AI didn’t make product fundamentals obsolete. It made ignoring them expensive.
Okay, I’m writing another article inspired by the recent episode of Lenny’s Podcast featuring Aishwarya Naresh Reganti and Kiriti Badam. I know, get your own content. Hear me out. I feel like there’s one more thing that came out loud and clear for me in this podcast that I want to share with you.
My last post focused on how AI products behave differently than traditional software, particularly non-determinism and the trade-offs between autonomy and control and the ramifications that has on the skills and practices product managers need to build if they want to lead AI product building efforts. I talked about the new ways of working, but now I want to talk about the fundamentals that haven’t changed…and the risk if we don’t get them right.
The same signals keep reinforcing the same truth: the fundamentals of good product management are not changing. What is changing is the cost of ignoring them.
The problem: speed without clarity is a new failure mode
AI has collapsed cycle time. Yawn. We know that.
Product teams can synthesize research faster, prototype faster, ship faster, and iterate faster than ever before. That part is undeniable. We see it weekly in organizations using AI to accelerate delivery.
We also see the other side of that acceleration.
Teams are hitting delivery timelines at warp speed and still missing the mark once their AI products meet real customers. Adoption stalls. Trust erodes. Value fails to materialize. The post-launch issues sound familiar:
“We shipped faster than we ever have, but usage is low”
“The model works, but customers aren’t happy.”
“We built an agent, but no one actually wants it.”
This is not an AI capability issue. It is what happens when speed outpaces clarity.
In a slower world, weak problem definition often reveals itself gradually. In an AI-accelerated world, it compounds immediately. Teams don’t just move faster. They follow momentum confidently, sometimes following the taillights of competitors without knowing where the road leads. That’s not a moral failure. It’s a structural one.
Acceleration without orientation doesn’t just waste effort. It increases risk.
These fundamentals are not new. The environment is unforgiving.
I love what Kiriti Badam said in the podcast. “One easy slippery slope with all these AI advancements is to keep thinking about the complexity of the solution and forget the problem you’re trying to solve.”
The fundamentals are clear:
- Problem first.
- Customer first.
- Outcome-oriented decision making.
None of this should be surprising. These are fundamentals for any product, AI included. They have been taught, written about, and debated for years. What has changed is the environment those fundamentals operate in.
AI introduces variability and ambiguity immediately. Users push systems beyond intended use faster. Non-deterministic behavior exposes gaps in assumptions on day one, not in quarter-end metrics. Weak problem framing no longer hides behind release cycles or roadmaps.
This is why we keep hearing the same guidance from different places.Researchers consistently emphasize that human-centered grounding is critical to trust and adoption in AI systems. The NIST AI Risk Management Framework places defining purpose, context, and intended outcomes at the very start of responsible AI development.
These are not nostalgic calls for “back to basics.” They are responses to a faster, less forgiving system.
Fundamentals now function as risk management.
Tool-first and speed-first is how teams deliver and still miss value
One pattern we see repeatedly with clients is this:
Teams are using AI to deliver AI products faster than ever. And many of them are still failing faster too.
Delivery timelines are now being met. Product outcomes are not.
This is where tool-first thinking becomes dangerous. “We should build an agent.” “We need AI here.” “Our competitors shipped this AI feature, we need to do the same.” When speed is cheap, it feels irresponsible not to move quickly. But capability is not value.
AI makes it easier than ever to optimize for what is possible instead of what is meaningful. Teams can build answers faster than they understand the question. They can automate workflows that never should have existed. They can ship something impressive that solves no real customer pain.
This aligns closely with what many product advisors highlight across conversations with AI practitioners: many failures are not technical. They are failures of problem selection and value definition.
Recent enterprise AI research reinforces the same point. Most AI initiatives stall after pilots not because the models fail, but because value was never clearly defined in the first place.
Meeting a delivery timeline is not the same as delivering value.
AI just makes that distinction harder to ignore.
Groundwork fundamentals as leverage in AI product work
This is where foundational product thinking becomes operationally critical, not theoretical.
At Product Rebels, the reason we emphasize tools like a Convergent Problem Statement is not because they are elegant artifacts. It’s because they force teams to align on who the customer is, what pain actually matters, and why it is worth solving now. (Download our free Groundwork Templates)
In AI work, that alignment becomes a guardrail against solution sprawl and capability chasing. Similarly, customer needs identification and prioritization prevents teams from mistaking “what the model can do” for “what the customer values.” It constrains AI capability to the problems that actually move outcomes. (get your template and guide here)
We see a consistent pattern. Teams that slow down just enough to converge on the problem and prioritize real customer needs end up moving faster later, with less rework, higher trust, and stronger adoption.
What Product Leaders and Product Managers can do tomorrow
1. Relentlessly converge on the problem before accelerating the solution
AI collapses the cost of execution. That means the biggest risk now sits upstream, in problem definition. Teams that skip convergence in favor of momentum don’t just move fast — they lock in the wrong direction earlier and harder.
Strong product leaders create intentional friction around:
- What problem is being solved (not the capability being enabled)
- Who it’s for
- What outcome = success in the eyes of the customer
This is where fundamentals like clear problem framing and needs prioritization do their real work. Without them, AI doesn’t amplify value. It amplifies drift.
2. Track leading indicators of trust, not just lagging outcomes
Business and customer outcomes still matter. But in AI products, trust is built or eroded before those outcomes show up. Product leaders need to pay attention to the early signals that indicate whether users believe the system is helping them. Leading indicators of trust include:
- When users accept, edit, or override AI outputs
- Where they hesitate, regenerate, or abandon interactions
- How often humans feel the need to double-check results
These signals are inputs into trust. If they’re ignored, teams often discover trust problems only after adoption stalls or outcomes flatten.
3. Hold product teams accountable for value, not velocity
Am I a broken record on this one? Maybe. But hey, product teams now hit timelines (or even beat them) while still missing the boat on customer and business outcomes. So a little reminder doesn’t hurt: product teams exist to create outcomes, not output.
In practice, this means:
- Treating shipped functionality as a hypothesis, not a finish line
- Reviewing whether behavior actually changed after release
- Being willing to slow, stop, or narrow when value isn’t materializing
Velocity without learning is no longer neutral. In an AI context, it compounds waste faster than teams expect.
The Takeaway
AI is changing workflows, roles, and velocity. No doubt about it.
BUT…It is not changing the responsibility we have for good product management fundamentals.
Problem-first keeps teams aligned on what actually matters.
Customer-first keeps teams grounded in reality, not assumptions.
Outcome orientation is what prevents speed from outpacing value.
Speed is easier than ever. That doesn’t mean value is.
Want help putting this into practice?
At Product Rebels, we work with product leaders and teams navigating exactly this shift: using AI to accelerate delivery without losing product discipline or customer trust.
Is your team experiencing challenges in implementing AI into your product operating model or struggling in establishing the practices that enable the best outcomes from AI product building?
Schedule 30 minutes with us to learn a little bit about you and explore how we can help.
About Product Rebels
Product Rebels helps product leaders bring their teams from good to great. We work with established product organizations that already know the basics of product management but want to operate at a higher level. Our focus is not Product Management 101. It’s helping teams build strong customer foundations and outcome-oriented ways of working that consistently translate into better results; for customers and for the business.
We partner with leaders and teams to change how product work actually happens day to day: how customer insight is gathered and shared, how problems are framed, how tradeoffs are made, and how learning compounds over time. AI is infused throughout these practices as an accelerator, helping teams synthesize learning faster, explore more options, and move with greater confidence without sacrificing judgment or customer connection.
The result is product teams that don’t just ship more, they build the right things, make better decisions under pressure, and deliver meaningful, sustained impact.
Sources and further reading
- Lenny’s Podcast – Building AI products that actually work (Aishwarya Naresh Reganti & Kiriti Badam)
https://www.lennysnewsletter.com/p/building-ai-products - McKinsey – Why do most AI transformations fail?
https://www.mckinsey.com/capabilities/quantumblack/our-insights/why-do-most-ai-transformations-fail - McKinsey – The state of AI in 2024
https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai-in-2024 - Stanford HAI – Human-centered and trustworthy AI research
https://hai.stanford.edu/research