Best of LinkedIn: Digital Products & Services CW 44/ 45

00:00:00: This episode is provided by Thomas Allgaier and Frennis based on the most relevant LinkedIn posts about digital products and services in calendar weeks, forty four and forty five.

00:00:09: Frennis is a B to B market research company helping enterprises gain the market customer and competitive insights needed to drive customer centric and cost efficient product development.

00:00:20: Welcome to the deep dive.

00:00:21: Yeah.

00:00:21: You know, looking across the post from product folks these last couple of weeks, there's this really strong.

00:00:29: common thread emerging.

00:00:30: It's all about discipline.

00:00:31: Discipline.

00:00:32: Interesting.

00:00:33: How so?

00:00:33: Well,

00:00:33: it feels like a big push away from just, you know, churning out features.

00:00:36: People are demanding more focus on the fundamentals, foundational excellence, measurable outcomes,

00:00:43: that kind of thing.

00:00:44: Absolutely.

00:00:44: It's less about what the shiny new features and much more about the how.

00:00:48: Like how our company is actually set up to build the right things consistently.

00:00:51: It feels like a call for maturity, really.

00:00:54: Exactly.

00:00:55: So today we're going to dig into that.

00:00:56: We've got three big shifts that seem to define where things are heading.

00:00:59: First, how AI needs to move beyond just being a tool for speed and become more strategic.

00:01:05: Second, this absolute need for product strategy that's led by evidence, really focused on outcomes you can actually measure.

00:01:14: And finally, the rise of the product operating model.

00:01:17: Seems like that's becoming the essential structure you need if you want to scale properly.

00:01:22: Okay, let's jump into AI first.

00:01:24: Roman Pichlor mentioned something that really resonated.

00:01:27: AI adoption, it's kind of hitting a wall.

00:01:29: Yeah, I saw that.

00:01:30: Because teams are just pushing for speed, right?

00:01:33: Overthinking strategically.

00:01:34: Precisely.

00:01:34: It's that feature.

00:01:35: rush culture just, you know, cranked up to eleven with AI.

00:01:38: And the big problem there, especially with AI, is you end up just maximizing a bad strategy faster.

00:01:44: Nabil Khan had a really neat framework for this.

00:01:46: Oh, yeah.

00:01:47: Success really hinges on aligning three things.

00:01:50: A real user problem, what AI can actually do realistically, and managing expectations.

00:01:54: If you rush, you just blow past that alignment.

00:01:57: And Nabil Khan also stressed finding problems where AI creates.

00:02:02: what was a ten X value, not just a little ten percent bump.

00:02:05: Yes,

00:02:05: that ten X versus ten percent is so key.

00:02:07: Why is that ten X jump so critical though?

00:02:10: given all the pressure to just deliver something with AI.

00:02:14: Well, think about it.

00:02:15: The cost to maintain these things, the inherent risks with primabilistic models, the governance complexity, a tiny ten percent gain, it probably gets eaten up by all that overhead.

00:02:25: Ah,

00:02:25: OK, that makes sense.

00:02:26: You need a massive, like, step change improvement to really justify the investment and, frankly, the volatility of ML models.

00:02:36: If you're not hitting that ten X, you're often just better off with a standard deterministic solution.

00:02:41: So that implies PMs need a whole new kind of skill set, doesn't it?

00:02:44: Akash Kutte talked about this, calling it AI product sense.

00:02:47: Exactly.

00:02:48: And it's different because AI isn't predictable like traditional software.

00:02:52: It's probabilistic.

00:02:53: And the costs can vary wildly depending on usage and training.

00:02:56: Right.

00:02:56: Gupta broke it down into, I think, six lenses, like model behavior, economics.

00:03:01: And the big one, trust and liability.

00:03:03: It's not enough anymore to just get the user psychology.

00:03:05: You have to understand model variance, latency, and the financial and legal fallout when things go wrong.

00:03:11: That's a heavy lift for PMs.

00:03:13: So how do people actually build that kind of intuition?

00:03:16: It sounds pretty technical.

00:03:17: Well, Paul Hurin's take is that you build it by doing, you can't just read a manual.

00:03:22: PMs need to get their hands dirty.

00:03:24: like actually fine-tuning models.

00:03:26: Yeah, or at least understanding it deeply.

00:03:28: Setting up observability, running proper AI evaluations.

00:03:32: You have to really internalize how these systems behave through practice.

00:03:36: And without that hands-on discipline.

00:03:38: Yeah.

00:03:38: Well, that leads straight into the risks Stefan Wolpers flagged.

00:03:42: He saw three big failure patterns when teams rush AI.

00:03:46: Okay, let's hear them.

00:03:47: The first one is very human.

00:03:49: Validation shortcuts.

00:03:51: This is basically automation bias.

00:03:54: The AI spits out something plausible, maybe even looks sophisticated.

00:03:57: You just accept it.

00:03:58: Right.

00:03:58: You skip the hard work of actually testing it empirically.

00:04:01: You assume the machine knows best.

00:04:03: Ouch.

00:04:03: Okay, what's number two?

00:04:05: This one's more strategic, maybe even a bit insidious.

00:04:08: Vision short-sightedness.

00:04:10: Wolpers calls it product vision erosion.

00:04:13: How does that happen?

00:04:14: Well, AI optimizes based on your current data and context, right?

00:04:18: So it's really good at solving today's problems for today's customers.

00:04:22: But it misses anything outside that bubble.

00:04:24: Exactly.

00:04:25: You miss the disruptive stuff, the things your data doesn't show you yet.

00:04:28: You end up climbing your current hill really, really well, but it might be the wrong hill altogether.

00:04:32: That's a scary thought.

00:04:34: Perfectly optimized for your relevance.

00:04:36: What's the third one?

00:04:37: Human disconnection.

00:04:39: This one feels like it strikes at the very core of product management.

00:04:43: It's when you let AI mediate your connection to your customers.

00:04:47: So instead of talking to users, you just look at AI-generated summaries.

00:04:51: Pretty

00:04:51: much.

00:04:52: You lose that direct dialogue, the nuance, the judgment calls, and if you lose that, you definitely lose the ability to spot those disruptive opportunities we just talked about.

00:05:01: And this isn't just theory, is it?

00:05:02: People are seeing this play out.

00:05:04: Oh, absolutely.

00:05:05: Mike Curranugin observed this huge pressure to ship AI features fast, often just ignoring whether users actually need them.

00:05:12: Which leads to features that look good on a roadmap.

00:05:15: but wouldn't survive five minutes with a real user.

00:05:18: And Eliza Cabrera actually put a number on it, something like, forty-two percent of companies are already abandoning AI initiatives because they misinvested.

00:05:25: Wow,

00:05:26: forty-two percent is significant.

00:05:28: They rushed the how without nailing the strategic what and

00:05:31: why.

00:05:33: Which, you know, tees up our next theme perfectly, product strategy and prioritization.

00:05:38: that failure to define the what and why.

00:05:41: The big shift here is moving away from just managing output, you know, features, tasks,

00:05:47: driving outcomes, actual impact, business value.

00:05:50: Yeah.

00:05:51: Mauricio Cardenas and Igor Voth both hammered this home.

00:05:55: Good PMs drive the business, they don't just administer a backlog.

00:05:58: Well, they're not just ticket managers.

00:05:59: But let's be real, prioritization is messy, even for the best PMs.

00:06:03: I saw Joss Confugere listed the usual frameworks, RIC, Moscow, but then he added the, let's say, unofficial framework, lie and buy time.

00:06:12: You know, when you tell a stakeholder their idea is going in the parking lot.

00:06:15: Oh, I know that parking lot.

00:06:16: It's more of a digital graveyard, as he put it.

00:06:18: Exactly.

00:06:19: It's funny because it's true.

00:06:20: That graveyard exists because PMs often lack the clarity or maybe the authority to just say no to bad ideas cleanly.

00:06:29: And getting that clarity.

00:06:30: Well, Arushi Apple had a great insight.

00:06:32: After shipping, I think, four roadmaps that just went nowhere, she realized something.

00:06:38: fundamental what

00:06:39: was that?

00:06:39: the roadmap?

00:06:40: it isn't the plan itself it's just the receipt for the decisions.

00:06:43: you've already made

00:06:44: a receipt.

00:06:45: i love that framing.

00:06:46: it forces you to back up doesn't it totally?

00:06:49: you have to start higher get alignment on the overall company intent first.

00:06:53: that shapes the product strategy which informs your big bets.

00:06:56: right.

00:06:56: then you write the roadmap as the execution sequence.

00:06:59: without that chain the roadmap is just a wish list.

00:07:02: you get blamed for not delivering

00:07:03: precisely.

00:07:04: And when you have that strategic clarity, then you can be properly evidence-led.

00:07:07: You can move past just gut feelings.

00:07:10: Rishi Kana made a good point here.

00:07:11: Which was?

00:07:12: Intuition is great for spotting potential, for getting started.

00:07:15: But it's data, it's insights that keep you honest.

00:07:18: Stop you falling in love with your own ideas.

00:07:20: Right.

00:07:21: Which connects directly to discovery, doesn't it?

00:07:24: Benny Shaken argued that the whole point of discovery is because innovation is just fundamentally uncertain.

00:07:30: Yeah, it's job isn't just to validate your brilliant idea.

00:07:33: It's about cheap learning, using prototypes, fake doors, whatever.

00:07:38: To replace those optimistic, Excel-driven hockey stick dreams.

00:07:42: Yes.

00:07:43: Replace those dreams with actual signals from the real world.

00:07:47: You only scale what actually earns the right to be scaled.

00:07:50: Okay, so strategy sets the what?

00:07:52: Discovery validates it.

00:07:54: But we can't forget how well it's built.

00:07:56: Dr.

00:07:56: Bartjewarski made a really strong case about technical quality.

00:07:59: Oh yes, stability, performance, fixing bugs.

00:08:02: He argues it's not some afterthought, not just tech debt to manage later.

00:08:05: It's a strategic feature.

00:08:07: If you neglect it, you get costly failures and boom, user trust evaporates.

00:08:11: Performance

00:08:11: is a feature.

00:08:12: Full stop.

00:08:13: Amy Mitchell added to that, saying companies should fund value streams, not just short-term projects.

00:08:18: And invest in the boring plumbing, as she called it.

00:08:21: the architecture.

00:08:22: Exactly.

00:08:23: That stable foundation is what lets you actually pivot your strategy quickly when you need to without everything falling apart or needing a massive rewrite.

00:08:30: Yeah.

00:08:31: So all these things, outcome focus, continuous discover, tech quality, they all need a solid, repeatable structure to work.

00:08:40: Which brings us nicely to theme number three.

00:08:42: Yeah.

00:08:43: The product operating model or POM.

00:08:46: Yeah, and Stephen Greeney has offered a really helpful clarification here because I think people mix this up.

00:08:51: Agile tells you how to build stuff, right?

00:08:53: It's about delivery.

00:08:54: Sprints, stand-ups.

00:08:56: But the product operating model, that tells you what to build and why.

00:09:00: It connects the strategy, the discovery, and the delivery into one coherent system.

00:09:04: And

00:09:04: if you don't have that model, you're just... You're stuck being a feature factory.

00:09:08: You might be super agile at delivering things.

00:09:10: But they're the wrong things.

00:09:11: delivered quickly.

00:09:12: Exactly.

00:09:13: Now, the function that supports this, ProductOps, is evolving fast.

00:09:18: Aiden ZayaPoor sees it moving beyond just being the operational glue.

00:09:22: To becoming more like a strategic intelligence engine.

00:09:25: Sensing, deciding, scaling the whole product org.

00:09:27: That's the idea.

00:09:29: But there's a catch.

00:09:30: ProductOps can easily just become another layer of bureaucracy if you're not careful.

00:09:34: Right.

00:09:35: And Tony Landy warned about this.

00:09:37: Success isn't just about hiring more ops people or buying fancy tools.

00:09:41: No,

00:09:41: it's about intention in designing the work itself.

00:09:44: How are decisions actually made?

00:09:46: How is knowledge shared?

00:09:47: How do you maintain alignment?

00:09:49: The structure has to reduce friction, not create more.

00:09:53: Okay, so if you're building this out, how do you roll it out effectively?

00:09:57: Well, Melissa Carey's advice is crucial.

00:10:00: Don't try to boil the ocean.

00:10:01: Don't roll out new processes everywhere on day one.

00:10:04: Start small, pilot with one team.

00:10:06: Exactly.

00:10:07: And maybe most importantly, product ops needs to act like a helpful shepherd, not the process police.

00:10:12: You need buy-in.

00:10:13: You want bottom-up adoption, not just top-down enforcement that everyone secretly resents.

00:10:17: Finding

00:10:17: that balance between standard ways of working and team autonomy.

00:10:21: It's tricky.

00:10:22: It is.

00:10:23: And underpinning that whole model, getting back to evidence, is data.

00:10:27: Hale Walton really emphasized that a good product-based operating model needs to run on smart data.

00:10:32: Specific, measurable, achievable, relevant, time-bound, the classic.

00:10:37: Right.

00:10:38: But the point is ensuring data drives outcomes, not just tracking activity.

00:10:43: It enables agile governance, not just busy work reporting.

00:10:46: And

00:10:46: what holds this whole complex system together?

00:10:48: What's the anchor?

00:10:49: According to Laura Giddings, it comes back to the vision.

00:10:52: A really crisp, clear product vision.

00:10:55: That's the true North, the compass for the entire operating model.

00:10:58: Everything should align back to that.

00:10:59: Okay, moving to the final stage of that model.

00:11:02: Launch excellence.

00:11:04: And maybe the role of the PM, who oversees it all.

00:11:06: So you've built a great product using a great system.

00:11:10: You still need to actually get it out there successfully.

00:11:12: Yep,

00:11:12: the go-to-market.

00:11:13: Yeah.

00:11:15: boiled launch success down to foundational clarity.

00:11:18: Meaning?

00:11:18: A super clear, ideal customer profile, sharp positioning so everyone knows what it is and who it's for, and then sales execution.

00:11:25: that's totally aligned with that.

00:11:26: And

00:11:26: that alignment piece, that's often where the wheels fall off, isn't it?

00:11:29: Oh, absolutely.

00:11:30: Katya Handsome highlighted this huge gap people have between shipping code and actually launching a product.

00:11:35: Vendors mix them up all the time.

00:11:37: Shipping

00:11:37: is just step one.

00:11:38: Launching is the whole orchestra playing together.

00:11:41: Exactly.

00:11:42: Launching means total organizational readiness.

00:11:45: Sales needs working demos.

00:11:47: Customer success needs to be trained on the changes.

00:11:49: Implementation needs the right pricing and messaging.

00:11:52: That readiness checklist, that's where PMs really earn their keep.

00:11:56: Success isn't just hitting deploy.

00:11:59: This kind of end-to-end ownership really defines the modern PM role, moving way beyond just speshing features.

00:12:06: Herschel Valentina called this the full-stack PM.

00:12:09: Cool stack, meaning?

00:12:10: Someone who owns discovery, delivery, operations, and growth, connecting the dots from high-level strategy all the way through execution to the measurable outcomes, the whole value chain.

00:12:19: Wow,

00:12:19: okay.

00:12:20: That sounds like a lot for one person, honestly.

00:12:22: Isn't that a recipe for burnout?

00:12:23: Can one person realistically juggle all of that?

00:12:27: It's definitely a huge scope, no doubt.

00:12:29: But Valentina's argument is that the PM is fundamentally there to create value, and value only happens when all those pieces connect

00:12:36: properly.

00:12:37: OK, fair point.

00:12:38: And to actually succeed in that massive role, Diana Matay shared a really vital lesson about communication.

00:12:45: PMs need to stop talking just in product jargon, preaching outcomes.

00:12:49: only other PMs understand.

00:12:51: Start speaking.

00:12:52: Business.

00:12:53: Talk about revenue, market share efficiency, ROI, use the language leadership understands.

00:12:58: That's how product gets the influence it needs, earns that seat at the strategic table.

00:13:03: Makes sense.

00:13:03: You got to translate product value into business value.

00:13:06: Exactly.

00:13:07: Wow.

00:13:07: Okay.

00:13:08: This deep dive really traced the evolution, didn't it?

00:13:10: from just features to strategy to the whole operating system to the full stack PM tying it all together.

00:13:17: Yeah,

00:13:17: the clear message from all these different voices seems to be building the product is maybe the easier part now.

00:13:22: The real challenge is building the system that can reliably deliver the right product.

00:13:26: And making sure that system uses AI strategically stays focused on outcomes and can actually launch things properly across the whole company.

00:13:35: Right.

00:13:35: It forces a different way of thinking.

00:13:37: Sophie Johnson summed it up nicely.

00:13:39: Treat the transformation itself as a product.

00:13:43: Know your customers for the change your internal teams.

00:13:46: Understand their pain points in adopting the new ways of working.

00:13:50: Define the outcomes you want from the change.

00:13:53: Experiment with how to achieve them.

00:13:54: That's

00:13:55: a fantastic way to frame it.

00:13:56: Build the system first, then the product, and the transformation is the ultimate product job.

00:14:01: Couldn't agree more.

00:14:02: So before we close out, if you enjoyed this deep dive, new episodes drop every two weeks.

00:14:07: Also check out our other editions on ICT and tech, artificial intelligence, cloud, sustainability and green ICT, defense tech and health tech.

00:14:15: Thanks so much for joining us for this deep dive.

00:14:17: Definitely subscribe so you don't miss our next look at what's really shaping the digital landscape.