AI & ML

AI is Transforming Product Management

By
Joel Passen
May 23, 2023
5 min read

Product management has evolved significantly over the years, adapting to customer expectations, increased competition, and technological advancements. What was once a fragmented and operational role is now one of the most strategic and cross-functional disciplines in many organizations. Modern product management has two key goals; delivering customer value and, subsequently, driving business growth.

In the past, product managers were often hardened SMEs relying on previous experiences and assumptions. However, in recent years, subject matter expertise has become less critical as the focus has shifted to better understanding a wide variety of users. Product managers now emphasize understanding customer needs, conducting user research, and gathering feedback to inform feature prioritization and, more importantly, the desired value-driven outcomes for customers. 

These days, PMs have a massive amount of data at their disposal, enabling experimentation and validation. This iterative mindset helps mitigate risks, validate hypotheses, and optimize product features based on user feedback. And, to that end, user feedback is the key — the currency of modern product management feedback is what makes or breaks a product. 

Traditional user feedback methods are a miss

Traditionally, user feedback has been gathered through surveys and customer interviews. Surveys are emailed to customers to gather feedback. Pretty straightforward. Customer interviews allow PMs to dig deeper into customer motivations, pain points, and specific use cases.  

What do traditional product research methods have in common? They are labor-intensive, often expensive, and time-consuming, requiring reliance on other teams to complete. And, maybe the biggest drawback is that the data and insights generated are typically from a small subset of the product’s user base. 

Welcome to the AI-era

AI-powered platforms are making it possible to sift through data using natural language processing (NLP) and machine learning algorithms to quickly analyze large amounts of customer-generated information like email, tickets, call transcripts, and more. These data sets have been nearly impossible to access in the past because of their unstructured nature. AI-based tools can search for patterns and recognize key signals that might be difficult and even impossible for humans to spot, especially at scale. 

AI is catapulting PM teams into a new era by enabling them to quickly and accurately identify trends in user preferences and behaviors related to specific feature requests, common product defects, and areas the most prone to user confusion. 

Additionally, AI-based platforms analyze vast amounts of data in real-time, helping product managers iterate and experiment faster while reducing costs and reliance on other teams. With the help of AI, teams can gain valuable, unbiased insights into their products more efficiently and more effectively than ever before.

Real use case examples are maturing 

For example, product teams at HireEZ, the award-winning outbound recruiting solution, use Sturdy to slice and dice real user feedback like feature requests, confusion, and product friction by product mix, segments, and cohorts to understand better how to maintain product-market fit across their customer base. 

Teams at MP, a leading provider of innovative HR technology and managed HR services, no longer rely solely on surveys and interviews to understand customers better. They use the real voice of the customer from email and support tickets to better identify opportunities to improve their offerings. 

It’s no surprise that AI is transforming product management. The function was poised for evolution. AI is now simply accelerating more teams to become even more customer-focused, data-driven, collaborative, and iterative. Product managers are embracing digital transformation, agile methodologies, and a customer-centric approach to navigate complex market dynamics and deliver innovative products.

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Customer Churn

The Most Dangerous Threat to CROs

Joel Passen
July 1, 2025
5 min read

The most dangerous threat to CROs doesn’t live in the opportunity pipeline.

It's churn.

  • It doesn’t scream like a missed quarterly pipeline goal.
  • It doesn’t show up in dashboards until it’s too late.
  • It's rarely caught by a generic 'health score'.
  • It's the board meeting killer.

Retaining and growing our customers is the only repeatable, compounding, capital-efficient growth lever left in B2B businesses.

📉 CAC is way up.

📉 Channels are saturated.

📉 Talent is expensive.

📉 Competition is fierce.

📉 Switching costs are low.

The path to $100M used to be “sell, sell, sell.”

Today? It’s “land, retain, expand.”

No matter how strong your sales motions are or how slick your product or service looks during the sales process, if your customers are churning, you’re stuck in a leaky bucket loop of doom.

Every net-new dollar you win is offset by dollars you lose. It's just math.

Yet most GTM orgs still operate like retention is someone else’s problem. "That's a CS thing."

  • The CS team might “own” the customer post-sale.
  • Account Management may own the renewal and growth number.
  • Support is in the foxhole on the front line.
  • RevOps might model churn with last quarter’s data.
  • Marketing might send an occasional newsletter via email.
  • Finance may be leaning in on the forecasting.
  • Product is building things that supposedly the customers want.

But in reality, churn is the CRO's problem. We wear it - or should.

If your go-to-market motion isn’t designed to protect and grow customers from Day 1, you’re not just leaving money on the table — you’re setting fire to it.

Retention and expansion aren’t back-end functions. They’re front-and-center revenue motions.

The most valuable work these days starts after the contract is signed — not before.

We need to stop treating post-live as a department and start treating it as the engine of durable growth.

Software

Have you heard this from your CEO?

Joel Passen
April 29, 2025
5 min read

"How are we using AI internally?"

The drumbeat is real. Boards are leaning in. Investors are leaning in. Yet, too many leaders hardly use it. Most CS teams? Still making excuses.

🤦🏼 "We’re not ready."Translation: We don't know where to start, so I'm waiting to run into someone who has done something with it.

🤦🏼 "We need cleaner data."Translation: We’re still hoping bad inputs from fractured processes will magically produce good outputs. Everyone's data is a sh*tshow. Trust me. 🤹🏼♂️ "We're playing with it."Translation: We have that one person messing with ChatGPT - experimenting.

😕 "Just don't have the resources right now."Translation: We're too overwhelmed manually building reports, wrangling renewals, and answering tickets forwarded by the support teams.

🫃🏼 "We've got too many tools."Translation: We’re overwhelmed by the tools we bought that created a bunch of silos and forced us into constant app-switching.

🤓 "Our IT team won't let us use AI."Translation: We’ve outsourced innovation to a risk-averse inbox.

It's time to put some cowboy under that hat 🤠 . No one’s asking you to rebuild the data warehouse or perform some sacred data ritual. You don’t need a PhD in AI.

You can start small.

Nearly every AI vendor has a way for you to try their wares without hiring a team of talking heads to perform unworldly 🧙🏼 acts of digital transformation.

Where to start.

✔️ Pick a use case that will give you a revenue boost or reveal something you didn't know about your customers.

✔️ Choose something that directs valuable work to the valuable people you've hired.

✔️ Pick something with outcomes that other teams can use.

Pro Tip: Your CEO doesn't care about chatbots, knowledgebase articles, or things that write emails to customers.

What do you have to lose? More customers? Your seat at the table?

CX Strategy

Talent gets you started. Infrastructure gets you scale.

Joel Passen
April 29, 2025
5 min read

We obsess over hiring A-players. But even the best GTM talent will flounder if the foundation isn’t there.

I’ve seen companies overpay for “rockstars” who quit in 6 months—not because they weren’t capable, but because they were dropped into chaos. No ICP. Bad data. No process. No enablement. No system to measure or coach.

Great GTM teams aren’t built on purple squirrels. They’re built on a strong foundation.

That foundation looks like this:

✅ A crisp, written ICP and buyer persona (not just tribal knowledge)

✅ Accurate prospect data to target the right ICP

✅ A playbook that outlines how you win—and how you lose

✅ A clear point-of-view that your team can rally around in every email, call, and deck

✅ Defined stages, handoffs, and accountability across marketing, sales, CS

✅ A baseline reporting system to see what’s working—and what’s not

When this exists, you can onboard faster, coach better, and scale smarter. It's not easy, and it’s not sexy, but it works.

Want to cut CAC and increase ramp speed? Start with your infrastructure. Hire into a structure.

How many customers will you have to lose before you try Sturdy?

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