Every company is evaluating the impact of AI on its organisation, whether through its products, operating model, or internal capabilities.
For Product Leadership, one question is becoming increasingly important:
What does AI mean for the capabilities of a Product Manager?
Should we introduce AI as an additional capability?
Should AI literacy become a core requirement for Senior and Staff Product Managers?
Should we add AI as a standalone skill to our competency frameworks?
And perhaps most importantly: how do we meaningfully assess AI literacy in Product Management?
These are just a few of the many questions Product Leaders are navigating. Here’s my take.
Start by separating skills from capabilities
First things first: we need to differentiate between skills and capabilities.
Skills are specific learned abilities that enable someone to perform a task.
Capabilities are broader. They encompass not only what someone can do, but also the behaviours, judgement, mindset and attitudes that allow them to consistently perform effectively.
So where does AI fit?
Somewhere in the middle.
AI can enhance specific skills, but its greater impact is in how it changes and potentially amplifies broader product capabilities.
That distinction matters.
If we treat AI as simply another skill to add to a Product Manager’s toolkit, we risk underestimating its impact. More importantly, we may end up creating another checklist item in an already crowded competency framework.
Think about PM competencies, not AI competencies
There are several established Product Management competency frameworks that organisations have adopted and tailored to their own context.
Two examples are Ravi Mehta’s PM Competencies and Petra Wille’s PM Wheel.
The important question isn’t whether we should add “AI” as another competency alongside customer discovery, strategy, execution, analytics or stakeholder management.
Instead, ask:
How does AI amplify the competencies we already expect from great Product Managers?
Take Voice of the Customer as an example.
Traditionally, a PM might conduct customer interviews, analyse qualitative feedback, synthesise themes and combine those findings with product analytics.
AI can significantly extend that capability.
PMs can use AI to analyse large volumes of customer feedback, identify patterns across qualitative and quantitative data, simulate or conduct synthetic user interviews, explore different customer perspectives, and connect UX findings with behavioural analytics.
The competency hasn’t fundamentally changed.
The leverage has.
And that distinction is important.
AI doesn’t necessarily create a new product competency. It changes the depth, speed, scale and potential quality with which an existing competency can be exercised.
The same applies across the PM role
Consider a few other examples.
Product discovery: AI can help synthesise research, identify patterns and generate hypotheses faster.
Strategy: AI can help PMs explore scenarios, challenge assumptions and stress-test strategic choices.
Analytics: AI can lower the barrier to querying data and identifying patterns, allowing PMs to move from asking what happened? to exploring why might it have happened?
Communication: AI can help tailor narratives to different audiences, turn complex thinking into clearer communication, and accelerate the creation of product artefacts.
Execution: AI can reduce the cost of creating specifications, prototypes, experiments and other artefacts, giving PMs more time to focus on judgement and decision-making.
In each case, AI is not the competency.
AI is the multiplier.
So what should Product Leaders do?
My recommendation is to resist the temptation to immediately add “AI” to your Product Management competency framework.
Instead, take a step back.
Start with the competencies you already believe define great Product Managers in your organisation.
Then ask, for each one:
How could AI amplify this capability?
And then go one step further:
What level of AI literacy does a PM need to use that capability effectively?
This creates a much more meaningful conversation.
Rather than asking whether a PM knows AI, we can ask whether they can apply AI effectively within their product responsibilities.
That is a much higher bar.
AI literacy should be contextual
I don’t believe every Product Manager needs to become an AI expert.
But I do believe Product Managers increasingly need to become AI fluent.
There is an important difference.
AI fluency isn’t about knowing every new model, tool or prompting technique.
It’s about understanding where AI can create leverage, where it cannot, how to work effectively with it, how to evaluate its outputs, and, critically, when not to use it.
That last point may become one of the most important Product Management skills of all.
As AI becomes more capable, the differentiator won’t simply be who can generate the most output.
It will be who can exercise the best judgement about what should be generated, what should be trusted, what should be challenged and what decisions still require human reasoning.
The future PM competency framework
Perhaps the future isn’t a competency framework with “AI” sitting somewhere in the middle.
Perhaps it’s a framework where AI is embedded across the entire system.
AI becomes part of how we define discovery.
How we define strategy.
How we define customer understanding.
How we define execution.
And ultimately, how we define what good Product Management looks like.
So rather than adding another box labelled AI to the competency framework, I’d challenge Product Leaders to redesign the boxes themselves.
The question isn’t: “Does this PM have AI skills?”
The better question is:
“How effectively does this PM use AI to amplify their product capabilities while maintaining the judgement to know where AI should and shouldn’t be used?”
That’s a much more useful measure of AI maturity in Product Management.