined by how well they combine human judgment, customer obsession, and critical thinking with those tools.

In the race to build faster, let's not forget to build smarter.

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