Welcome folks! 👋

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

• What AI is really automating in product growth (and what it’s not)

• Why the most valuable skills in growth are shifting from execution to judgement, influence, and synthesis

• How to audit your own workflow - and “move to higher ground” before the next AI wave hits

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The New Growth PM Playbook: What AI Can't Replace

A quick guide on how to future-proof your career in product growth as AI automates the busywork.

Are Product Growth Roles Doomed by AI?

If you work in product growth today, you’re either excited, anxious, or both about the rise of AI.

I think that you should definitely be excited.

But I also think a touch of anxiety here is both warranted, and healthy.

Every week you’ll see some advert about a new tool that will 10x your funnel analysis, write onboarding copy for you, or predict which features will drive expansion.

For those in growth, it can be fatiguing and also fear inducing.

AI is absolutely changing the way product growth work gets done.

If you’re on the right side of the shift, the future of growth roles is about being augmented by AI and about rising to a new level of value as the tactical work gets automated away.

I’ve been working in product-led SaaS for a long time now.

I remember when - amongst other things - growth involved hammering through SQL queries at silly o’clock in the morning, 20-tab spreadsheet models, and (shock horror!) writing onboarding tooltips by hand!

Today?

We have AI tools summarising and generating insights from large scale survey feedback, generating experiment ideas, drafting A/B test copy, predicting churn risk, and optimising email send times - before you’ve had your morning coffee.

So the uncomfortable truth is that if you’re not consciously evolving, the role may not disappear, but your seat might.

AI is already eating the low-leverage parts of product growth.

So what should you do?

Simple.

Use AI as a force multiplier, not a crutch - and double down on the distinctly human skills that tools can’t replicate.

If you want to not only survive but thrive as a growth practitioner in the AI era, read on.

AI Is Already Eating the Low-Leverage Work—And That’s Good News

Let’s be clear.

AI is not poised to replace the need for product growth ICs.

But it is already making a lot of busywork obsolete.

What’s getting automated? Here’s a non-exhaustive list.

• Data Cleaning and Aggregation: AI tools now ingest raw event data, surface anomalies, and even recommend which segments to dig into. You probably used to spend hours on this per week - now it’s minutes.

• Copywriting for Tests and Onboarding: Generative AI drafts onboarding flows, upgrade prompts, even release notes. Sure, you still need to edit, but the blank page problem is gone.

• Synthesising User Feedback: AI can summarise thousands of survey responses, support tickets, or user interviews - flagging the most common themes and suggesting follow-up questions.

• Experiment Ideation: Gone are the days of staring at the ceiling for new test ideas. AI can suggest dozens of hypotheses, prioritised by likelihood of impact, based on your funnel data and industry benchmarks.

• Campaign/Journey Orchestration: AI now helps map out user flows, trigger points, and personalised paths based on behavioral patterns and cohort analysis.

• Reporting & Visualisation: Automated generation of growth reports, custom dashboards, and data visualisations - with AI suggesting which metrics are most relevant to track.

• Competitive Analysis: AI tools can monitor competitor websites, app store listings, and social presence to flag significant changes and feature launches.

• Localisation & Market Adaptation: Automated translation and cultural adaptation of growth assets (copy, images, flows) for different markets.

• Predictive Modeling: AI forecasting user behaviors, churn likelihood, and potential lifetime value - helping prioritise where to focus growth efforts.

• Landing Page Generation: AI tools creating and iterating landing page variants based on historical performance data and industry best practices.

• Email & Message Timing: Automated optimisation of when to send communications based on individual user engagement patterns.

• Audience Segmentation: AI-driven clustering of users into actionable segments based on behavior patterns and characteristics.

• …

The great thing about all this is that it if you become adept as using the tech for these things, you’ll clear a whole lot of time and mental space for higher leverage work.

One under-discussed side effect of AI is that build speed is exploding.

What the Best Growth PMs Are Doing Differently

The best growth ICs are using AI as a co-pilot.

Here are some of the common applications I’m seeing.

1. Accelerating the Experiment Loop

AI now drafts multiple variants for A/B tests, from in-app copy to onboarding flows. You can test more, faster, and with better coverage of ideas.

A growth squad at one of my advising clients used ChatGPT to generate a bunch of onboarding headline variants. They ran a multi-armed bandit test, found a stat-sig activation uplift, then used AI again to localise and roll out the winning variant to multiple markets - all in a single sprint.

Bonkers.

2. Supercharging Analysis

AI-powered analytics tools spot trends, outliers, and correlations that would take hours (or days) to notice manually. The best ICs use these insights to ask better, deeper questions.

Tip: Use AI for the “what” and “where.” Use your product sense for the “why” and “what next.”

3. Scaling User Research

AI can cluster jobs to be done from free-text data, suggest personas, or even simulate user reactions to new features (with a healthy pinch of salt!).

Cautionary tale: I’ve seen a team trust and apply an AI-generated persona that didn’t wholly reflect their actual buyers - until a quick round of real interviews set them straight. AI is a powerful assistant, but it’s not a substitute for talking to customers.

4. Reducing Toil, Unlocking Creativity

By offloading repetitive work, AI gives you back the most precious growth commodity: time for thinking, synthesis, and creative problem solving.

The opportunity is not to use AI to do your job faster.

The real opportunity is to do a different job: more strategic, more collaborative, more impactful.

What AI Still Can’t Do (and Why This Is Your Edge)

AI excels at pattern recognition, synthesis, and suggestion.

But (at least for now) it’s fundamentally limited in some crucial areas:

1. Defining the Right Problems

AI can generate hundreds of hypotheses, but it has no feel for what’s truly painful or valuable. The best growth work comes from solving the really key problems - the ones customers are desperate for you to fix, where a better solution changes their world.

Spotting those problems requires taste, context, and judgment. AI can highlight anomalies in your data, but it can’t tell you which ones are important enough that people will pay, stay, and spread the word.

That’s still your edge.

Sure, AI can analyse historical data patterns to suggest prioritisation between metrics like activation and expansion, but making these strategic trade-offs still benefits significantly from human judgment that incorporates:

• Market context and timing (e.g. shifting competitive dynamics)

• Company-specific constraints and opportunities

• Qualitative insights from customer conversations

• Risk tolerance and company culture

• Second-order effects that may not be captured in historical data

Given sufficient high-quality historical data and clear success metrics, AI can absolutely help inform these decisions.

However, the key distinction is that AI excels at pattern recognition within known parameters, while humans excel at incorporating novel contexts and making judgment calls with incomplete or ambiguous information.

2. Synthesising Context and Ambiguity

While AI excels at processing structured data and identifying patterns, it struggles with the rich contextual intelligence that experienced growth professionals bring to the table:

• Understanding company politics and stakeholder dynamics (e.g. knowing when to push for a controversial experiment vs. building consensus first)

• Reading market timing and competitive shifts that aren't yet reflected in the data

• Interpreting subtle user behaviors and feedback signals that come from years of pattern recognition

• Balancing conflicting priorities across different teams and departments

• Knowing when to trust the data vs. when something feels "off" based on experience

For example, AI might flag that your enterprise activation rate dropped by 10% last quarter, but a human would need to explain that's because sales shifted focus to a new vertical with a longer evaluation cycle - and that this is actually aligned with the company's strategic direction.

The key is using AI to surface insights from your data, while relying on human judgment to layer in the organisational context, market understanding, and experiential wisdom that makes those insights actionable.

3. Building Trust and Influence

No AI can navigate the complex human dynamics of cross-functional growth work. For example:

• Convincing a skeptical sales leader that your new PQA definition won't hurt their quota by walking them through real customer stories and conversion paths

• Getting core-product team buy-in for your experiment by joining their planning meetings, understanding their constraints, and finding win-win solutions

• Building trust with marketing by proactively sharing insights from your activation experiments that could improve their campaigns

• Reading the room in a tense product review and knowing when to push vs when to build more consensus

The skills of influence, relationship-building, and organisational storytelling remain distinctly human. They require emotional intelligence, political savvy, and the ability to adapt your message based on subtle social cues and shifting team dynamics.

4. Making Strategic Trade-offs

Should you optimise for short-term conversion or long-term expansion?

Should you follow a competitor's pricing model or double down on your unique value prop?

These are not formulaic decisions.

They're complex trade-offs that require deep context and judgment.

The right answer of course is to use AI to inform these decisions - not make them.

Let AI crunch the numbers and surface patterns, but keep the final judgment in human hands where context, intuition, and calculated risk-taking matter most.

If your job is asking why, not just what, you’re on higher ground.

The New Growth Skill Stack

Something I see becoming increasingly problematic is that too many product growth ICs are over-invested in the skills AI is automating.

If your value is wrapped up in writing onboarding copy, building dashboards and running SQL queries, AI is already as good (or better) at those things than junior-mid level PMs.

The most valuable thing that growth PMs can do today is to start leaning into this skill stack:

1. Strategic Focus: Understanding which metrics matter right now for your business, and ruthlessly prioritising.

2. Judgement and Synthesis: Combining data, user insight, and business context to make calls AI simply can’t.

3. Cross-Functional Influence: Getting buy-in for changes across product, engineering, marketing, and sales.

4. Storytelling: Translating complex findings into narratives that move execs and teams to action.

5. User Empathy: Interpreting not just what users do, but why - and advocating for solutions that serve both users and business.

6. AI Leverage: Not just “using” AI, but orchestrating it - knowing which tasks to automate, and where to step in yourself.

Beyond the core skill stack, the growth PMs who are thriving in this AI-shift share some common habits:

• Curiosity + Humility: They assume they don’t know everything and are willing to learn from anyone.

• Bias for Action: They act without waiting for permission - they treat growth problems like owners.

• Data Discipline: They stress-test AI answers, run their own checks, and know where hallucinations creep in.

• Hands-On with Tools: They aren’t spectators. They try the models, build scrappy prototypes, and get a real feel for what works.

How to Audit Your Workflow and Move to Higher Ground

Here’s a practical exercise.

Spend a week tracking your time.

For each task, ask:

• Could a decent AI tool do this today (or soon)?

• Am I spending my best hours on judgement, synthesis, and collaboration - or on grunt work?

How to move up the stack:

• Automate ruthlessly: Use AI for analysis, copy, and experiment ideation.

• Double down on ambiguous work: Volunteer for projects that require context, cross-team alignment, or novel insight.

• Sharpen your business acumen: Become an expert in business metrics, pricing strategy, and how to tie growth experiments to revenue.

• Invest in relationships: Spend more time with engineering, sales, and customer success. Influence will always be a human game.

• Level up your storytelling: Present not just data and results, but the story - what it means, why it matters, and what to do next.

These are mainly skills that have ALWAYS been important in growth - but in more equal balance with the necessary tactical supporting work.

Today though, that balance point has shifted.

The supporting work can be automated and you get to double down on the leverage creating activities that AI still isn’t good at.

Bonus: Start a “learning log” of how you’re using AI - what works, what doesn’t, and what new opportunities you’re spotting. Share this with your team. You’ll quickly become the go-to for “how do we use AI well here?”

AI Won’t Replace Growth Roles - But It Is Changing The Role - For The Better

We’re entering an era where AI handles the repetitive, the rote, and the routine.

Product growth ICs who cling to old-school execution will get outpaced - not by AI, but by peers who wield AI as a force multiplier.

Your edge?

• Strategic focus

• Synthesising ambiguity

• Building trust and influence

• Telling the story behind the numbers

• Never losing sight of the why behind the what

If you lean into these, you’ll be well placed to ride the AI wave instead of being swamped by it.

The future of product growth does mean doing more with less.

But critically it’s also about doing what only you can do.

Use AI create the space for you to do just that.

THAT’S A WRAP

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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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