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

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

• 0:00
  Speaker 1: 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.
• 0:09
  Speaker 1: 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.
• 0:20
  Speaker 0: Welcome to the deep dive.
• 0:21
  Speaker 0: Yeah.
• 0:21
  Speaker 0: You know, looking across the post from product folks these last couple of weeks, there's this really strong.
• 0:29
  Speaker 0: common thread emerging.
• 0:30
  Speaker 0: It's all about discipline.
• 0:31
  Speaker 1: Discipline.
• 0:32
  Speaker 1: Interesting.
• 0:33
  Speaker 1: How so?
• 0:33
  Speaker 1: Well,
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  Speaker 0: it feels like a big push away from just, you know, churning out features.
• 0:36
  Speaker 0: People are demanding more focus on the fundamentals, foundational excellence, measurable outcomes,
• 0:43
  Speaker 1: that kind of thing.
• 0:44
  Speaker 1: Absolutely.
• 0:44
  Speaker 1: It's less about what the shiny new features and much more about the how.
• 0:48
  Speaker 1: Like how our company is actually set up to build the right things consistently.
• 0:51
  Speaker 1: It feels like a call for maturity, really.
• 0:54
  Speaker 0: Exactly.
• 0:55
  Speaker 1: So today we're going to dig into that.
• 0:56
  Speaker 1: We've got three big shifts that seem to define where things are heading.
• 0:59
  Speaker 1: First, how AI needs to move beyond just being a tool for speed and become more strategic.
• 1:05
  Speaker 1: Second, this absolute need for product strategy that's led by evidence, really focused on outcomes you can actually measure.
• 1:14
  Speaker 1: And finally, the rise of the product operating model.
• 1:17
  Speaker 1: Seems like that's becoming the essential structure you need if you want to scale properly.
• 1:22
  Speaker 0: Okay, let's jump into AI first.
• 1:24
  Speaker 0: Roman Pichlor mentioned something that really resonated.
• 1:27
  Speaker 0: AI adoption, it's kind of hitting a wall.
• 1:29
  Speaker 1: Yeah, I saw that.
• 1:30
  Speaker 1: Because teams are just pushing for speed, right?
• 1:33
  Speaker 1: Overthinking strategically.
• 1:34
  Speaker 1: Precisely.
• 1:34
  Speaker 0: It's that feature.
• 1:35
  Speaker 0: rush culture just, you know, cranked up to eleven with AI.
• 1:38
  Speaker 1: And the big problem there, especially with AI, is you end up just maximizing a bad strategy faster.
• 1:44
  Speaker 1: Nabil Khan had a really neat framework for this.
• 1:46
  Speaker 0: Oh, yeah.
• 1:47
  Speaker 1: Success really hinges on aligning three things.
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  Speaker 1: A real user problem, what AI can actually do realistically, and managing expectations.
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  Speaker 1: If you rush, you just blow past that alignment.
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  Speaker 0: And Nabil Khan also stressed finding problems where AI creates.
• 2:02
  Speaker 0: what was a ten X value, not just a little ten percent bump.
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  Speaker 0: Yes,
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  Speaker 1: that ten X versus ten percent is so key.
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  Speaker 1: Why is that ten X jump so critical though?
• 2:10
  Speaker 1: given all the pressure to just deliver something with AI.
• 2:14
  Speaker 0: Well, think about it.
• 2:15
  Speaker 0: 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.
• 2:25
  Speaker 0: Ah,
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  Speaker 1: OK, that makes sense.
• 2:26
  Speaker 0: You need a massive, like, step change improvement to really justify the investment and, frankly, the volatility of ML models.
• 2:36
  Speaker 0: If you're not hitting that ten X, you're often just better off with a standard deterministic solution.
• 2:41
  Speaker 1: So that implies PMs need a whole new kind of skill set, doesn't it?
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  Speaker 1: Akash Kutte talked about this, calling it AI product sense.
• 2:47
  Speaker 0: Exactly.
• 2:48
  Speaker 0: And it's different because AI isn't predictable like traditional software.
• 2:52
  Speaker 0: It's probabilistic.
• 2:53
  Speaker 0: And the costs can vary wildly depending on usage and training.
• 2:56
  Speaker 1: Right.
• 2:56
  Speaker 1: Gupta broke it down into, I think, six lenses, like model behavior, economics.
• 3:01
  Speaker 0: And the big one, trust and liability.
• 3:03
  Speaker 0: It's not enough anymore to just get the user psychology.
• 3:05
  Speaker 0: You have to understand model variance, latency, and the financial and legal fallout when things go wrong.
• 3:11
  Speaker 0: That's a heavy lift for PMs.
• 3:13
  Speaker 1: So how do people actually build that kind of intuition?
• 3:16
  Speaker 1: It sounds pretty technical.
• 3:17
  Speaker 0: Well, Paul Hurin's take is that you build it by doing, you can't just read a manual.
• 3:22
  Speaker 0: PMs need to get their hands dirty.
• 3:24
  Speaker 1: like actually fine-tuning models.
• 3:26
  Speaker 0: Yeah, or at least understanding it deeply.
• 3:28
  Speaker 0: Setting up observability, running proper AI evaluations.
• 3:32
  Speaker 0: You have to really internalize how these systems behave through practice.
• 3:36
  Speaker 1: And without that hands-on discipline.
• 3:38
  Speaker 1: Yeah.
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  Speaker 1: Well, that leads straight into the risks Stefan Wolpers flagged.
• 3:42
  Speaker 1: He saw three big failure patterns when teams rush AI.
• 3:46
  Speaker 0: Okay, let's hear them.
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  Speaker 1: The first one is very human.
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  Speaker 1: Validation shortcuts.
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  Speaker 1: This is basically automation bias.
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  Speaker 1: The AI spits out something plausible, maybe even looks sophisticated.
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  Speaker 0: You just accept it.
• 3:58
  Speaker 0: Right.
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  Speaker 1: You skip the hard work of actually testing it empirically.
• 4:01
  Speaker 1: You assume the machine knows best.
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  Speaker 0: Ouch.
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  Speaker 0: Okay, what's number two?
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  Speaker 1: This one's more strategic, maybe even a bit insidious.
• 4:08
  Speaker 1: Vision short-sightedness.
• 4:10
  Speaker 1: Wolpers calls it product vision erosion.
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  Speaker 0: How does that happen?
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  Speaker 1: Well, AI optimizes based on your current data and context, right?
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  Speaker 1: So it's really good at solving today's problems for today's customers.
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  Speaker 0: But it misses anything outside that bubble.
• 4:24
  Speaker 1: Exactly.
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  Speaker 1: You miss the disruptive stuff, the things your data doesn't show you yet.
• 4:28
  Speaker 1: You end up climbing your current hill really, really well, but it might be the wrong hill altogether.
• 4:32
  Speaker 0: That's a scary thought.
• 4:34
  Speaker 0: Perfectly optimized for your relevance.
• 4:36
  Speaker 0: What's the third one?
• 4:37
  Speaker 1: Human disconnection.
• 4:39
  Speaker 1: This one feels like it strikes at the very core of product management.
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  Speaker 1: It's when you let AI mediate your connection to your customers.
• 4:47
  Speaker 0: So instead of talking to users, you just look at AI-generated summaries.
• 4:51
  Speaker 0: Pretty
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  Speaker 1: much.
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  Speaker 1: 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.
• 5:01
  Speaker 0: And this isn't just theory, is it?
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  Speaker 0: People are seeing this play out.
• 5:04
  Speaker 1: Oh, absolutely.
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  Speaker 1: Mike Curranugin observed this huge pressure to ship AI features fast, often just ignoring whether users actually need them.
• 5:12
  Speaker 0: Which leads to features that look good on a roadmap.
• 5:15
  Speaker 0: but wouldn't survive five minutes with a real user.
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  Speaker 1: And Eliza Cabrera actually put a number on it, something like, forty-two percent of companies are already abandoning AI initiatives because they misinvested.
• 5:25
  Speaker 1: Wow,
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  Speaker 0: forty-two percent is significant.
• 5:28
  Speaker 0: They rushed the how without nailing the strategic what and
• 5:31
  Speaker 1: why.
• 5:33
  Speaker 0: Which, you know, tees up our next theme perfectly, product strategy and prioritization.
• 5:38
  Speaker 0: that failure to define the what and why.
• 5:41
  Speaker 0: The big shift here is moving away from just managing output, you know, features, tasks,
• 5:47
  Speaker 1: driving outcomes, actual impact, business value.
• 5:50
  Speaker 1: Yeah.
• 5:51
  Speaker 1: Mauricio Cardenas and Igor Voth both hammered this home.
• 5:55
  Speaker 1: Good PMs drive the business, they don't just administer a backlog.
• 5:58
  Speaker 0: Well, they're not just ticket managers.
• 5:59
  Speaker 1: But let's be real, prioritization is messy, even for the best PMs.
• 6:03
  Speaker 1: I saw Joss Confugere listed the usual frameworks, RIC, Moscow, but then he added the, let's say, unofficial framework, lie and buy time.
• 6:12
  Speaker 1: You know, when you tell a stakeholder their idea is going in the parking lot.
• 6:15
  Speaker 0: Oh, I know that parking lot.
• 6:16
  Speaker 0: It's more of a digital graveyard, as he put it.
• 6:18
  Speaker 1: Exactly.
• 6:19
  Speaker 1: It's funny because it's true.
• 6:20
  Speaker 1: That graveyard exists because PMs often lack the clarity or maybe the authority to just say no to bad ideas cleanly.
• 6:29
  Speaker 0: And getting that clarity.
• 6:30
  Speaker 0: Well, Arushi Apple had a great insight.
• 6:32
  Speaker 0: After shipping, I think, four roadmaps that just went nowhere, she realized something.
• 6:38
  Speaker 0: fundamental what
• 6:39
  Speaker 1: was that?
• 6:39
  Speaker 0: the roadmap?
• 6:40
  Speaker 0: it isn't the plan itself it's just the receipt for the decisions.
• 6:43
  Speaker 0: you've already made
• 6:44
  Speaker 1: a receipt.
• 6:45
  Speaker 1: i love that framing.
• 6:46
  Speaker 1: it forces you to back up doesn't it totally?
• 6:49
  Speaker 0: you have to start higher get alignment on the overall company intent first.
• 6:53
  Speaker 0: that shapes the product strategy which informs your big bets.
• 6:56
  Speaker 0: right.
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  Speaker 0: then you write the roadmap as the execution sequence.
• 6:59
  Speaker 1: without that chain the roadmap is just a wish list.
• 7:02
  Speaker 1: you get blamed for not delivering
• 7:03
  Speaker 0: precisely.
• 7:04
  Speaker 0: And when you have that strategic clarity, then you can be properly evidence-led.
• 7:07
  Speaker 0: You can move past just gut feelings.
• 7:10
  Speaker 0: Rishi Kana made a good point here.
• 7:11
  Speaker 0: Which was?
• 7:12
  Speaker 0: Intuition is great for spotting potential, for getting started.
• 7:15
  Speaker 0: But it's data, it's insights that keep you honest.
• 7:18
  Speaker 0: Stop you falling in love with your own ideas.
• 7:20
  Speaker 1: Right.
• 7:21
  Speaker 1: Which connects directly to discovery, doesn't it?
• 7:24
  Speaker 1: Benny Shaken argued that the whole point of discovery is because innovation is just fundamentally uncertain.
• 7:30
  Speaker 0: Yeah, it's job isn't just to validate your brilliant idea.
• 7:33
  Speaker 0: It's about cheap learning, using prototypes, fake doors, whatever.
• 7:38
  Speaker 1: To replace those optimistic, Excel-driven hockey stick dreams.
• 7:42
  Speaker 0: Yes.
• 7:43
  Speaker 0: Replace those dreams with actual signals from the real world.
• 7:47
  Speaker 0: You only scale what actually earns the right to be scaled.
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  Speaker 1: Okay, so strategy sets the what?
• 7:52
  Speaker 1: Discovery validates it.
• 7:54
  Speaker 1: But we can't forget how well it's built.
• 7:56
  Speaker 1: Dr.
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  Speaker 1: Bartjewarski made a really strong case about technical quality.
• 7:59
  Speaker 0: Oh yes, stability, performance, fixing bugs.
• 8:02
  Speaker 1: He argues it's not some afterthought, not just tech debt to manage later.
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  Speaker 1: It's a strategic feature.
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  Speaker 1: If you neglect it, you get costly failures and boom, user trust evaporates.
• 8:11
  Speaker 1: Performance
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  Speaker 0: is a feature.
• 8:12
  Speaker 0: Full stop.
• 8:13
  Speaker 0: Amy Mitchell added to that, saying companies should fund value streams, not just short-term projects.
• 8:18
  Speaker 1: And invest in the boring plumbing, as she called it.
• 8:21
  Speaker 1: the architecture.
• 8:22
  Speaker 0: Exactly.
• 8:23
  Speaker 0: 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.
• 8:30
  Speaker 1: Yeah.
• 8:31
  Speaker 0: So all these things, outcome focus, continuous discover, tech quality, they all need a solid, repeatable structure to work.
• 8:40
  Speaker 0: Which brings us nicely to theme number three.
• 8:42
  Speaker 0: Yeah.
• 8:43
  Speaker 0: The product operating model or POM.
• 8:46
  Speaker 1: Yeah, and Stephen Greeney has offered a really helpful clarification here because I think people mix this up.
• 8:51
  Speaker 1: Agile tells you how to build stuff, right?
• 8:53
  Speaker 1: It's about delivery.
• 8:54
  Speaker 0: Sprints, stand-ups.
• 8:56
  Speaker 1: But the product operating model, that tells you what to build and why.
• 9:00
  Speaker 1: It connects the strategy, the discovery, and the delivery into one coherent system.
• 9:04
  Speaker 1: And
• 9:04
  Speaker 0: if you don't have that model, you're just... You're stuck being a feature factory.
• 9:08
  Speaker 0: You might be super agile at delivering things.
• 9:10
  Speaker 1: But they're the wrong things.
• 9:11
  Speaker 1: delivered quickly.
• 9:12
  Speaker 0: Exactly.
• 9:13
  Speaker 0: Now, the function that supports this, ProductOps, is evolving fast.
• 9:18
  Speaker 0: Aiden ZayaPoor sees it moving beyond just being the operational glue.
• 9:22
  Speaker 1: To becoming more like a strategic intelligence engine.
• 9:25
  Speaker 1: Sensing, deciding, scaling the whole product org.
• 9:27
  Speaker 0: That's the idea.
• 9:29
  Speaker 0: But there's a catch.
• 9:30
  Speaker 0: ProductOps can easily just become another layer of bureaucracy if you're not careful.
• 9:34
  Speaker 1: Right.
• 9:35
  Speaker 1: And Tony Landy warned about this.
• 9:37
  Speaker 1: Success isn't just about hiring more ops people or buying fancy tools.
• 9:41
  Speaker 1: No,
• 9:41
  Speaker 0: it's about intention in designing the work itself.
• 9:44
  Speaker 0: How are decisions actually made?
• 9:46
  Speaker 0: How is knowledge shared?
• 9:47
  Speaker 0: How do you maintain alignment?
• 9:49
  Speaker 0: The structure has to reduce friction, not create more.
• 9:53
  Speaker 1: Okay, so if you're building this out, how do you roll it out effectively?
• 9:57
  Speaker 0: Well, Melissa Carey's advice is crucial.
• 10:00
  Speaker 0: Don't try to boil the ocean.
• 10:01
  Speaker 0: Don't roll out new processes everywhere on day one.
• 10:04
  Speaker 1: Start small, pilot with one team.
• 10:06
  Speaker 0: Exactly.
• 10:07
  Speaker 0: And maybe most importantly, product ops needs to act like a helpful shepherd, not the process police.
• 10:12
  Speaker 0: You need buy-in.
• 10:13
  Speaker 0: You want bottom-up adoption, not just top-down enforcement that everyone secretly resents.
• 10:17
  Speaker 0: Finding
• 10:17
  Speaker 1: that balance between standard ways of working and team autonomy.
• 10:21
  Speaker 1: It's tricky.
• 10:22
  Speaker 0: It is.
• 10:23
  Speaker 0: And underpinning that whole model, getting back to evidence, is data.
• 10:27
  Speaker 0: Hale Walton really emphasized that a good product-based operating model needs to run on smart data.
• 10:32
  Speaker 1: Specific, measurable, achievable, relevant, time-bound, the classic.
• 10:37
  Speaker 0: Right.
• 10:38
  Speaker 0: But the point is ensuring data drives outcomes, not just tracking activity.
• 10:43
  Speaker 0: It enables agile governance, not just busy work reporting.
• 10:46
  Speaker 0: And
• 10:46
  Speaker 1: what holds this whole complex system together?
• 10:48
  Speaker 1: What's the anchor?
• 10:49
  Speaker 0: According to Laura Giddings, it comes back to the vision.
• 10:52
  Speaker 0: A really crisp, clear product vision.
• 10:55
  Speaker 0: That's the true North, the compass for the entire operating model.
• 10:58
  Speaker 0: Everything should align back to that.
• 10:59
  Speaker 1: Okay, moving to the final stage of that model.
• 11:02
  Speaker 1: Launch excellence.
• 11:04
  Speaker 1: And maybe the role of the PM, who oversees it all.
• 11:06
  Speaker 1: So you've built a great product using a great system.
• 11:10
  Speaker 1: You still need to actually get it out there successfully.
• 11:12
  Speaker 1: Yep,
• 11:12
  Speaker 0: the go-to-market.
• 11:13
  Speaker 0: Yeah.
• 11:15
  Speaker 0: boiled launch success down to foundational clarity.
• 11:18
  Speaker 0: Meaning?
• 11:18
  Speaker 0: A super clear, ideal customer profile, sharp positioning so everyone knows what it is and who it's for, and then sales execution.
• 11:25
  Speaker 0: that's totally aligned with that.
• 11:26
  Speaker 0: And
• 11:26
  Speaker 1: that alignment piece, that's often where the wheels fall off, isn't it?
• 11:29
  Speaker 0: Oh, absolutely.
• 11:30
  Speaker 0: Katya Handsome highlighted this huge gap people have between shipping code and actually launching a product.
• 11:35
  Speaker 0: Vendors mix them up all the time.
• 11:37
  Speaker 0: Shipping
• 11:37
  Speaker 1: is just step one.
• 11:38
  Speaker 1: Launching is the whole orchestra playing together.
• 11:41
  Speaker 0: Exactly.
• 11:42
  Speaker 0: Launching means total organizational readiness.
• 11:45
  Speaker 0: Sales needs working demos.
• 11:47
  Speaker 0: Customer success needs to be trained on the changes.
• 11:49
  Speaker 0: Implementation needs the right pricing and messaging.
• 11:52
  Speaker 0: That readiness checklist, that's where PMs really earn their keep.
• 11:56
  Speaker 0: Success isn't just hitting deploy.
• 11:59
  Speaker 1: This kind of end-to-end ownership really defines the modern PM role, moving way beyond just speshing features.
• 12:06
  Speaker 1: Herschel Valentina called this the full-stack PM.
• 12:09
  Speaker 0: Cool stack, meaning?
• 12:10
  Speaker 1: 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.
• 12:19
  Speaker 1: Wow,
• 12:19
  Speaker 0: okay.
• 12:20
  Speaker 0: That sounds like a lot for one person, honestly.
• 12:22
  Speaker 0: Isn't that a recipe for burnout?
• 12:23
  Speaker 0: Can one person realistically juggle all of that?
• 12:27
  Speaker 1: It's definitely a huge scope, no doubt.
• 12:29
  Speaker 1: But Valentina's argument is that the PM is fundamentally there to create value, and value only happens when all those pieces connect
• 12:36
  Speaker 0: properly.
• 12:37
  Speaker 0: OK, fair point.
• 12:38
  Speaker 1: And to actually succeed in that massive role, Diana Matay shared a really vital lesson about communication.
• 12:45
  Speaker 1: PMs need to stop talking just in product jargon, preaching outcomes.
• 12:49
  Speaker 1: only other PMs understand.
• 12:51
  Speaker 0: Start speaking.
• 12:52
  Speaker 1: Business.
• 12:53
  Speaker 1: Talk about revenue, market share efficiency, ROI, use the language leadership understands.
• 12:58
  Speaker 1: That's how product gets the influence it needs, earns that seat at the strategic table.
• 13:03
  Speaker 0: Makes sense.
• 13:03
  Speaker 0: You got to translate product value into business value.
• 13:06
  Speaker 1: Exactly.
• 13:07
  Speaker 1: Wow.
• 13:07
  Speaker 1: Okay.
• 13:08
  Speaker 1: This deep dive really traced the evolution, didn't it?
• 13:10
  Speaker 1: from just features to strategy to the whole operating system to the full stack PM tying it all together.
• 13:17
  Speaker 1: Yeah,
• 13:17
  Speaker 0: the clear message from all these different voices seems to be building the product is maybe the easier part now.
• 13:22
  Speaker 0: The real challenge is building the system that can reliably deliver the right product.
• 13:26
  Speaker 1: And making sure that system uses AI strategically stays focused on outcomes and can actually launch things properly across the whole company.
• 13:35
  Speaker 0: Right.
• 13:35
  Speaker 0: It forces a different way of thinking.
• 13:37
  Speaker 0: Sophie Johnson summed it up nicely.
• 13:39
  Speaker 0: Treat the transformation itself as a product.
• 13:43
  Speaker 0: Know your customers for the change your internal teams.
• 13:46
  Speaker 0: Understand their pain points in adopting the new ways of working.
• 13:50
  Speaker 0: Define the outcomes you want from the change.
• 13:53
  Speaker 0: Experiment with how to achieve them.
• 13:54
  Speaker 0: That's
• 13:55
  Speaker 1: a fantastic way to frame it.
• 13:56
  Speaker 1: Build the system first, then the product, and the transformation is the ultimate product job.
• 14:01
  Speaker 0: Couldn't agree more.
• 14:02
  Speaker 0: So before we close out, if you enjoyed this deep dive, new episodes drop every two weeks.
• 14:07
  Speaker 0: Also check out our other editions on ICT and tech, artificial intelligence, cloud, sustainability and green ICT, defense tech and health tech.
• 14:15
  Speaker 1: Thanks so much for joining us for this deep dive.
• 14:17
  Speaker 1: Definitely subscribe so you don't miss our next look at what's really shaping the digital landscape.