AI is definitely here! And not only in the products we build, but also inside the organisations building them.
Most of the early AI conversation in product management circled around tools and automation.
But as Marty Cagan notes in Transformed (2024), “The critical issue for leaders is not whether we use AI, it’s how AI changes the way we lead and structure product organisations.”
Are you still scaling by adding more PMs and trying to ship faster? That worked in 2015. It’s useless in 2025!
The orgs that are winning today are scaling strategic thinking, discovery capability, and leadership maturity.
Those who adapt will thrive. Those who cling to old scaling models will be outpaced, not by AI, but by organisations that do it differently.
AI is speeding up the wrong things
In many organisations, the introduction of AI is being seen as a real revolution in productivity. And looking at it…the excitement is justified!
Some of the things AI can do today:
AI can accelerate most of the operational work that used to fill a product manager’s day. But acceleration alone is not the goal: the reality is that AI is often speeding up the wrong activities.
Rather than deepening discovery or sharpening strategy, AI optimises the visible outputs of process (ticket movement, backlog updates, and sprint velocity) without questioning if those outputs are meaningful or not.
“Product roles have become process roles ripe for AI replacement with too much focus on shipping features, not solving real problems.” - Timoté Geimer, CEO @ dualoop
For product leaders, the critical question is no longer “how fast are we moving?”
It’s “are we moving towards better outcomes, or just producing more noise?”
Without the right leadership, AI risks turning feature factories into hyper-automated production lines, optimising delivery but widening the gap between product teams and actual customer value.
5 key shifts product leaders must drive
AI is reshaping how product work gets done, but the most important shift isn’t technological. It’s cultural.
Organisations that scale well in this new era evolve how they think, lead, and structure their product functions. They aren’t the ones with the best tools.
“The role of product leadership is to enable teams to solve hard problems — not to manage backlogs or enforce process.”- Marty Cagan in Transformed (2024)
To lead a modern product organisation (one that does great in the AI era) requires a shift in mindset across five key dimensions:
1. From feature delivery → outcome discovery
Too many product teams are still measured by how much they ship, rather than by whether what they ship works. But shipping ≠ solving!
AI can automate delivery at scale, but it cannot discover unmet needs or validate whether a solution is creating value.
“Good discovery is what prevents wasted delivery.” - Teresa Torres
Yet most teams only do discovery occasionally, in between roadmaps and releases, rather than treating it as a continuous practice.
💡 Put discovery at the center of your product organisation. Coach teams to frame opportunities, run lean experiments, and validate outcomes regularly, not just to gather feedback post-launch.
2. From owning the backlog → owning the mission
The traditional PM role has revolved around managing a backlog: prioritising requests, maintaining velocity, and delivering roadmap items.
But because this kind of “backlog babysitting” is exactly the kind of work AI is primed to replace, PMs must shift from reporting the work to shaping the why.
Modern product leaders must focus their teams on solving true customer problems and aligning those problems with business goals. That requires clarity of mission, not just task management.
💡 Empower product trios to own problems, not feature lists. Replace backlog rituals with framing rituals: customer insight reviews, opportunity assessments, strategic problem canvases.
3. From reporting progress → coaching capability
AI can now generate burndown charts, meeting summaries, Jira updates…
If your leadership role is still centered collecting status updates, you could already have been replaced by a chatbot.
Your real job is to develop capability, help teams make better decisions, challenge assumptions, operate independently with strong product judgment.
“Most PMs don’t fail because they lack tools, they fail because they haven’t been coached in how to think.” - Shreyas Doshi
💡 Shift your time from status updates to strategic 1:1s, feedback loops, and coaching sessions that build product thinking at every level.
4. From prioritising faster → framing better
AI can help you prioritise faster based on effort estimates, usage data, or user sentiment.
But what your teams prioritise still depends on the quality of your insight and framing. What you feed the AI is key to ensure it’s not just sorting noise.
💡 Teach teams to lead with insight. Prioritise based on validated problems, not stakeholder pressure or roadmaps.
5. From scaling processes → scaling thinking
When faced with growth, many organisations increase processes: adding layers, roles, and reporting lines in the name of control.
But control doesn’t scale…autonomy does.
The best teams in the AI era will be smaller, leaner, and more empowered, not more bureaucratic.
The challenge is no longer “how do we track all this?”, but “how do we trust and enable it?”
💡 Scale trust, not templates. Build a shared understanding of vision, outcomes, and principles, then give your teams the space to lead.
What to invest in: discovery, strategy, and product leadership
If you’re leading a product organisation today, you’re probably asking: “What should we focus on now that AI is changing everything?”
The truth is, the fundamentals haven’t changed, they’ve just become must-haves.
The organisations that scale well in the AI era aren’t investing in more process or more layers. They’re investing in the skills and systems that AI can’t replace.
There are four areas where forward-thinking product leaders are focusing their time, budget, and coaching energy:
1. Deepening discovery culture
Generative AI is great at surfacing patterns but it doesn’t know what questions to ask. It doesn’t understand context. It can’t build empathy.
Discovery is where human insight still leads, and where most organisations are underpowered.
“Most product teams think they’re doing discovery, but they’re really doing validation.” - Teresa Torres in Continuous Discovery Habits:
AI can speed up analysis. But discovery requires intent, structure, and curiosity.
Where to invest:
- Weekly user touchpoints
- Structured hypothesis testing, not “gut feel” prioritisation
- Shared synthesis rituals (across PM, design, and tech)
👀 What to track: Are your teams generating new insights or just confirming assumptions?
2. Framing strategy through customer problems
Many roadmaps still look like feature release calendars: they’re disconnected from customer pain, and too reactive to internal pressure.
AI can recommend prioritisation based on usage or cost, but it can’t define your company’s calls or the problems you choose to solve.
Where to invest:
- Clear product missions linked to business goals
- Opportunity assessments
- Problem-first quarterly planning (based on discovery insights)
👀 What to track: Are teams solving validated problems or shipping backlog items?
3. Building leadership at every level
Leadership is still too often centralised: product managers execute, leads coordinate, and only the CPO “owns” strategy.
This model doesn’t scale, especially not when AI compresses execution cycles.
What scales is distributed leadership: every PM, designer, and tech lead must think critically, align cross-functionally, and own outcomes.
“Most PMs are expected to lead but are never taught how.” - Shreyas Doshi
Where to invest:
- Coaching programs focused on product judgment
Mentorship from strong leads - Team autonomy, via clear strategic context
👀 What to track: Can your teams explain the “why” behind their roadmap?
4. Making space for creative and ethical product work
AI can write specs, draft designs, even suggest experiments, but it cannot evaluate risk, weigh ethical trade-offs, or make vision-driven bets.
The value of human leadership is shifting toward creativity, integrity, and long-term thinking.
And those are what differentiate good product organisations from great ones.
Where to invest:
- Vision framing: not just for exec offsites
- Ethical product decision-making (bias, safety, social impact)
- Bold bets: time and space for teams to pursue new ideas, not just ship requests
👀 What to track: Are your teams shaping the future or reacting to it?
📍What to avoid when scaling product in the AI era
Even experienced product leaders fall into patterns that feel like scaling but in reality amplify misalignment, waste, or shallow thinking.
Use this table as a diagnostic: if your org is doing these things, you may be scaling the wrong system.
Product leadership is evolving, are you?
AI won’t kill product management, but it will kill the version of product management that was never built to last: the one that prioritised ticket flow over discovery, velocity over outcomes, and surface-level alignment over deep strategic thinking.
The question for product leaders isn’t whether to respond to AI.
It’s how fast they can evolve their organisations to meet the moment.
The teams that will thrive in the AI era are already shifting:
- From coordination to coaching
- From delivery to discovery
- From velocity to value
- From backlog managers to outcome leaders
They’re investing in strategy, discovery, and leadership, the parts AI can’t replicate.
At dualoop, this is where we focus our work. We help product organisations scale smarter, by aligning strategy, enabling autonomy, and building real discovery capability across teams.
AI will keep moving fast. The only question is whether your organisation can move forward, not just faster.
The future belongs to product leaders who are ready to evolve. Are you one of them?
