Welcome folks! 👋

This edition of The Product-Led Geek will take 8 minutes to read and you’ll learn:

• How to build and sharpen 10 essential growth skills - from analytics and experimentation to storytelling and strategy - using AI as a daily force multiplier.

• Practical frameworks for collaborating with AI effectively: starting small, breaking down problems, creating feedback loops, and developing reflexive habits.

• Guardrails and pitfalls to avoid when applying AI in growth work, ensuring you focus on judgment, strategy, and verified insights rather than blindly trusting outputs.

Let’s go!

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10 Must-Have Skills for Product Growth in 2025 - And How AI Can Supercharge Every One

OK - now that you know that AI isn’t going to make your growth role obsolete - here’s a short guide on how to use it to become the most effective growth IC on your team.

The New Growth Stack Is About AI Leverage

In last weeks post, we established that AI isn’t a threat to your relevance.

Instead it’s the accelerant that lets you play at a higher level in every area that matters.

I’ve seen this first-hand with companies I’ve been working with - the best growth PMs today are relentless about using AI as a force multiplier.

They use AI to uncover deeper insights, run bolder tests, and make better decisions.

But what does this actually look like in your day-to-day?

I this post I break down 10 essential growth skills - and how you can use AI to build, sharpen, and supercharge each one.

Learning to Think Like an AI-Augmented Growth PM

Before we dive into specific skills, I want to talk about how to develop your AI collaboration instincts.

This is the meta-skill that makes everything else work.

Start with Small Experiments

The biggest mistake I see folks make with AI is trying to boil the ocean.

They read about some impressive capability and immediately try to automate their entire workflow.

This usually ends in frustration.

Instead, start small:

• Pick one repetitive task you did today

• Spend 5 minutes trying to get AI to help

• Note what worked and what didn't

• Try again tomorrow with what you learned

Break Problems Down

Effective AI collaboration is about decomposition. Take any growth challenge and ask:

• What parts involve pure computation or data analysis?

• What parts need human judgment or stakeholder buy-in?

• What parts are about generating options vs. evaluating them?

The more clearly you can separate these, the better you can leverage AI for each piece.

Build Learning Loops

Every time you work with AI:

1. Start with a clear goal ("I want to analyse these user interviews")

2. Try a prompt

3. Rate the output: Was it useful? Why or why not?

4. Refine and try again

Save your best prompts. Share them with your team. This compounds quickly.

Develop New Reflexes

The goal isn't to memorise AI features. It's to develop new instincts:

• When you open a spreadsheet, think "Could AI help structure this?"

• When writing a status update, think "Could AI help me frame this better?"

• When stuck on a problem, think "What would AI suggest?"

These reflexes take time to build. Start consciously, until they become automatic.

Now, let's look at how to apply this mindset to ten crucial growth skills...

1. Data Analytics & Metric Fluency

What's the skill?

The ability to ask smart questions about your data and translate numbers into actionable insights. This means knowing how to build cohorts, analyse retention, and identify where your funnel needs work.

Learning Path:

Level 1: Start Here

• Pick one metric you look at daily

• Ask AI to explain what might cause changes in this metric

• Try asking follow-up questions until you hit something insightful

• Time investment: 10 minutes

Level 2: Build Momentum

• Feed AI 3 months of your key metrics

• Ask it to spot patterns you might have missed

• Challenge its assumptions - this trains both the AI and you

• Time investment: 30 minutes

Level 3: Advanced Analysis

• Use AI to build complex cohort analyses

• Have it generate SQL for deeper investigation

• Compare its insights with your product intuition

• Time investment: 1 hour+

Common Pitfalls:

• Trusting AI insights without verification

• Getting lost in data without asking "So what?"

• Forgetting to document useful prompts

2. Experimentation & A/B Testing

What's the skill?

Running experiments that actually teach you something useful about your product. This means knowing how to design tests that matter, interpret results correctly, and turn findings into action.

Learning Path:

Level 1: Start Here

• Take your latest A/B test idea

• Ask AI to poke holes in your hypothesis

• Have it suggest three variations you hadn't considered

• Time investment: 15 minutes

Level 2: Build Momentum

• Feed AI your last 5 test results

• Ask it to find patterns you missed

• Use it to draft your next three test hypotheses

• Time investment: 45 minutes

Level 3: Advanced Testing

• Use AI to simulate test outcomes before running

• Generate multiple test variants at once

• Build automated test analysis templates

• Time investment: 1-2 hours

Common Pitfalls:

• Letting AI generate variants without a clear hypothesis

• Not saving successful prompt templates

• Forgetting to add context about your users

3. Funnel Modelling & Customer Journey Mapping

What's the skill?

Seeing your product through your users' eyes and spotting where they get stuck. This means mapping journeys that make sense, finding the real friction points, and knowing which improvements will matter most.

Learning Path:

Level 1: Start Here

• Pick one user segment

• Ask AI to map their typical journey from your raw event data

• Compare this with what you expected

• Time investment: 20 minutes

Level 2: Build Momentum

• Feed AI user feedback alongside journey data

• Have it identify disconnect points

• Use it to simulate impact of proposed changes

• Time investment: 1 hour

Level 3: Advanced Mapping

• Build AI-powered journey prediction models

• Generate automated friction reports

• Create dynamic journey visualisations

• Time investment: 2+ hours

Common Pitfalls:

• Trusting the map more than real user feedback

• Not updating journey models as product changes

• Focusing on too many segments at once

4. Technical Acumen

What's the skill?

Speaking the language of engineering well enough to get things done. This means knowing how your product actually works, being able to scope realistic experiments, and fixing basic tracking issues yourself.

Learning Path:

Level 1: Start Here

• Pick one feature you work with often

• Ask AI to explain its technical architecture in simple terms

• Practice explaining it back to yourself

• Time investment: 15 minutes

Level 2: Build Momentum

• Feed AI your tracking implementation docs

• Have it explain your event structure

• Use it to troubleshoot basic tracking issues

• Time investment: 30 minutes

Level 3: Advanced Technical Work

• Learn to write basic SQL queries with AI help

• Generate and modify tracking specs

• Debug data inconsistencies

• Time investment: 1 hour+

Common Pitfalls:

• Using AI without understanding the basics

• Not verifying generated code with engineers

• Forgetting to document what you learn

5. User Empathy & UX Insight

What's the skill?

Really getting inside your users' heads. Not just what they do, but why they do it - and more importantly, why they don't do what you want them to do.

Learning Path:

Level 1: Start Here

• Grab 5 recent support tickets

• Ask AI to identify common emotional themes

• Compare with your own reading

• Time investment: 15 minutes

Level 2: Build Momentum

• Feed AI interview transcripts

• Have it map user journey pain points

• Use it to generate better interview questions

• Time investment: 45 minutes

Level 3: Advanced Insight Work

• Build sentiment analysis workflows

• Create automated user insight reports

• Generate hypothesis backlogs from patterns

• Time investment: 2+ hours

Common Pitfalls:

• Letting AI replace actual user conversations

• Over-focusing on negative feedback

• Missing emotional context in AI analysis

6. Cross-Functional Collaboration & Influence

What's the skill?

Getting things done through others when you don't control their roadmap. This means translating your ideas into language each team cares about and building the right coalitions to ship.

Learning Path:

Level 1: Start Here

• Pick one upcoming cross-team meeting

• Feed AI the context and ask for stakeholder-specific talking points

• Practice translating your growth metrics into their KPIs

• Time investment: 15 minutes

Level 2: Build Momentum

• Have AI analyse past successful projects

• Map communication patterns that worked

• Build templates for different audiences

• Time investment: 45 minutes

Level 3: Advanced Influence

• Create stakeholder-specific dashboards

• Build automated update formats

• Generate influence strategy playbooks

• Time investment: 1-2 hours

Common Pitfalls:

• Over-relying on AI-generated language

• Not personalising templates enough

• Forgetting the human element

7. Communication & Storytelling

What's the skill?

Making people care about your data. This means turning dry metrics into stories that convince people to change direction or approve resources.

Learning Path:

Level 1: Start Here

• Take your latest product update

• Ask AI to reshape it as a story

• Note which version gets more engagement

• Time investment: 10 minutes

Level 2: Build Momentum

• Feed AI successful past communications

• Extract patterns and frameworks

• Build your personal story template

• Time investment: 30 minutes

Level 3: Advanced Storytelling

• Create multi-format narratives

• Build presentation frameworks

• Develop executive communication playbooks

• Time investment: 1+ hours

Common Pitfalls:

• Letting AI write the whole story

• Missing emotional resonance

• Over-complicating simple messages

8. Hypothesis-Driven Mindset

What's the skill?

Making better bets on what will work. This means turning hunches into testable predictions and learning fast from both wins and failures.

Learning Path:

Level 1: Start Here

• Take your latest product idea

• Ask AI to turn it into 3 testable hypotheses

• Rate each hypothesis for effort vs. impact

• Time investment: 15 minutes

Level 2: Build Momentum

• Feed AI your last quarter's experiments

• Build a hypothesis improvement framework

• Create prediction scorecards

• Time investment: 45 minutes

Level 3: Advanced Hypothesis Work

• Generate hypothesis trees

• Build automated validation frameworks

• Create learning loop databases

• Time investment: 1-2 hours

Common Pitfalls:

• Writing vague hypotheses AI can't help test

• Not tracking prediction accuracy

• Forgetting to update your priors

9. Creativity & Innovation

What's the skill?

Coming up with ideas that aren't just different, but better. This means generating lots of possibilities, but knowing how to pick the ones worth betting on.

Learning Path:

Level 1: Start Here

• Pick one product problem

• Ask AI for 20 wild solutions

• Force yourself to rank them

• Time investment: 20 minutes

Level 2: Build Momentum

• Use AI to research parallel industries

• Adapt solutions from other domains

• Build innovation frameworks

• Time investment: 1 hour

Level 3: Advanced Ideation

• Create systematic innovation processes

• Build idea validation workflows

• Develop cross-industry pattern matching

• Time investment: 2+ hours

Common Pitfalls:

• Accepting AI ideas without critique

• Getting stuck in brainstorm mode

• Not connecting ideas to metrics

10. Business Acumen & Strategic Focus

What's the skill?

Knowing which metrics actually matter to the business. This means connecting your growth work to revenue, understanding unit economics, and picking battles that move company metrics, not just product metrics.

Learning Path:

Level 1: Start Here

• Pick your top growth metric

• Ask AI to map its connection to revenue

• Build a simple impact model

• Time investment: 20 minutes

Level 2: Build Momentum

• Feed AI your company's key metrics

• Create unit economics dashboards

• Model growth initiative impact

• Time investment: 1 hour

Level 3: Advanced Strategy

• Build predictive business models

• Create scenario planning tools

• Develop strategic evaluation frameworks

• Time investment: 2+ hours

Common Pitfalls:

• Getting lost in theoretical models

• Not grounding predictions in reality

• Forgetting about implementation costs

Learning Your Way to AI-Powered Growth

Effectively using AI in specific situations is important, but not enough.

The real skill lies in learning how to learn with AI.

Here's what that looks like in practice:

Start Small, Learn Fast

• Pick one skill from above

• Spend 15 minutes each day with AI

• Document what works and what doesn't

• Build your personal playbook

Create Learning Loops

• Save your best prompts

• Share wins with your team

• Build on others' discoveries

• Keep pushing the boundaries

Remember the Fundamentals

• AI is your co-pilot, not your replacement

• Focus on judgment and strategy

• Let AI handle the grunt work

• Always verify the important stuff

So where does that leave you now?

Well here are three things to do today or tomorrow:

1. Pick your weakest skill above

2. Try the Level 1 exercise

3. Share what you learn with one colleague

I keep thinking about what makes someone truly effective with AI right now.

It's rarely about having early access to the latest models or memorising prompt techniques.

The people doing the most interesting work simply dive in daily, stay curious, and build their own way of thinking alongside these tools.

They’ve learnt how to learn with AI.

Your move.

THAT’S A WRAP

Before you go, here are 3 ways I can help:

Take the FREE Learning Velocity Index assessment - Discover how your team's ability to learn and leverage learnings stacks up in the product-led world. Takes 2 minutes and you get free advice.

Book a free 1:1 consultation call with me - I keep a handful of slots open each week for founders and product growth leaders to explore working together and get some free advice along the way. Book a call.

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That’s all for today,

If there are any product, growth or leadership topics that you’d like me to write about, just hit reply to this email or leave a comment and let me know!

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Until next time!

— Ben

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