Someone on the team — an engineer, a designer, maybe a PM — builds a demo. It’s impressive. It works. It solves a problem that customers have been complaining about, or it does something that wasn’t possible a year ago. They show it to the team. The team gets excited. They show it to leadership. Leadership gets excited. And then, if you’re lucky, someone says, “Let’s get this in front of a few design partners.” More often, leadership says, “Let’s ship this: Let’s invest in it. Let’s build it out. Let’s market it.” A roadmap materializes. Resources get allocated. The machine starts moving.

And at no point in this sequence do the decision-makers step back and ask: Is this the most valuable problem we could be solving right now? For this company? For this product portfolio? Is this the right solution — or just the first one someone built?

I call this demo-driven development. And it’s been around for as long as I’ve been in product — nearly two decades.

Two Flavors, Same Root Cause

The pattern shows up in two ways.

In the first, the team hasn’t validated the problem at all. A customer complained, or a new technology made something cool possible, and the team jumped straight to building. The demo feels like evidence that they’re on the right track. It isn’t. It’s an assumption wrapped in working code.

In the second, the team has identified a real problem — but they’ve locked onto one solution without doing the compare-and-contrast work. They’re not asking whether this is the best possible solution, or which customers it serves and which it doesn’t. They’re not thinking about which market segments to address first or why. They go straight to finding a couple of design partners who’ll confirm the thing they built is useful.

And of course those partners will say it’s useful. If you find someone with a problem and hand them a working solution, they’ll say thank you. That doesn’t mean it was the best thing to build. It doesn’t mean it’s the best use of the company’s resources. And even if building is super cheap now, the time and attention of your customers is not. Their eyeballs and their minutes are a precious resource. We still need to think deeply and strategically about which ideas we put in front of them, when, and which conversations we have with them.

Both flavors share the same root cause: the demo substituted for strategic thinking. In one case, it replaced problem discovery. In the other, it replaced the diverge phase of solution discovery.

This Isn’t New. But AI Made It Worse.

Demo-driven development predates AI. It predates agile. It’s what happens whenever a team confuses having a working prototype with having a strategy.

But I’m naming it now because AI has made it dramatically more dangerous.

When it took months to build a prototype, the friction of building created a natural check. You had time to think, to question, to pressure-test assumptions before the thing existed. That friction is gone. AI can help a team go from idea to working demo in hours, sometimes minutes. The prototype is more polished, more convincing, and more emotionally compelling than anything we could build before. And every stakeholder in the room is going to look at it and think: why wouldn’t we ship this?

Here’s why: because a demo is not a strategy. A prototype that works is not evidence that you’ve found the highest-impact use of your team’s time. And the ability to build fast makes the cost of building the wrong thing higher, not lower — because you’ll build more wrong things, faster, and every one of them will consume resources, create expectations, and generate switching costs when you finally realize you need to change direction.

The Misconception Underneath

There’s a deeper issue here that I see in teams at every level of maturity: the belief that product discovery means putting something in front of a customer and getting feedback.

That’s part of discovery. It’s not all of discovery. And it’s not the part that prevents demo-driven development.

The part that prevents it is the strategic work that happens before you build anything — identifying who you’re building for, what pain you’re solving, what outcome you’re driving, and in what order. Figuring out what your evidence actually says about where the highest-value opportunity lives. Making tradeoffs explicit instead of letting the most exciting demo win by default.

Most teams skip that work. Not because they’re lazy or incompetent. Because nobody taught them that it’s a separate step — and because the demo is right there, working, waiting to be shipped.

If This Sounds Familiar

I’m not offering a solution in this post. That’s deliberate. The solution is an entire methodology — one I’ve spent nearly two decades developing, teaching, and refining. I’m writing a book about it right now, The Product Science Principles, and demo-driven development is where Chapter 2 begins.

But naming the problem is the first step. So the next time you’re in a room where everyone is excited about a demo and nobody is asking whether it’s the right thing to build — you’ll have a word for what’s happening.

And maybe that word will be enough to slow things down just long enough for the right questions to get asked.

I’m looking for beta readers for the book — people who’ll read early chapters and share their honest, in-the-moment reactions as they go. If that sounds interesting, reply and I’ll send you what I’ve got.

And whether or not you want to read early drafts, I’d love to hear from you: are you seeing demo-driven development in your organization? What does it look like? Hit reply and tell me — it’ll make the book better.

The most common setup I see right now: an AI agent that tallies feature requests across sales calls, support tickets, and customer transcripts. Clean ranked list. Updated in real time. Zero manual effort.

It feels productive. But it’s automating confirmation bias at scale.

Feature Requests Are Symptoms, Not Diagnoses

Feature requests are surface-level data. They tell you what customers think they want — not what they actually need. They reflect the solutions customers can imagine, which are always limited by what they’ve already seen.

This is the “faster horses” problem, dressed up in modern tooling. The observation attributed to Henry Ford has never been more relevant: if you ask people what they want, they’ll describe a slightly better version of what they already have. AI lets you aggregate those descriptions faster than ever. But speed doesn’t fix the underlying problem — you’re still listening to answers that come from the wrong questions.

When you use AI to synthesize feature requests, you get a beautiful, well-organized view of the surface. What you don’t get is any understanding of why those requests exist — which users feel the pain most acutely, what constraints they’re working within, and whether the features they’re asking for would actually solve the underlying problem.

What Happened When We Asked Different Questions

When I was leading discovery for Shutterstock Editor, we didn’t start by asking people what features they wanted in a photo editing tool. That question would have produced a predictable list: layers, filters, templates, text tools — the same features every editing tool already offers.

Instead, we asked about their goals. What were they making with the images they downloaded from Shutterstock? What were they using the edited images for? We asked about their jobs and how much time they had. We asked how confident they felt in their design skills.

These questions had nothing to do with the product category. They had everything to do with the customer’s world.

What we found was a segment that no feature request tally would have surfaced: people who needed to produce designed content but didn’t identify as designers. Bloggers. Social media managers. Small business owners putting together presentations. They had limited time and no patience for complex tools. Design work wasn’t their core job function or passion — they wanted the design step to take as little time as possible so they could get back to the work that actually mattered to them.

That insight is what let us scope an MVP around a specific, underserved segment instead of trying to build a Photoshop competitor for everyone. We launched to rave reviews from the target users who told us it saved them time. The fringe users — the ones who needed more — asked for exactly the features our research had predicted they’d want. Usage grew 10x in the first year.

No amount of feature request tallying would have gotten us there. The insight came from understanding outcomes and constraints, not from counting what people asked for.

What You Should Be Pointing AI At

AI is incredible at synthesizing large volumes of data. The question isn’t whether to use it — it’s what you point it at.

Most teams point AI at feature requests and bug reports. That’s downstream data. It tells you what people are reacting to in the product as it exists today. It’s useful for local optimizations — fixing what’s broken, smoothing what’s rough.

What you should be pointing AI at is the upstream context that helps you understand what products would actually be useful:

• The outcomes that matter to customers — not what features they want, but what they’re trying to accomplish in their work and life

• The frequency and depth of their pain — how often they hit the problem, and how much it costs them when they do

• The competitive alternatives they’re considering — including the option of doing nothing, which is your most common competitor

• The constraints they’re working under — time, budget, skills, organizational dynamics, technical limitations — the factors that determine whether a solution will actually fit into their world

AI can find traces of all of this in the data you already have — in sales call transcripts, in support conversations, in customer success notes, in usage patterns. But only if you ask it the right questions. An AI agent asked to “identify the top feature requests” will do exactly that, beautifully. An AI agent asked to “identify the most common unmet outcomes and the constraints preventing customers from reaching them” will give you something fundamentally more useful.

10% Moves vs. 10x Moves

Tallying feature requests to make local optimizations — that’s entry-level product work. It produces 10% improvements. There’s nothing wrong with it, and every product needs some of it. But it’s not where senior PMs should be spending their time and attention.

Understanding pain depth, frequency, competitive alternatives, and adoption constraints — that’s what produces the 10x moves. The ones that change the trajectory of a product. The ones that find a new segment, redefine a value proposition, or reveal that the biggest opportunity is somewhere nobody was looking.

If you’re a senior PM, a staff PM, a product leader, and your primary use of AI is counting feature requests, you’re flying at the wrong altitude.

The Interview Still Matters Most

Here’s the thing AI can’t do: sit across from a customer and notice that they said “it’s fine” but didn’t mean it. Follow an unexpected thread because something in their tone of voice told you there was more to the story. Build enough rapport that someone admits they’ve been doing a workaround they’re embarrassed about. Notice that this person’s experience of the problem is fundamentally different from the last person’s, and adjust your questions on the fly to understand why.

Discovery interviews — real conversations with real people about their real lives — are still the highest-value thing a product manager can do. AI has made everything around the interview faster: transcription, summarization, theme extraction, pattern recognition across dozens of conversations. What used to take fifty hours of processing can now be done in two.

But the interview itself is irreducibly human. And it matters more now, not less. When every company has access to the same AI-generated market data, the same AI-summarized support tickets, the same AI-clustered usage patterns, the competitive advantage shifts to whoever has proprietary insight that AI can’t generate. That insight comes from direct conversation. It comes from asking the questions that surface what you don’t know yet — not the questions that confirm what you already believe.

AI hasn’t changed what great discovery looks like. It’s changed how fast you can do it — for better or worse. The PMs who win in the AI era won’t be the ones who process the most data. They’ll be the ones who ask the right questions — of the data and of their customers.

I’ve been thinking about this a lot as I write my book, The Product Science Principles — a guide to the repeatable, science-based methodology behind high-growth products. These ideas are also at the core of Ada, an AI coaching tool I recently built to help PMs sharpen their discovery interview guides. Because the questions you ask determine whether you find 10% insights or 10X ones.

If you want to try Ada or hear more about the book as it develops, reach out — I’d love to hear what you’re working on.

So I’m just gonna say it now.

Last August, I started at Datadog. I just hit the 6-month mark.

I am group product manager for observability data platforms. I work with the teams that ingest, index, store, and query all of the observability data that Datadog clients and engineers rely on every day.

For those unfamiliar with the observability space: Datadog is like the dashboard of a car—it tells you what’s going on inside the complex machine. But instead of being a dashboard for a car, Datadog is the dashboard for software programs.

And I can honestly say that aside from myself, Datadog is the best employer I’ve ever had.

What Was Holding Me Back

For eight years, I ran Product Science Group. I built it from nothing to $880K in revenue by 2024, with 117% year-over-year growth. I worked with Fortune 500 companies like Pfizer and Novartis. I coached product leaders at growth-stage startups like GTreasury and FalconX. My podcast hit nearly 100,000 listens. I taught at NYU Stern.

I was proud of what I’d built. I loved the work—helping product leaders develop evidence-based strategies, watching teams I’d coached ship products that users loved, seeing the Product Science Method take root in organizations.

Then 2025 hit.

The first quarter was brutal. The economy was in disarray due to the threats and reversals of tariff plans, as well as the impact of AI on corporate leaders' spending decisions. Corporate budgets froze. Or more accurately, they redirected—everything went to AI, and only AI.

My consulting pipeline dried up. And I had to face the reality that this wasn’t a temporary blip—the market had fundamentally shifted. The only two things selling in tech consulting were: (1) AI-specific expertise, or (2) helping laid-off product managers find jobs.

The Decision I Didn’t Want to Make

I considered both pivots, but neither felt right to me.

I love the craft of product management. I didn’t want to move further away from it by focusing on coaching job-seekers. And I didn’t feel like I knew enough to sell myself as an expert on AI—so I wanted to go in-house to get closer to it.

That’s when I realized: No matter what I think about AI, that parade is marching on. I’d rather help guide the parade than stand on the sidelines.

But to guide it, I needed to be in it.

On April 1, 2025, I started looking for a product leadership job.

Why Datadog

I wanted to get closer to AI—the close you can only get when you work in it day in and day out. But I didn’t want to work at Anthropic or OpenAI. Those seem like hypercompetitive, intense jobs.

I feel like I got as close to that as I can without actually working at one of those companies.

Datadog serves many AI-native companies, both frontier labs and startups building AI products. We deal with big data—my team manages the data pipelines that process trillions of data points daily across hundreds of terabytes. The company has a frontier model, Toto, for time series data. We’ve got AI platform teams to help the company adopt and use and build AI. We’re innovating with AI solutions in our product.

And it’s a great NYC-born public tech company. As a veteran of NYC startups and scale-ups, it felt like a natural next step.

On top of that, the work-life balance is amazing for a company so intense and successful. I can still be present as a single mom. The income and stability that I have, at least for now, is great.

Six Months In

I’m having fun. I’m learning. I got exactly what I wanted, in spades.

The technical scope is exhilarating. It’s the kind of systems thinking I love, but at a scale that pushes my understanding daily.

I get to see how the company processes all that data and how it makes decisions about what practices to follow. I’m leading the PM team for 500 engineers who work on data platforms that power Datadog’s core products. The discipline of evidence-based strategy isn’t theoretical here—it’s essential.

And I am loving it at Datadog. In many ways, I got exactly what I wanted—engaging and interesting challenges, top-notch teams, a flexible culture where I feel supported and happy to be a part of.

Full Circle

Here’s what makes this story feel complete: Product Science Group isn’t gone. It’s evolving.

What I’m learning at Datadog—about AI, about data platforms at scale, about product leadership in the age of LLMs—is already informing what comes next for PSG. My team at PSG is still with me, and we’re developing AI and product management training. We’re getting ready to kick that off in 2026 as one of our offerings.

The expertise I’m building in-house will make me a better teacher when I share it. Sometimes you need to step into the parade to understand how to help others join it.

This wasn’t the path I expected when I started Product Science Group eight years ago. But it’s exactly where I need to be right now—both building product at massive scale AND helping others get here too.

The craft of product management continues. It just looks different than I expected.

And I’m excited about both versions of it.

Let’s march in this parade, together.

Best,

Holly

Key takeaways from Holly’s conversation with Dina Levitan, founder and principal of Chill Labs and lead product consultant at the Product Science Group, include the importance of centering the human experience in product development, the power of diverse and empowered teams, and why evidence-based strategies can unlock innovation, even in unfamiliar territory.

Resources

Visit Chill Labs where Dina helps companies build products that users want and do so in a way that supports growth and scale.

Explore Dina’s personal site to explore her consulting and speaking services.

Connect with Dina on LinkedIn

Sign up for the “Automate Yourself” Newsletter at newsletter.chillaborate.com.

Follow Holly on LinkedIn

Visit the Product Science Group website

Explore Product Science Workshops and Courses

Quotes from Dina Levitan:

(00:01:31) “One of the questions I ask myself when I’m working on a product problem is how can I systematize the best practices and the issues that we’re seeing here so that we can remove that problem and never have it happen again. That’s one of those SRE principles that I like to keep top of mind...”

(00:05:21) “Whether it’s in engineering or product management or life generally, understanding the true nature of a problem and its root causes is one of the most interesting and exciting pieces of the puzzle for me and motivates me to do my best work.” - Dina Levitan

(00:16:13) “I know that for me, one of the more fun aspects of the job is facilitating workshops and seeing how ideas from each participant build on top of each other and help us get to a clear path forward.” - Dina Levitan

Lab Notes

Lab Note 608.1: Continuous Discovery & Delivery: Centering the human is key.

(00:27:09) “They followed the user journey as a query went through the different points of their system, and that one of the main things they were focusing on was how to keep that consistent customer experience and have a great customer experience consistently through all of those points.” - Mark Enache

Lab Note 608.2: Empowered Teams: Teams with diverse skillsets and perspectives foster greater understanding.

(00:28:04) “Being naturally and natively who you are in a business situation can help foster and develop just a greater understanding of what’s actually being said...” - John Haggerty

Lab Note 606.3: Evidence-Based Product Strategy: Gathering evidence leads to sound product strategy even in areas where pre-existing expertise is limited.

(00:29:05) “The energy of saying, you know, if you don’t have a pm, that’s a subject matter expert in this they’re doomed to fail. It’s very similar type of energy to saying, you know, we don’t need to gather evidence.” - Mark Enache

Lab Note 606.4: Building empowered teams requires both science and art.

(00:29:59) “When our clients are asking us to come in and help them introduce evidence-based product strategy, or indeed any company wants to introduce and uplevel a new type of product strategy, that is innovation.” - Mark Enache

Lab Note 606.5: Empathy and contextual understanding remain uniquely human.

(00:30:57) “Will the roles change? Yes, absolutely. They’ll change and evolve. That’s why knowing how AI works, getting ahead of it, understanding it and figuring out how you want to bring it into your role today is a very powerful way to go about it.” - John Haggerty

When an established brand buys an AI-first start-up, the deal is rarely about headline‐grabbing valuations. It is about compressing years of data-science hiring, model training, and product learning curves into a single leap. That leap only pays off if the acquirer can weave the new algorithms, data pipelines, and talent into its core products before competitors close the gap.

2. Five Integrations That Moved the Needle

1. Nike acquisition of Celect (predictive analytics, 2019)

   1. Integration Focus: Hyper-local demand predictions to cut stock-outs and overstock

   2. Notable Results: Celect models shrank inventory-planning windows from six months to 30 minutes, supporting Nike’s “Consumer Direct Offense” strategy.

2. Salesforce acquisition of MetaMind (deep-learning NLP & vision, 2016) plus other AI buys

   1. Integration Focus: Einstein AI layer embedded across Sales, Service, Marketing and Commerce Clouds

   2. Notable Results: Einstein now delivers >80 billion AI predictions a day and powers opportunity scoring, chatbots and image recognition inside core CRM workflows.

3. McDonald's acquisition of Dynamic Yield (personalisation, 2019) + Apprente (voice, 2019)

   1. Integration Focus: Real-time menu recommendations and automated drive-thru order-taking

   2. Notable Results: Deployed Dynamic Yield in >10,000 U.S. drive-thrus and lifted average order value. Voice pilots created McD Tech Labs to adapt NLP to QSR use-cases.

4. Mastercard acquisition of Brighterion (fraud AI, 2017)

   1. Integration Focus: Smart-agent ML models scoring every card transaction in real time

   2. Notable Results: Combined network data and Brighterion’s technology cut false positives and improved fraud detection accuracy across 150 billion annual transactions.

5. Microsoft acquisition of Nuance (speech & clinical AI, closed 2022)

   1. Integration Focus: Ambient clinical intelligence (DAX) integrated into Teams and Azure Health

   2. Notable Results: DAX auto-generates EHR notes during telehealth visits, attacking clinician burnout while expanding Microsoft’s healthcare cloud TAM by ~$500 billion.

3. Common Challenges Encountered

1. Data labyrinths – AI models starve without unified, high-quality data. Each case above required massive data plumbing before models could generalise across the acquirer’s global footprint. McDonald’s had to align menu, traffic and weather feeds in near-real time while Nike merged RFID store signals with Celect demand graphs.

2. Culture and talent retention – Startup innovators resist slow release cycles; legacy teams distrust “black-box” outputs. Salesforce created a dedicated Einstein Research group of 175 data scientists reporting to MetaMind’s founder to safeguard velocity.

3. Ethics, privacy and bias – PCI, HIPAA and regional AI regulations force new governance boards (e.g., Microsoft’s Responsible AI committee overseeing Nuance data flows).

4. Measuring ROI beyond a pilot – Early pilots often raise expectations faster than revenue. For example, while the introduction of Dynamic Yield had a positive revenue impact for McDonald's initially, franchisees complained that Dynamic Yield’s uplift was uneven, prompting McDonald’s to sell a majority stake to Mastercard after three years.

5. Change management in the field – Store managers, call-center agents or treasury analysts must trust the algorithm before they act on it. Nike rolled out small “inventory wins” to frontline merchandisers first, then scaled enterprise-wide.

4. Tips for a Successful Integration

Start with user journeys, not feature parity. A purely “gap analysis” misses the emotional and workflow frictions that sabotage adoption. In the GTreasury & CashAnalytics merger, a user-centred discovery uncovered hidden pain points and produced a roadmap the combined teams could rally around.

Ship thin slices that prove value quickly. McDonald’s chose drive-thru suggestive selling—visible ROI within weeks—before tackling kitchen forecasting. Prioritise use-cases where data quality is highest and latency constraints are manageable.

Synchronise data infrastructure early. Create shared schemas, streaming pipelines and unified feature stores before model training. Nike’s RFID rollout ran in lock-step with Celect’s demand algorithms to avoid “garbage-in” forecasts.

Pair cross-functional squads. Blend legacy domain experts with acquired data scientists in one backlog, one OKR set, one release cadence. Salesforce embedded Einstein APIs directly inside the Lightning platform, so product managers and ML engineers shipped together.

Retain and empower AI talent. Keep founders visible (e.g., MetaMind’s Richard Socher became Salesforce Chief Scientist) and give teams autonomy over model evolution, tooling and open-source contributions.

Build ethical guardrails. Stand up model governance, bias testing and incident response processes that match the acquirer’s brand trust level—especially in finance and healthcare. Mastercard credits its “Smart Agent” explainability for regulator confidence.

5. A Decision Checklist for Leaders

1. Strategic fit: Will the AI capability deepen the core value proposition or distract from it?

2. Buy vs. build vs. partner: Assess speed-to-market advantage against integration debt.

3. Integration scope: Embed models invisibly (Salesforce), keep a standalone product line (Mastercard), or spin off once maturity is reached (McDonald’s sale to Mastercard).

4. Data readiness: Map required data assets, ownership, privacy constraints and cleansing effort.

5. Talent plan: Define roles, career paths and incentive structures for acquired staff.

6. Success metrics: Mix leading indicators (model precision, adoption rates) with lagging business KPIs (revenue lift, cost reduction).

7. Governance & ethics: Establish oversight boards, auditing, and red-team testing for bias and security.
   

   

6. Bottom Line

AI acquisitions succeed when legacy giants respect the startup’s speed and wrap that speed in the acquirer’s scale, data, and distribution. Put real users at the center, wire the data first, and guard culture as carefully as code. The companies above show that when those pieces click, a century-old burger chain, a shoemaker, a payments network, and an enterprise cloud titan can successfully integrate AI into their product ecosystem.

Dina Levitan is Principal Product Managment Consultant at Product Science Group and Founder at Chill Labs. Check out Chill Labs at chillaborate.com

AI remains a powerful differentiator—for now. What we're witnessing is a race, with every product leader scrambling to figure out how to harness AI's potential before their competitors do.

I wrote previously about how product leaders are using AI in their day-to-day work. Now I’m going to write about how product leaders are adapting their craft when it comes to building AI-native products.

The stories that follow come from the trenches—from product managers and leaders who are figuring out in real-time how to build meaningful AI products.

The Strategic Decision: When and Where to Build with AI

As product leaders, we have to start with a clear understanding of the customer need combined with clarity on how AI can address it better than other solutions.

Kristy Hissa, General Manager at Kairos, an AI startup focused on improving meeting outcomes, shares, "The biggest mistake I see is integrating AI features without a strategy. AI isn't a shortcut to identifying business problems, validating their impact, and then solving them." Hissa offers a pragmatic framework for evaluation: "Cost + Time + Talent." Before diving into AI development, product leaders must honestly assess whether they have the financial resources, timeline, and team capabilities to execute competitively.

Matt Luppino's journey at Formation Bio illustrates the importance of using product skills to figure out the right product opportunity to go after. Rather than chasing the crowded field of AI-powered drug discovery, Formation Bio focused specifically on a category of drug development bottlenecks - running clinical trials. This strategic focus has yielded remarkable results: Formation Bio can now run clinical trials "in about 30% of the time with 50% of the costs of a standard trial." They’ve identified a specific, high-value problem where AI could make a meaningful difference.

Defining and Measuring AI Success

One of the most perplexing challenges in AI product development is determining what ‘good’ looks like. AI outputs exist on a spectrum of quality that can be frustratingly subjective. The non-deterministic nature of AI means that measuring 'good' becomes more complex—you can't simply test if a feature works as designed when the output varies each time you run it.

A technique that is quickly rising as a top skill for AI product managers is crafting evals, or evaluations. As Aman Khan says in Lenny’s Newsletter,

“Evaluating AI systems is less like traditional software testing and more like giving someone a driving test:

• Awareness: Can it correctly interpret signals and react appropriately to changing conditions?

• Decision-making: Does it reliably make the correct choices, even in unpredictable situations?

• Safety: Can it consistently follow directions and arrive safely at the intended destination, without going off the rails?”

Another skill that remains critical for product managers in AI as it is in other domains is product discovery and design research. Is what you think good looks like also what your customers think good looks like?

Mijeong Kim, Product Manager at Kairos, confronted this challenge head-on: "One big challenge is that it's hard to measure the quality of AI-generated answers. As a product manager, I need to define what 'good' looks like and find a way to measure the value AI is creating."

To answer this, Kim conducted blind surveys comparing AI-generated meeting agendas to human-generated ones. The result? Users rated the AI agendas as more outcome-focused and helpful. This kind of discovery research on the AI output is a valuable way of keeping humans in the loop.

Kim's advice crystallizes an essential principle for AI product management: "Start by knowing what you're actually selling to the user. Sometimes it's a measurable outcome, like saving time or reducing costs. Other times, it's a psychological value, like feeling more confident or prepared. Either way, define the user's desired outcome first. Then measure whether your AI feature is helping them achieve it, without getting too caught up in the AI part itself."

Formation Bio's approach offers another lens on measuring AI success. Luppino's team tracks concrete business outcomes: time and cost reductions in clinical trials.

In the end, measuring product success for AI products has a lot in common with measuring product success for any kind of product. Determine how to measure if you are delivering on your core value proposition, regardless of how much you use AI to get there.

Building AI Products Outside of Big Tech

The conventional wisdom suggests that competing with Big Tech in AI is a fool's errand. How can smaller companies possibly match the resources and reach of Google or Microsoft?

Kristy Hissa offers an optimistic perspective: "FAANG is so busy with world domination, that if you do one thing really, really well and you treat your customers right, you have a good chance of being successful."

Hissa's experience working closely with FAANG companies reveals another advantage: "We work very closely with one of the FAANG companies and there are use cases where they have told their customers 'No', while making an introduction to Kairos as a vertical, specialized vendor." The giants can't be everywhere at once, creating opportunities for specialized solutions that serve specific market needs better than generic platforms. Thus, we’re seeing a rise of Micro SaaS products that may be too small to be funded as VC-backed startups and will compete based on how close of a relationship the product develops with its user base.

Managing the Human Side of AI Products

Kristy Hissa points to another human-centered challenge: managing user expectations based on their familiarity with AI. "The broader community of users who have started adopting AI is generally familiar with what to expect, but you might need to invest more in customer relations for setting the right expectations for the late majority that is coming to the AI party."

As AI becomes more mainstream, product leaders must navigate varying levels of user sophistication. Early adopters might push for cutting-edge features, while mainstream users need hand-holding and clear value propositions.

The human side also extends to team dynamics and organizational readiness. Building AI products requires new skills, new workflows, and often new mindsets. Product managers often find themselves at the forefront of learning how to use AI in our work as well as learning about what AI can do for our products.

The Product Leader's AI Playbook

The experiences of these product leaders coalesce into practical lessons for anyone embarking on the AI product journey:

Start with the problem, not the technology. Kristy Hissa's warning about strategy-less AI implementation serves as a north star. The most successful AI products emerge from deep understanding of customer problems combined with nuanced understanding of how to build on new technological capabilities.

Define success in user terms. Mijeong Kim's approach to measuring AI quality through user outcomes rather than technical metrics provides a template for meaningful evaluation. Your AI is only as good as the real problems it solves for users.

Embrace the ecosystem. Modern cloud infrastructure and AI services level the playing field considerably. Learn what’s available and what it can do for your products. You don't need to build everything from scratch—focus your resources on what makes your solution unique.

Keep humans in the loop. Whether it's user research, evaluation of results, communication, or team skills, the human elements of AI product development deserve as much attention as the technical ones.

As we navigate this AI transformation, Kristy Hissa's enduring principle resonates: "Truly understanding your ideal customer and creating business value by solving their most impactful problems... doesn't change." The tools may be revolutionary, but the fundamentals of great product management remain constant.

For product leaders standing at the crossroads of AI adoption, the message is clear: start small, think strategically, and never lose sight of the customer problems you're solving. The AI transformation isn't just about keeping up with technology—it's about thoughtfully applying that technology to create meaningful value in the world.

Here's some feedback we heard from participants:

• "POA was an interesting concept that I haven’t heard about and am excited to take it back to my cross-functional team."

• "I'm inspired by the stories of real impact made by research."

• “What I found particularly helpful was the part that talks about opportunity assessments, this is something that teams rarely have visibility over and talking about it gives us the opportunity to take a more proactive position about allocation of resources.”

So what is the UMPF framework for Product Opportunity Assessments?

UMPF stands for Users, Market, Product, and Feasibility—four interconnected lenses that create a comprehensive view of any product opportunity. But the sequence matters as much as the components.

First Things First: Start With Users

We always begin with users, and there's a critical reason why. When approaching opportunities from a business problem perspective, it's dangerously easy to convince ourselves that users want something they'll never actually use.

We want to understand their pains and their desired user outcomes. We want to understand the depth and frequency of their pains to help us start to estimate how likely they are to try a solution.

Market: From Individual Pain to Scalable Opportunity

Once we understand individual user experiences, we expand our view to the market. How many others share this level of pain? What factors differentiate those who experience it from those who don't?

We want to quantify opportunities using market data. This can be product data that we own, it can be data we gather, or insights from market analysts.

It also goes beyond numbers; it includes mapping the competitive landscape from the customer's perspective. What alternatives are users aware of? What are they actively considering?

This dual view—market size and competitive context—provides the foundation for realistic opportunity sizing.

Product Vision: Bridging Problem and Solution

Once we have that picture, we want to think about the product vision. What is its value proposition, and what are the broad strokes of solution ideas? How can we understand these options enough to understand how doable they might be?

Feasibility: The Reality Check

Finally, we want to get a rough idea of feasibility. Do we have the right skills on our teams? Do we need to buy, build, or partner? The idea is to get a sense of the order of magnitude that is enough to be able to compare and contrast with other product opportunities.

From Assessment to Action

Pulling all of that together, we can present enough info to be able to make recommendations about whether to invest, pivot, persevere, or sunset. At the portfolio level, we can also start to compare and contrast opportunities so that we can decide how much to invest in each bet.

Interested in learning more? We love bringing this half-day workshop to companies and organizations so that we can help teams work on exactly what they care about while teaching them new tools and techniques.

Learn more here

Rather than AI being the end-all-be-all answer, Mike hopes that AI will be used to integrate both qualitative insights from empathetic customer interactions and quantitative data. This evidence-based approach involves testing strategies through small experiments, learning from the outcomes, and aligning decisions with core values and customer outcomes for incremental growth.

Mike also reveals insights from his experience working with Bob Moesta on jobs-to-be-done interviews, highlighting the value of going deep in customer research to uncover valuable insights, and in his case, understand attendees’ needs and improve the conference and/or community experience.

Takeaways include asking deeper questions, spending more time gathering that qualitative feedback so that you can go deeper, being curiously observant of jobs-to-be-done, and finding various modalities in which to gather continuous feedback from the “friendlies” and those who are more hesitant to capture a complete picture.

Resources

Follow Mike Belsito on LinkedIn

Explore the free resources and upcoming events at mindtheproduct.com

Listen to Mike on Rocketship.fm

Follow Holly on LinkedIn

Visit the Product Science Group website

Explore Product Science Workshops and Courses

Quotes from Mike Belsito

(00:06:52) "The job that people hire your product for, you know, if you want to think of it, in jobs to be done context, it may not be what you think it is." - Mike Belsito

(00:21:29) "Qualitative data is data. So being data driven doesn't mean that we're relying only on the reports and the analytics. It's not an either or. It's not quantitative versus qualitative. It's using them both together." - Mike Belsito

(00:28:15) "It's not just what you've learned, but it's these relationships that are a real value to you." - Mike Belsito

Lab Notes

Lab Note 607.1: Depth is where you find Differentiation.

(00:32:54) “...when you do any type of research, finding out what words mean and getting deeper is, I think, very key to finding that differentiation. So something I like to say is verbs are questions and adjectives are questions, right? Whenever someone says, you know, I'm going to I need to be online, well, what does that mean?” - Adam Thomas

Lab Note 607.2: Start with observation of the jobs-to-be-done.

(00:33:59) “Folks have an ideal way of doing their work, and then they have an actual way. One boondoggle. One place where you'll get trapped with jobs to be done is to start thinking about the ideal way that folks like to do jobs instead of thinking about what is actually happening.” - Adam Thomas

Lab Note 607.3: Use different modalities to get feedback from a range of people.

(00:38:11) “... in the context of a product getting continuous feedback, Is it a user interview? Is it a survey? And the importance of reaching people beyond just those friendlies, the ones who are eager to share their feedback and to go beyond the power users ...finding a way to reach them and hear their voices and include their thoughts in the product development process.” - Dina Levitan

In my experience, while some of the tasks we try to use AI for are still missing the mark, there are many areas where AI is genuinely helpful and productive.

And things are only going to go faster from here.

In this issue, I’m sharing some favorite articles as well as recapping our recent posts about what product leaders need to know today.

What resources are you finding most helpful in your own AI adoption journey?

Thoughtfully,
Holly

Product Science Group’s Picks

A Week in My Life as a Product Leader with AI
By Peter Yang
Peter analyzed every time he used AI in a week and shared 17 use cases where it really shined. This is an interesting and tactical guide to using AI in your product job.

How to become a supermanager with AI
By Hilary Gridley
Hilary shares 5 major areas where she’s using AI to upskill her team and give her more time to focus on what matters - the human side of product leadership.

Our Product Leadership in 2025 Article Series

The Narrowing Path: Navigating Product Leadership in 2025
I lay out 6 challenges facing product leaders today, including AI adoption, the FAANG preference paradox, flattening orgs, and more.

Vibe Coding for Product Leaders: What I Learned Using AI to Rebuild the Product Science Group Website
Coding with rapid prototyping tools like Replit, Bolt, and Lovable is making building more accessible - but how far can it get you, today?

The AI Transformation Imperative, Part 1: Using AI to Enhance Your Product Leadership
Tactical tips for using AI to improve your product leadership practices, from writing and thinking strategically to performing top-notch research to communicating and empowering your teams.

Upcoming Events

Move Fast and Build Things: Product Management in the No-Code Era

Today, I’m participating in a Product Coffee event hosted by Jason Brett. I’m joining a candid roundtable discussion and demos of PM created technology and prototypes of solutions to real world problems, using low and no code platforms.

Join us today at 11:30 EST

Dina Levitan and I will be at UXDX 2025 in NYC in May

This May 12-14, Dina and I are presenting two critical sessions for product leaders navigating today’s rapidly evolving landscape!

📊 Product Opportunity Workshop: From Uncertainty to Strategic Confidence

Master evidence-based opportunity assessment to transform complex product portfolios, make confident investment decisions, and gain stakeholder trust through data-driven strategies that maximize value in rapidly evolving markets.

🤖 Product Leadership in the Age of AI Forum

An interactive forum exploring how AI is fundamentally redefining product leadership, where you’ll exchange strategies for integrating AI capabilities, building specialized teams, and evolving portfolio management approaches to maintain competitive advantage.

If you’re wrestling with portfolio decisions or AI integration challenges, these sessions are designed for you. The product landscape is changing faster than ever - let’s navigate it together in NYC!

Get your tickets to UXDX

Brought to you by Product Science Group

Our team at Product Science Group partners with startup founders and product leaders to share The Product Science Principles and the accompanying framework, the Product Science Method, which I've developed over my time in tech startups. Through our coaching and consulting work, we help our clients determine which product growth opportunities they should pursue and build the product management skills to deliver on their goals. Reach out if you'd like to explore working together as a client or team member!

Thank you for reading!

Got a question for me or want to share something you're excited about? Just hit reply. I read every e-mail.

The AI transformation falls into two distinct dimensions:

1. Using AI to enhance your existing product practice: Tools and approaches that make you more effective regardless of whether you're building AI-powered products.

2. Building AI-native products and features: Strategies and skills for creating products where AI is central to the value proposition.

In this article, we will focus on the first dimension, which is relevant to every product leader: how to enhance your existing product practice with AI using the Product Science Principles. Our next article will address the second dimension of building AI-native products.

Note on Data Privacy and Enterprise AI Usage

Before diving into the specifics, I want to address an important consideration: Always check with your employer regarding their policies around usage of generative AI, LLMs, and other AI tools. One of the many reasons I use Claude.ai is that their default policy is not to train their models on information users have provided.

Many large enterprise organizations have developed acceptable use policies for AI and set up access for their employees to tools like Microsoft's Copilot. If you work at an organization that has not given employees permission to use AI, consider using this article to help you get a sense of how to make the case for why your organization will fall behind if you don't adopt some form of allowed AI usage.

Using AI to Implement the Product Science Principles

The Product Science Principles consist of three core principles that I've observed in every successful growth organization I've worked with:

1. Evidence-Based Product Strategy: Using data and insights to drive product decisions

2. Continuous Discovery and Delivery: Maintaining tight feedback loops with users

3. Empowered Teams: Creating an environment where teams can do their best work

Let's explore how AI can help you implement each of these principles more effectively.

AI for Evidence-Based Product Strategy

Evidence-based product strategy requires gathering and synthesizing information from multiple sources - market data, user research, competitive analysis, and business metrics. AI excels at helping product leaders process and extract insights from this information overload.

AI as Thought Partner and Writing Coach

As product leaders, we spend significant time developing strategies, communicating with stakeholders and teams, and talking to users and customers. The key to successful AI use in strategy development is using AI as a true collaborator rather than a replacement for our thinking.

My approach to working with generative AI as a thought partner goes like this:

1. Provide contextual foundation: I always begin by sharing existing materials - previous writing, transcripts from interviews, data sets, or other relevant context. This ensures the AI understands some of the depth and nuance of the subject matter.

2. Define clear communication goals: I explicitly state what kind of artifact we're creating and who the target audience is. For example, "We're creating a summary of recent customer insights for key stakeholders" or "We're drafting a product strategy document for executive leadership."

3. Use AI to deepen my thinking: Instead of jumping straight to drafting once the AI has the context ingested, I have the AI ask me probing questions about my ideas. This forces me to articulate my thoughts more clearly and often reveals gaps in my communication.

4. Collaborative drafting: Once I've thoroughly explored the concept, the AI creates an initial draft based on our dialogue. This draft incorporates my insights while providing structure and clarity. This is one of my favorite steps because it quickly gives me a strawman to respond to and improve upon.

5. Fact-checking: I review the draft critically, suggest edits, and have the AI implement changes. I look closely for any assertions or quotes that might not be backed up by the context I gave it. Several times, I've asked the AI where it got a statistic and received its apology for making it up.

6. Human feedback: Finally, I move the draft to collaborative tools like Google Docs where colleagues can provide additional feedback. I ask the readers to approach it the way I guide research participants - tell me what they are thinking, when they are confused, when they have questions, and when they see something insightful.

This approach has dramatically increased not only my productivity and the quality of my writing but also the depth of my thinking. The AI doesn't replace my expertise and insights - it amplifies them by serving as an intellectual sparring partner that challenges assumptions and helps clarify complex ideas.

Beyond documents, this collaborative approach works equally well for developing meeting agendas, creating frameworks for difficult conversations, or creating easily digested summaries for messages or social media.

AI for Continuous Discovery and Delivery

Continuous discovery requires regular interaction with users and quick iteration on ideas. AI tools are transforming how product leaders conduct discovery by making it faster and more accessible.

Developing and Refining Research Plans

Before diving into prototyping or user interviews, we need a solid research plan. This critical first step in discovery can benefit tremendously from the same collaborative approach with AI that we use for strategy development.

The process I guide my teams to follow for research planning typically involves:

1. Generating a list of knowledge gaps: What do I wish I knew about our users, their problems, or our potential solutions? This is where AI can help expand our thinking by suggesting additional angles we might not have considered.

2. Crafting a research plan: Based on those knowledge gaps, I first create a draft research plan outlining the methods and approaches I think will best help us get answers. Then I share this draft with AI, asking it to identify potential gaps, blind spots, or improvements. This review process often reveals assumptions I didn't realize I was making or alternative approaches I hadn't considered.

3. Developing research scripts and protocols: For qualitative research, creating effective discussion guides that avoid leading questions and prompt rich insights. AI can help refine question phrasing and suggest probes for deeper exploration.

By feeding existing data and contexts into generative AI, we can ask it to identify potential blind spots in our research approach. This collaborative approach doesn't replace the human judgment needed to design effective research, but it can help ensure our research plans are more comprehensive and less biased by our existing perspectives.

Supercharging Prototype Generation and Refinement

One element of product discovery that is changing quickly is the time and resources required to create interactive prototypes. AI is making it easier to bring ideas to life in ways that weren't as easily accessible before.

In my work, I've recently rebuilt my own business website using AI-assisted development through Replit. What amazed me was the speed at which I could go from concept to functioning prototype. This "vibe coding" approach - using conversational AI to generate code - dramatically accelerated the development process.

I've also seen this approach work effectively in educational settings. My students at NYU Stern use tools like Replit to create functional prototypes without needing deep technical expertise. This has transformed their ability to test product concepts with users, allowing them to see the gaps in their designs and rapidly gather more meaningful feedback.

Research Synthesis and Analysis

When it comes to qualitative research, AI tools can help with analyzing large amounts of feedback or sentiment and providing a thought partner to traditional research synthesis. When analyzing privately captured feedback or publicly shared sentiments, AI tools can help identify patterns and insights across large volumes of data without the time commitment previously required.

When we're doing product discovery to validate customer problems and solution ideas, especially in 0-1 settings, my team still finds that the act of analysis helps us deepen our understanding of the problem, our empathy with the user, and our ability to talk confidently about our findings. But just as in writing, using AI as a thought partner in the analysis can help us make sure we see as many angles as possible.

AI for Empowered Teams

Empowered teams need the space and support to do their best work. AI can help product leaders create this environment by handling routine tasks and providing infrastructure that enables teams to focus on high-value activities.

Automating Repetitive Tasks

Many product leaders are automating repetitive tasks with AI to free up more time for supporting their teams. In my own practice, I've used tools like Zapier to automate routine tasks such as:

• Generating summaries of external meetings that I can quickly review and share with my team on Slack

• Creating structured, centralized reports from disparate data sources without any coding

• Managing coordination across multiple calendars that need to respond to each other

These automations free up valuable time for authentic customer and team interaction - the aspects of product leadership that remain uniquely human.

Beyond simple automations, AI agents can now handle more complex workflows. For example, they can:

• Monitor key metrics and alert you to significant changes

• Do multi-step processes that require some analysis and decision-making

• Prepare preliminary data analyses for product reviews

I've tried playing with n8n and zapier agents to automate more complex tasks than I can do with zapier zaps alone. So far, I've found the setup to be more challenging than I'd hoped, but I'm going to keep working at it, because I believe there is huge value here.

Supporting Your Team to Adopt AI

While you might be excited about AI tools, successfully integrating them across your product team requires intentional support. Based on my experience, teams need four key elements to successfully adopt AI tools:

1. Clarity on policies and information security: Create clear guidelines on what types of information can and cannot be shared with AI tools. Document which AI tools are approved for use within your organization and establish protocols for reviewing AI-generated content before external sharing. This creates a safe framework within which your team can experiment without concerns about data security.

2. Access to purpose-built tools: Consider investing in premium versions of AI tools where it makes sense—the productivity gains often justify the cost. Look beyond general-purpose AI tools to identify domain-specific applications relevant to your product area. Some organizations are developing internal tools that connect company data with AI capabilities in a secure way, providing the benefits of AI while maintaining control over sensitive information.

3. Time for experimentation and learning: One of the biggest barriers to AI adoption is simply the time pressure teams face. Build dedicated time into work schedules for team members to explore and test AI tools. Acknowledge that there's a learning curve, and the first attempts may not yield immediate value. Creating space for experimentation signals that you value this exploration as an investment in future capabilities.

4. Supportive learning culture: Organize regular "show and tells" where team members can demonstrate effective AI use cases they've discovered. Create dedicated channels or employee groups focused on AI tool experimentation—think of it like a book club, but for testing AIs. Consider providing access to formal training opportunities; I've heard good things about AI product management courses on Maven.

Remember that your role as a product leader includes being a capability builder for your team. By thoughtfully supporting their AI adoption journey, you're helping future-proof your organization while making their day-to-day work more engaging and impactful.

Getting Started: Building Your AI Tool Stack

If you're just beginning to explore how AI can enhance your product practice, I recommend starting not with tools but with outcomes:

1. Identify what you wish you were getting done:

   • Look at your to-do list for items that consistently don't get completed

   • Consider routine tasks that you find yourself delaying or avoiding

   • For me, this was sending Slack updates to my team about business development calls

2. Identify available inputs and integrations:

   • What information sources do you already have that could feed an automation?

   • Check what APIs or integrations (like Zapier) can access this information

   • Consider what connections between tools would create the most value

3. Determine your desired level of oversight:

   • I prefer to keep a human in the loop for most automations

   • For my Slack updates, I built a Zapier flow that sends me a draft message first

   • I can review and edit before it goes to the team, ensuring quality and accuracy

4. Start small and iterate:

   • Build the first version of your automation and test it out

   • Use it consistently for a week or two before evaluating

   • Keep an "automation backlog" of ideas, just like you would with product features

5. Experiment with different tools:

   • Try sending the same prompt to different LLMs to compare responses

   • You may develop a preference for a particular AI that seems to understand your needs better

   • Consider the tradeoffs between specialized tools and general-purpose ones

The key is finding the sweet spot between what's impactful to automate and what's accessible for you to implement without excessive time investment.

Conclusion: The Human-AI Partnership

As product leaders, we're experiencing a fundamental shift in how we work. AI isn't replacing our strategic thinking or customer empathy - it's amplifying these uniquely human capabilities by handling routine tasks and expanding our creative capacity.

The most successful product leaders will be those who develop an effective human-AI partnership, leveraging technology to enhance their existing practice while maintaining focus on the core elements of great product leadership: understanding customer problems, envisioning compelling solutions, and aligning teams to deliver value.

In our next article, we'll explore the second dimension of the AI transformation imperative: building AI-native products and features. We'll dive into the strategic and organizational considerations required to successfully develop products where AI is central to the value proposition.

I’ve got a couple of spots available to work directly with product leaders in my coaching program. If you find my content helpful, let’s hop on a call and see if my coaching is right for you.

Learn More About Coaching

Read the Show Notes on our Website

Resources

Visit Buy Build Fund where Andy partners with investors and business owners

Explore Prints of Love, where he serves as Board Member & CEO

Connect with Andrew Breen on LinkedIn

Follow Andy Breen on X

Follow Holly on LinkedIn

Visit the Product Science Group website

Explore Product Science Workshops and Courses

Quotes from Andy Breen:

(00:05:57) "It's not about the technology. Just because you build it, they will not come. And you really do need to be solving problems for people and offering them something that changes them and their life and their abilities." - Andy Breen

(00:08:57) "...these days especially every product gets about 30 seconds of consideration, you know, at the top. And so you have to have a clear value proposition." - Andy Breen

(00:26:19) "There are a lot of stay-at-home moms or like people like engineers doing little SAS side projects and stuff that were turning into real businesses, real revenue, real profits, you know, something that's not often talked about in the venture world." - Andy Breen

Lab Notes

Lab Note 606.1: Too many product managers underestimate the importance of assessing value risk.

(00:32:49) “I always come back to value risk. That's, you know, that's the one that people forget, ignore, or just under undervalue.” - Dina Levitan

Lab Note 606.2: Conducting value research often means holding uncomfortable tensions.

(00:33:44) “...We'll have an initial hypothesis and try and get one really early on using the expertise and the knowledge that we have in house [...] while sort of in parallel developing this emerging hypothesis through the discovery work [...] I experienced this qualitatively as a feeling of, like, holding this tension of the unknown that is kind of extending throughout the whole project.” - Mark Enache

Lab Note 606.3: When qualitative and quantitative are telling you the same thing, you’re on your way to product market fit.

(00:35:54) “...the actual product market fit requires that you then design, build, and bring to market the product. This is just the first step. It's almost got a pull of its own that you're like, wow, now everything seems really clear, and it almost seems like it was obvious the whole time, but it wasn't.” - Holly Hester-Reilly

Lab Note 606.4: Continuous Product Discovery has been useful since the 90’s, even if it looks different today.

(00:36:37) “That really spoke to me with the continuous discovery and delivery product science principle. And, you know, it says something about the practitioners in the field, you know, it comes down to that. How fast can you learn?” - Dina Levitan

Lab Note 606.5: Deciding which customer use cases to replace with AI requires evidence-based product strategy.

(00:37:11) “His story around call centers and trying to understand what parts of the activities that go through the call center should and should not be moved to automation and digital experiences was a really a really masterful evidence-based product strategy of coming back and saying, hey, the evidence is showing us that we shouldn't even be trying to remove the human side of the lost card situation. That is a piece of product strategy.” - Holly Hester-Reilly

Learn More

I had seen some demos of tools like Replit, Lovable, and v0. Since I’d long felt constrained by the website management tool (Webflow) that I was using for my business, I decided to experiment with what I later learned is called "vibe coding"—using conversational AI to create code.

My business website seemed the perfect test case: substantial enough to be meaningful, but without the complexity of a full SaaS product.

What started as an experiment would evolve into a complete replacement of my Webflow site. Working with AI to build my website was exhilarating. Having a "personal developer" available 24/7 delivered the most significant productivity boost I've experienced since discovering Zapier years ago.

After about 30 hours of working with Replit, I launched my redesigned and redeveloped website, achieving two of my key business objectives: increasing engagement metrics (92% increase to average visit length, 1200% increase in visits to the Services page) and newsletter signups.

However, vibe coding is not yet ready to replace developers for complex SaaS platforms or enterprise products.

My Initial Evaluation of Conversational AI Development Tools

Like many product leaders, I found myself evaluating various AI prototyping tools including Lovable, Bolt, Cursor, V0, and Replit. I played around with each, watched YouTube tutorials, and assessed which would best meet my needs. The decisive factor was deployment capability, which Replit offered out of the box.

Now that I’ve spent around 100 hours with Replit over the past several months, I can confidently share that it is, as of April 2025, the most appropriate for prototyping, testing, and creating relatively simple "brochure-ware" sites like business websites.

However, I caution that while I'm delighted with my results, these tools aren't yet truly "no-code" solutions accessible to everyone. Some technical understanding remains necessary, particularly for troubleshooting.

The Vibe Coding Process I Used To Go From Content to Code

To get started, I began with Claude.ai, my favorite generative AI tool. I created a project, told it the goal of the work, and uploaded materials from 8 years of running Product Science Group: case studies, client proposals, writing, and existing website content.

I worked with Claude to develop the copy and structure for my landing page. Claude suggested components including a hero section, testimonials, and a services list. While the initial design was functional but plain, it provided a solid foundation.

I then brought this content to Replit, asking it to create a landing page. The first attempt was decent but needed refinement, so I adopted an iterative, component-by-component approach:

1. I'd focus on one section (like testimonials) and request specific improvements: "Change the placement of the testimonial photo" or "Adjust the card style and fonts."

2. After perfecting each component of the landing page (logos carousel, services section, resources, events, podcast, newsletter), I moved on to building out the linked pages.

3. For each new page, I would work with Claude to write content, then implement it in Replit—with each page typically taking 1-2 hours end-to-end.

This process, while not instantaneous, was remarkably efficient compared to traditional development cycles—especially when working with external developers.

The most challenging aspect came when implementing a content management system for content such as my Product Science Podcast episodes. I needed to migrate episodes from both WordPress and Webflow. After downloading the content and cleaning it in a Google Sheet (with my assistant's help), I uploaded the CSV to Replit and requested a podcast viewing page with an admin backend.

The process revealed both the power and fragility of AI-generated code. Without a proper test suite, changes often broke existing functionality in unexpected ways. I encountered several issues:

• API calls failing after content or database changes

• Front-end components not updating when backend APIs changed

• New pages not being added to the router, resulting in broken links

Debugging required leveraging my product management background and basic technical knowledge. I would read through the AI's step-by-step explanations and code changes to identify issues, then provide specific guidance like "I think you forgot to do X" or "That's not what I wanted you to change."

This highlighted that while these tools are powerful, they still require a level of technical sophistication that isn't entry-level.

Product Management Principles Applied to AI Collaboration

The product management skills that proved most valuable in this process were:

Creating a Product Vision: Just as with human teams, I needed a clear vision for what the new website would accomplish. This guided all subsequent decisions and helped ensure coherence across the site.

Audience Segmentation: Understanding my target audience—product or transformation leaders in traditional enterprises—was crucial for both design decisions and content creation. When working with Claude to write copy, I needed to communicate my audience clearly so the content would resonate with them.

Breaking Work into Small Chunks: Perhaps the most directly transferable PM skill was breaking large tasks into smaller, manageable pieces. Just as product managers work with engineering teams to break epics into user stories, I broke my commands to the AI into specific, incremental steps.

For example, when adding the podcast section:

1. First, I requested the main page layout

2. Then I added an admin interface for content management

3. Finally, I refined specific elements (like changing an open text field to a dropdown to maintain data consistency)

This incremental approach avoided overwhelming the AI with complex instructions that might lead to incorrect assumptions or technical debt.

Evidence-Based Product Strategy: Following Product Science principles, I grounded my website redesign in evidence from customer interviews and market research. I analyzed past conversations with ideal clients to identify differentiators, pain points, and their natural language. I also used LinkedIn's ads interface to understand my audience demographics better and narrow my target profile.

Continuous Discovery and Delivery: Throughout development, I shared iterations with my internal team and set up feedback sessions with people in my network. This allowed me to gather insights and refine the site based on real user reactions.

Empowered Teams: This principle is about how we define success and share context with the teams building the software product. Even without a formal team, I applied this principal by sharing context with the AI and defining key performance indicators. I ensured proper data instrumentation to track newsletter sign-ups, meeting bookings, page engagement, and other critical metrics.

By applying these product management principles to AI collaboration, I created a more coherent, effective website and avoided many potential pitfalls of AI-assisted development.

Evaluation and Insights for Product Teams

Working with AI to build my website was remarkably fast and flexible compared to traditional development.

The quality improvement over my Webflow site exceeded expectations. I implemented features that had previously been beyond my capabilities, like conditional logic that displays YouTube embeds for video episodes and static images with Spotify links for audio-only podcast episodes. This kind of "if-this-then-that" functionality would have been challenging to implement in Webflow without custom code.

The project represented a significant time investment—between 20-40 hours from idea to initial launch—but one that delivered substantial value for the cost.

However, vibe coding is not yet ready to replace developers for complex SaaS platforms or enterprise products. The interconnectedness and complexity of such systems still require human developers. AI-generated code lacks the reliability and stability needed for production-grade software at that scale.

For product leaders, I recommend vibe coding for:

• Early-stage startup ideas

• Business model innovations that don't require cutting-edge technical solutions

• Prototypes for usability testing

• Quick mockups for value-based user testing

• Simple business websites and landing pages

I advise against using it for:

• Complex production software

• Systems requiring high reliability

• Applications with significant security concerns

• Projects without someone who can troubleshoot technical issues

While product leaders should explore these tools to understand their capabilities, it's important to recognize their current limitations. The most promising approach sees AI not as a replacement for human creativity but as a partner that enhances productivity.

We should be cautious about over-reliance on AI, as it's trained on averages and may produce generic results. True innovation still comes from human creativity, a deep understanding of unmet needs, and meaningful connections with customers. The exceptional—not the average—is what creates competitive advantage.

I’ve got a couple of spots available to work directly with product leaders in my coaching program. If you find my content helpful, let’s hop on a call and see if my coaching is right for you.

Learn More About Coaching

The landscape of product leadership has undergone a profound transformation in recent years. Job openings for product managers at tech companies have contracted dramatically, now standing at only 60% of their 2022 peak, according to data from TrueUp shared in Lenny's newsletter. This contraction has created a challenging environment where competition for each leadership role has intensified, and the bar for entry into the leadership ranks has risen significantly.

But that only tells part of the story, as it only includes tech companies. There are many product leaders inside traditional enterprises. Based on my own experiences working with traditional enterprises, I would argue this number is growing as more traditional companies wake up to the value of product management for the software that enables their businesses.

Recent LinkedIn data reveals approximately 400,000 product leaders at the Director level and above across English-speaking profiles in the Americas and Europe. Among these leaders, 33% work at companies with more than 10,000 employees, while only 22% operate within the technology sector. And 37% of these product leaders work at companies experiencing modest growth of just 3-10%, highlighting the pressure many face to deliver results in constrained environments.

6 Critical Challenges Facing Today's Product Leaders

The challenges product leaders face today aren't merely temporary obstacles but fundamental shifts in how product leadership functions in modern organizations. Here are six that I see time and again.

1. The AI & Product Leadership Gaps

When we talk about AI and product leadership, it typically falls into two big areas.

The one that is most accessible to all is learning to use AI to improve productivity and work quality. This includes:

• using generative AI as a thought partner and writing coach in plans and communications

• using AI in product discovery for prototyping, feedback analysis, and research synthesis tools

• using agentic AI tools to automate tedium and scale capacity to execute repetitive tasks

The one that is harder to gain opportunities in is developing winning strategies for AI products themselves and successfully executing on those strategies with AI product teams. If you don’t have access to this yet, you can prepare by building your portfolio of case studies and projects.

2. The Expanded Leadership Mandate

As organizations flatten, product leaders now manage more direct reports with fewer advancement opportunities to offer their teams. Meanwhile, learning and development budgets have contracted precisely when the need for upskilling in areas like AI has become most acute, creating significant talent development challenges.

3. The Business Acumen Imperative

As someone who cut their teeth in startups, I hate having to describe this as something new. The truth is, there was an era where many product leaders didn't have to show impact and results the way they do today.

Financial fluency has become non-negotiable for product leaders. Gone are the days when a compelling vision could stand alone without a robust business case. Today's product leaders must speak the language of CFOs—understanding margins, EBITDA, ROI, and other financial metrics that translate product outcomes into business impact.

4. The Overreliance on Subject Matter Expertise

With the product management boom a few years ago, more people without true product management experience have been in hiring positions for product managers and product leaders. One way this typically manifests is in a false belief that what is needed is a leader from within the same sector. In B2B SaaS, where I've spent the majority of my career, FinTechs exec want to hire FinTech experts to lead product, MarTechs want to hire MarTechs experts, and so on.

I believe this is misguided, as truly great product leaders can generally ramp up in a new domain more quickly than a domain expert who has not been on a product team can overcome natural biases that product leaders have spent a decade or more learning how to counter.

5. The FAANG Preference Paradox

With all the Big Tech layoffs in recent years, there is an abundance of talent on the market with FAANG experience, and the gap between those with and without FAANG experience has accelerated in recent years.

This neglects to account for the different skill sets for product leaders in places without massive customer bases and experimentation infrastructure. Product leaders at B2B SaaS startups and traditional enterprises typically face different core challenges than those in Big Tech, and I've seen many an ex-FAANG leader flounder in these environments.

6. The Enterprise Reality Gap

For the 20% of product leaders working in financial services and traditional enterprise environments, the gap between theoretical best practices and organizational reality is strong. Political navigation is as crucial as product expertise, while difficulty with access to customers and infrastructure for experimentation often make "textbook" product discovery extremely difficult.

A Preview of Our Product Leadership Series

In the coming weeks, we'll explore each of these challenges in depth, offering practical strategies for navigating the evolving product leadership landscape. Our series will include:

• The AI Transformation Imperative: How product leaders can develop AI strategy expertise and effectively collaborate with data scientists and engineers

• Doing More With More Reports: How successful product leaders are navigating org flattening while delivering the results they need to succeed

• Learning to Speak Finance: How product leaders have learned to speak the language of the C-Suite and build buy-in with their peers

• Showing You Understood the Assignment: How product leaders are showing they have the subject matter chops to get the job done

• Advancing Without FAANG Credentials: Strategic approaches to career development for product leaders without big tech pedigrees

• Enterprise Reality vs. Product Leadership Ideal: How successful product leaders bridge the gap between theory and practice in complex organizational environments

Each article will draw on real-world examples and provide actionable insights. We'll examine how successful product leaders are adapting their approaches to thrive despite resource constraints, organizational complexity, and intense competition.

Join us in the weeks ahead as we explore the strategies and approaches that are helping product leaders thrive in 2025 and beyond.

And in the meantime, I'd love to add your insights to the articles if you’re a product leader either in-seat or on the hunt (or both).

Book a time with me here

Product management and Site Reliability Engineering (SRE) might seem like separate disciplines, but both share a core objective: delivering reliable, scalable, and high-quality user experiences. By applying SRE principles to product management, teams can improve efficiency, enhance collaboration, and drive continuous innovation.

Product management and Site Reliability Engineering (SRE) might seem like separate disciplines, but both share a core objective: delivering reliable, scalable, and high-quality user experiences. By applying SRE principles to product management, teams can improve efficiency, enhance collaboration, and drive continuous innovation. PMs building products can benefit from the systems-thinking mindset and holistic approach inherent to Site Reliability Engineering. This blog post explores how SRE principles can be leveraged to create more resilient and user-focused products. Examples explored include:

1. Embracing risk

2. Automation & eliminating toil

3. Monitoring

4. Release management

5. Simplicity

6. Blameless postmortems

1. Embracing Risk: Experimentation as a Way to Learn

SRE acknowledges that risk is inevitable in building and maintaining complex systems. Instead of avoiding risk, SRE practices embrace it strategically by setting clear Service Level Objectives (SLOs), giving teams clarity around how much room they have to experiment. By leveraging SLOs, product teams can ensure a balance between innovation and reliability.

In product management, the same principle applies through Continuous Discovery and iterative development cycles. Rather than relying on assumptions, product teams should:

• Run small-scale experiments and A/B tests to validate ideas before full deployment.

• Embrace a "test and learn" culture, where failure is an opportunity for refinement rather than a setback, such as through the Built-Learned-Planning demo.

• Start small and grow iteratively - using a phased approach to roll out changes to ensure that launches meet reliability objectives and also provide customer value.

2. Eliminating Toil: “Automate Yourself Out of a Job”

Toil, defined as "repetitive, manual work that doesn’t contribute to long-term improvements", is a well-known challenge in both SRE and product management. Product managers often find themselves stuck in operational overhead, focusing on executing the next release or working on manual reporting or data reconciliation. While the classic SRE phrase is “Automate Yourself Out of a Job”, I like to think of it as “Automating Yourself Into a Better Job”.

SRE’s principle of automating toil can be applied in product management by:

• Automating routine product analytics reports, ensuring insights are always up-to-date and accessible.

• Implementing automated workflows for user feedback collection and prioritization.

• Using AI-driven product management tools to identify patterns in user behavior, reducing the need for manual analysis.

By automating key tasks, product managers can focus on strategy, vision, and long-term product improvements rather than operational inefficiencies.

3. Monitoring: Watch Your Metrics and KPIs Go Up and to the Right

SRE teams rely heavily on monitoring and observability to ensure system reliability. They track Service Level Indicators (SLIs) and use them to measure adherence to defined objectives. Similarly, product managers should establish and monitor Key Performance Indicators (KPIs) that align with business and user goals.

Best practices include:

• Setting clear product metrics - in an SRE context, this may be SLIs such as page load time, error rates, and uptime, while for Product, we have a different set of Metrics that Matter, such as the Pirate Metrics (AARRR).

• Using real-time dashboards to track customer behavior and engagement.

• Setting up alerts to proactively identify performance issues before users are too far affected.

Data-driven decision-making allows teams to react quickly to problems and continuously refine the user experience.

4. Release Engineering: Ensuring Smooth Deployments

One of the most critical areas where SRE and product management intersect is release engineering: ensuring that new features reach users in a stable and reliable manner.

Product managers can apply SRE’s structured approach to deployments by:

• Utilizing feature flags to roll out new functionality gradually, minimizing risk.

• Prioritizing canary releases to test new features with a small subset of users before a full rollout.

• Integrating automated testing into the CI/CD pipeline to catch issues early and prevent regressions.

A smooth release process ensures that innovation does not come at the cost of stability.

5. Simplicity: Continuous Discovery and Reducing Product Entropy

SRE prioritizes simplicity and reducing operational entropy, ensuring that systems remain manageable and scalable over time. The same principle applies to product management. Complexity should be minimized wherever possible to enhance user experience and maintainability. A recent client example where product entropy was reduced, leading to a 50% decrease in monthly bug creation rate YoY and 80% of backlog issues being closed 1 year later can be found at this case study.

Strategies for reducing product entropy include:

• Focusing on the user: removing unnecessary features that create cognitive overload.

• Maintaining a clear and concise product vision to prevent scope creep.

• Regularly simplifying workflows to improve usability and streamline interactions.

By continuously refining the product based on real-world data and user feedback, teams can ensure long-term success.

6. Blameless Postmortems: Learning from Failure

In SRE, blameless postmortems are used to analyze failures objectively and extract key learnings. This practice fosters a culture of accountability without fear, leading to continuous improvement.

Product teams can adopt this mindset by:

• Holding retrospective meetings after major product launches to identify lessons learned.

• Documenting decision-making processes to improve future strategy alignment.

• Encouraging a culture where failures are seen as opportunities to refine the product.

• Introducing the "pre-mortem": thinking about the risks and all the ways a product may fail before getting too deep.

When teams embrace failure as part of the learning process, they create a more resilient and adaptive product strategy.

Conclusion

Applying SRE principles to product management enhances product reliability, efficiency, and scalability. By embracing risk, eliminating toil and leveraging automation, monitoring KPIs, refining release processes, reducing entropy, and reflecting regularly, product managers can create better user experiences while ensuring long-term product health.

The intersection of SRE and product management is a powerful framework for building resilient, user-centric, and scalable products. By borrowing and adapting these principles, product leaders can drive continuous innovation while maintaining system reliability, ensuring that their products work not only today but are sustainable for the future.

References

• Becoming SRE (2024) Advice from Former SREs

• Blameless (2023). SRE Principles

• Google SRE Book (2016). Site Reliability Engineering

• Torres, Teresa. (2021). Continuous Discovery Habits

Read on productsciencegroup.com

Ha's journey underscores the importance of adaptability, continuous learning, and leveraging networks in navigating a product management career. She highlights the significance of being proactive, driving outcomes, and having the courage to ask for opportunities and support from leadership. Ha's experience showcases how excelling in one's function, seeking opportunities for growth and expansion, and being ready to pivot when necessary can lead to transformative career shifts, such as her transition from a VP of Product role to venture capital.

Continuous product discovery is proven critical through Ha’s story, even as companies scale, emphasizing the need to maintain customer contact and understanding throughout growth phases. Ha Nguyen's advice on driving impact, seeking expanded responsibilities, and being willing to walk away when opportunities for growth are limited resonates with the team at Product Science Group. By prioritizing personal growth, seeking supportive environments, and proactively shaping their career paths, product developers and leaders can make strategic decisions that propel their careers and products forward.

Read the Show Notes on our Website

Resource Links:

Follow Ha Nguyen on LinkedIn

Visit NextStepFwd, Ha’s Consulting website

Join the NextStep Substack

Follow Holly on LinkedIn

Visit the Product Science Group website

Explore Product Science Workshops and Courses

Quotes from Ha Nguyen:

(00:05:45) "I felt like I was ready to sort of increase my scope and my impact, albeit at a smaller company. However, I really wanted to have more influence over the direction of the company, not just the direction of our products." - Ha Nguyen

(00:09:39) "Be incredibly good at what you do. Startups give you the opportunity to have expanded scope, work for a great boss who's supportive of your career. Don't be afraid to ask. And then finally, if. If they're not going to support you and what you need, then you might need to make the decision to walk." - Ha Nguyen

(00:28:47) "When you build the network and people have seen that you have been putting the hours in to build what I call the labor of love projects, it, it's just easier because when you need to reach out to folks for anything they want to help you, they'll pick up the phone and they'll help you." - Ha Nguyen

Lab Notes:

Lab Note 605.1: As a company grows, you must intentionally create structure for continuous product discovery. (00:31:46)

“In a company that's in hypergrowth, one of the things that can happen is you can start losing contact with customers. Where you used to be talking to customers regularly, now you know your scope gets narrowed, other teams scope gets narrowed and it ends up being sort of handed to a single person or a single team as like they're the ones with the customer contact. And that can be a really difficult maturation for a company because you end up having all these handoffs and that decreases the actual understanding of the customer.” - Holly Hester-Reilly

Lab Note 605.2: Raise your hand to access new opportunities. (00:33:03)

“...I was working at a very early-stage startup, even though I was the head of product, I identified a lot of challenges that were going to prevent us from having a successful launch and growing our and scaling our company. And so I ended up getting involved with a lot of things like building business processes, setting up a customer service organization and doing a lot of things that are outside of the traditional scope of being a product leader.” - Digant Davé

Lab Note 605.3: There are varied perspectives on work-life balance and startup life. (00:34:07)

“Younger generations today are being more thoughtful about their work-life balance and what may and the meaning of the work that they're doing. And they're asking deeper questions about how do we make sure that if we're taking this time away from the rest of our life, that it's for a mission, it's for a reason.” - Holly Hester-Reilly

Lab Note 605.4: Fractional product work is a great fit for creative people who thrive in ambiguity, but it’s not accessible or right for everyone. (00:36:38)

“...for me, like it's much more about a feeling, I think of feeling sort of alive, that entrepreneurial spirit, that there's variation... That I might not know what to expect. Because that feeling of sort of risk or uncertainty for me is really tied to creativity...” - Mark Enache

Lab Note 605.5: Invest in your network throughout your career and it will pay dividends. (00:40:27)

“I think that it's important in a regular kind of corporate setting in life, but then also especially in the consulting world, in the independent world. Just the network is, is where business comes from in many cases. And I think that what she said resonated just being able to meet people and kind of know where people's strengths lie and who you can call on for different needs and how you can help them.” - Dina Levitan

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Stephanie Leue, Chief Product Officer at Doodle at the time of this recording, shares how she integrates customer feedback, collaboration with internal teams, and data-driven insights to inform strategic decisions, underscoring the value of communication and using various artifacts to convey the product strategy effectively across the organization.

By engaging in discovery while leveraging foundational work, teams can build trust and demonstrate progress to stakeholders. Stephanie’s experience as a seasoned product leader in B2B SaaS, along with her focus on leadership coaching and diversity and inclusion, underscores the critical role of experienced product leaders in making the craft of product management appear seamless.

Overall, her approach emphasizes the iterative nature of product strategy development, de-risking bets with data and discovery, and fostering collaboration and trust within teams to drive meaningful progress and innovation in product development.

Read the Show Notes on our Website

Resource Links

Visit Stephanie’s webpage

Follow Stephanie Leue’s Substack

Follow Stephanie Leue on LinkedIn

Visit the Doodle website

Follow Holly on LinkedIn

Visit the Product Science Group website

Explore Product Science Workshops and Courses

Quotes from Stephanie Leue:

"We just don't want to build one feature after another. We want to have kind of a bigger picture in mind and we want to achieve that bigger version of what we are today, right?"

“If you show them 1 minute 20 about the future of a product, they immediately have 20,000 things that will never work out. And they have 20,000 questions and like tons of ideas why things will take way longer than expected. But to be honest, that's exactly the discussions you want to have, right?”

“We needed to train ourselves to ask the right questions. We needed to get answers to these questions. We needed to understand the data we got. We needed to compose a picture out of that data. So that takes a while because that's like a team effort.” -

Lab Notes

Lab Note 604.1: Experienced product leaders make the craft of product management look easy

“...when you see, when you experience someone who's an expert doing their craft, they make it look easy, they make it look simple. And I think that that was true with the way she was describing her process.” - Dina Levitan

Lab Note 604.2: Strategy is a series of bets that can make even the CPO uncomfortable

“...great things happen in that space of discomfort.” - Mark Enache

Lab Note 604.3: Use different communication approaches for different audiences of your product strategy

“I thought that was a really great example of empowering teams based on, you know, their context to have the vision at the granularity that makes sense for them. And I thought it also, you know, really harkens back to one of the product science principles of having the evidence at every level.” - Dina Levitan

Lab Note 604.4: Find foundational alignment and begin executing while you are developing your bigger product strategy

“...you want to calm your executives with the knowledge that you are moving forward on something that you're building something worthwhile and you have to start with the yes, I will go and do the thing. And then you can start sneaking in that maybe you're uncovering some things that put to question some of what they've told you to do, but you already are building trust...” - Holly Hester-Reilly

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Why watch?

• Understand What Product Entropy Looks Like in the Wild: Learn to recognize the signs of increasing disorder and inefficiency in your product landscape through real-world examples, such as the evolution of Google Tasks.

• Discover the Impacts of Product Entropy: Understand how product entropy can negatively affect your engineering teams, go-to-market strategies, and ultimately, your customers. We explored how engineering teams can get bogged down in maintenance, GTM teams struggle to make sense of the ecosystem, and customer experience suffers.

• Learn How to Develop an Evidence-Based Customer Journey Map: We delved into why customer journey maps are a powerful tool for understanding user experiences, identifying pain points, and evaluating product strengths and weaknesses. You'll learn the key components of a journey map, and how to incorporate evidence from user research, including quotes and data.

• Explore Strategies to Reduce Complexity in Product Ecosystems: Discover practical approaches to combat product entropy, including maintaining, sunsetting, and hybrid strategies. We discussed how to decide which path is right for your business by considering personas, workflow phases, user satisfaction, revenue, bug rates, user numbers, tech stack consistency, and pain points.

• See Real Results: We shared examples of how our clients have successfully fought product entropy, including a 50% decrease in the monthly bug creation rate and an 80% reduction in the backlog.

Watch Now on YouTube

Ready to keep moving forward?

The Product Science Group specializes in helping teams like yours tackle the challenges of product portfolio complexity.

Don't let your product portfolio become a sprawling, confusing mess. Take the first step towards a more cohesive and efficient product strategy.

We offer services to help you:

• Conduct Product Portfolio Analysis: We'll work with you on discovery, mapping, strategy, and action planning to streamline your product ecosystem.

  Learn more

• Run a Product Opportunity Workshop: Get started with a half-day workshop to drive evidence-based investment decisions for your products.

  Learn more

We'd love to hear about the challenges you're facing. Reply and tell us how this impacts you. We read every e-mail.

Hubert Palan, the founder and CEO of Productboard, is a key figure in the evolution of product management tools, drawing from his extensive background as an engineer turned product manager. He recognized the pressing need for a dedicated platform that addresses the unique challenges faced by product managers, emphasizing customer-centricity and segmentation, a perspective shaped by his studies under Steve Blank at UC Berkeley.

Traditional tools like Jira and Asana, according to Palan, often fall short in catering to the nuanced needs of product management, prompting the creation of Productboard to fill this gap, which now serves over 6,000 clients including major players like Zoom and JPMorgan Chase. By leveraging visual communication tools and focusing on customer segmentation, Palan believes product managers can better understand and cater to their target audience, ultimately driving product success and organizational growth.

Read the Show Notes on our Website

Resource Links

Follow Hubert Palan on LinkedIn

Follow Hubert Palan on X (formerly Twitter)

Explore Productboard

Learn about Steve Blank’s Lean Startup philosophy

Follow Holly on LinkedIn

Visit the Product Science Group website

Explore Product Science Workshops

Quotes from Hubert Palan:

“Because at the end of the day you have to make a decision. Do I add another use case? Do I satisfy a new need, Do I expand the set of capabilities that my product has or do I double down on what the use case that I already have, but make them better or improve the usability? And that's every day.” Hubert Palan (18:06)

“Frequently Personas are more like a design user Personas as opposed to Persona representing the business market segments that product managers need to think about. Because at the end of the day it needs to be a viable business, not just a product that satisfies needs of a specific user.” - Hubert Palan (21:16)

“It's challenging and there's no shortcut, unfortunately. I mean, you have to do the workshops, you have to communicate frequently. You need to do the AMA sessions, you need to have the big 10-page document and then a summary of it. And it's constant communication, constant coordination.” - Hubert Palan (31:54)

Lab Notes

603.1: Roadmaps are a conversation, not a one-way document.

"As he was mentioning the bidirectional flow of information that the roadmap can serve both to provide inspiration for people to get ideas and further the conversation, but also to incorporate their ideas into a living document.” - Dina Levitan (37:35)

603.2: Tools like roadmaps, customer interview snapshots, ideal customer profiles, competitive landscapes, and journey maps help leaders share context and set direction.

“This comes at all levels. Like, obviously as a founder, you have so much information, but even a product manager or a researcher, the whole team, so much just from what they've observed, that this really hits at all levels of disseminating, trying to share as much as you can about what you know.” - Allison Kassing (39:35)

603.3: For more valuable personas, enrich them with data and an understanding of both behavior and business opportunities.

“If we are aligned on what kind of business are we trying to drive or what kind of metrics are we trying to drive for these particular users, then we're already on the same page and we can focus on, okay, cool, we know that we're trying to solve the same problem for these people. We know why it's a valuable problem. So now how do we do that? What would actually drive that for this person?” - Mark Enache (40:34)

603.4: Find simple questions to identify who is best suited to get value from your product.

“...finding people who were already doing some of these things, but doing them in spreadsheets, because then he's not trying to convince them to change everything about what they're doing. He's just trying to say, hey, we can make this easier. Let's give you a tool for it.” - Holly Hester-Reilly (44:28)

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The universe tends toward chaos—and so does your product portfolio. A portfolio that is increasing in complexity experiences product entropy.

A well-balanced and streamlined product portfolio is both an art and a science, requiring constant attention.

Otherwise, your portfolio suffers from peanut buttering – spreading a little investment here and there without enough focus in any one area to achieve the high growth you seek.

Join us for a webinar on March 19th to learn how to drive order amidst the chaos. Details below.

And check out our picks on product portfolio complexity, tech migrations, and roadmaps.

Warmly,
Holly

Product Science Group’s Picks

Product Sprawl
By Rich Mironov
Just like entropy is ever-increasing in the universe, the complexity of product ecosystems increases rapidly over time if we don't regularly apply thoughtful and intentional product strategy practices. Learn more about Rich Mironov's take on Product Sprawl, which describes this phenomenon and its impacts, especially in the enterprise space. - Dina Levitan

A Simplified Roadmap Storytelling Framework
By Noa Ganot
Product strategy and roadmap development go hand-in-hand, and this article presents an interesting approach to roadmapping with the GOSPA framework. -Holly Hester-Reilly

How to Build a Roadmap for your Tech Migration that Delivers Business Outcomes
By Holly Hester-Reilly
Tech migrations don't have to stall innovation – discover how approaching them as product opportunities rather than lift-and-shift projects can deliver business value at every stage while keeping your team energized and your customers delighted. - Holly Hester-Reilly

Upcoming Webinar

We’re excited to announce an upcoming webinar with Product Science Group team members:

Fighting Product Entropy: User Journey Mapping for Overlapping Products

In complex systems, overlapping features and goals lead to confusion and lost value. In this webinar, learn how to fight product entropy by developing an evidence-based customer journey map. Discover how to combine data to create a cohesive view of the user experience. Explore techniques for consolidating product experiences and building stakeholder alignment. By the end of this session, you’ll be equipped to address product entropy, reduce complexity, and drive product consistency.

Join us on March 19th at 3PM EST

Brought to you by Product Science Group

Our team at Product Science Group partners with startup founders and product leaders to share The Product Science Principles and the accompanying framework, the Product Science Method, which I've developed over my time in tech startups. Through our coaching and consulting work, we help our clients determine which product growth opportunities they should pursue and build the product management skills to deliver on their goals. Reach out if you'd like to explore working together as a client or team member!

Thank you for reading!

Got a question for me or want to share something you're excited about? Just hit reply. I read every e-mail.

Inessa shares key aspects of product development, such as recognizing a true market opportunity, structuring teams for new market pursuits, and proving ROI when launching a new product.

During product development, Inessa and her team focused on fostering empathy and understanding among team members through direct customer interactions. Throughout the episode, practical insights and lessons learned are shared, emphasizing the significance of collaboration, innovation, and customer-centric approaches in product management and development. The conversation also touches on strategies for dealing with difficult stakeholders and addressing their concerns proactively.

Read the Show Notes on Our Website

Resource Links

Follow Inessa on LinkedIn

Visit the Hinge Health website

Celebrate Inessa’s 2023 Product Management Leader of the Year Award

Follow Holly on LinkedIn

Visit the Product Science Group website

Explore Product Science Workshops and Courses

Quotes from Inessa Lurye:

"So initially we had a really focused, dedicated tiger team before any engineering was staffed... We were speaking [to] members or potential members, like really doing in-depth research to understand this space." - Inessa Lurye

"We integrated the measurement of baseline data around pain and symptoms and specific patient reported outcomes... into our initial application to get a baseline and then into our app experience." - Inessa Lurye

"We had our pelvic floor physical therapists and Hinge Health... a group of clinical specialists who are PTs that work with the product team to develop new products." - Inessa Lurye

Lab Notes

Lab Note 602.1 is Great product leaders use all of the product science principles to succeed

“You've got continuous discovery and delivery with a robust program around encouraging team members, product managers and their teams to get involved in customer conversations and bring engineers and designers in. And you've got the empowered teams with her talking about team structure and how they put those teams in place. And the teams were well staffed with UX research, with department physical therapy, SMEs with engineers and designers and product managers.” - Holly Hester-Reilly (29:16)

Lab Note 602.2: Include a subject matter expert on your cross-functional team for a tighter feedback loop

”The most novel for me in that tiger team was what they called the Product PT, the Product Physical Therapist. So the subject matter expert being part of that team, really understanding the customer.” - Dina Levitan (30:50)

Lab Note 602.3: Evidence comes from customers, forums, and subject matter experts

“They also heard from customers in online forums like Facebook groups where people were going to talk about these things and they were seeing it from both people who were already their customers interacting with their other products and services and from people who were out in the market, who might not have been their customers, but who were talking about these problems, trying to find solutions to them, which tells you that they care about this problem enough to try to change their behavior to fix it.” - Holly Hester-Reilly (33:11)

Lab Note 602.4: When your buyers aren’t your users, make it easy for buyers to see the outcome

"One of the things that she did really beautifully was build in the measuring mechanisms so that as they built out their pilot, they could start to gather actual data so that they could come back and say to their customers, who were the people who were paying for the healthcare - These are the impacts we're making on your patients. And here's why you should be paying for this health care, because it has these benefits for how they're experiencing their life.”- Holly Hester-Reilly (34:11)

Lab Note 602.5: Simple tools can incentivize customer touchpoints for an outsized impact

“She talked about how having those interactions changed the energy for the team, especially talking about the engineers. But the whole team having those regular interactions with customers really made people owners and active participants in the process of gathering insights from the members.” - Holly Hester-Reilly (36:57)

Lab Note: 602.6: Spend the most time on your biggest naysayers

“But no matter what the situation is, just thinking about what are going to be their biggest questions, what can you imagine them saying and how are you going to respond to that is a really powerful activity for a Product leader.” - Holly Hester-Reilly (39:11)

Ready to elevate your product leadership game? Dive deep into practical solutions for real-world product challenges.

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