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Best of LinkedIn: Digital Products & Services CW 42/ 43 · Best of LinkedIn: Digital Transformation & Tech

• 0:00
  Speaker 1: This deep dive is provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts about digital products and services in calendar weeks.
• 0:08
  Speaker 1: forty-two and forty-three.
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  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.
• 0:21
  Speaker 1: Welcome back, everyone.
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  Speaker 1: We've sifted through a lot of insights to bring you the really critical market signals for digital products and services right now.
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  Speaker 1: What we're seeing looking across our sources from the ICT and tech world these past couple of weeks is this real collision between, let's say, execution discipline and this incredible AI-enabled speed.
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  Speaker 0: Exactly.
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  Speaker 0: It's like you can't just be good at one anymore.
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  Speaker 0: Success really demands having that structured execution, but running it at hyperspeed.
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  Speaker 1: So that's the core
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  Speaker 0: theme.
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  Speaker 0: Yeah.
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  Speaker 0: And this deep dive.
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  Speaker 0: today is really your shortcut to understanding these shifts.
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  Speaker 0: Leaders seem laser focused on, well, three main areas.
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  Speaker 0: First, defining the new product operating model, the POM, which interestingly seems much more about mindset than just org charts.
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  Speaker 0: Second, looking at really practical ways AI is speeding up discovery, prototyping, experimentation.
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  Speaker 0: And third, sort of redefining what good design and UX actually mean when, let's face it, user attention spans are just plummeting.
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  Speaker 1: Right.
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  Speaker 1: Okay, let's dig into that.
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  Speaker 1: Starting with theme one, nailing down this product operating model.
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  Speaker 1: You know, companies have been reorganizing for years, but the sources suggest the model itself is the key lever now for connecting strategy to, well, actually getting things shipped.
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  Speaker 1: You mentioned mindset.
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  Speaker 1: What does that really mean beyond a new diagram?
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  Speaker 0: Well the consensus and you see this in posts from people like Christopher Tritina is that loads of organizations have the shape of a POM, right?
• 1:44
  Speaker 0: They've got the squads of the roadmaps, but they're missing the the spirit Mm-hmm.
• 1:49
  Speaker 0: that truly successful POM is more of a culture less a document.
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  Speaker 0: it needs Three things that aren't negotiable Real empowerment, where teams genuinely own the outcomes, not just shipping features.
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  Speaker 0: Ah,
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  Speaker 1: that distinction.
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  Speaker 0: Yeah.
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  Speaker 0: Then accountability, meaning results are measured by the value delivered.
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  Speaker 0: And finally, a relentless, like laser sharp customer focus.
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  Speaker 1: That outcomes versus features point.
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  Speaker 1: That's usually where things get sticky, isn't it?
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  Speaker 1: I saw John Leslie reinforcing this need for structure, saying, a strong POM has to align three key things.
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  Speaker 1: Strategy, finding.
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  Speaker 1: a problem actually worth solving.
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  Speaker 1: Discovery finding the right solution to that problem.
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  Speaker 1: And delivery... you know, actually building and shipping it.
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  Speaker 1: If those three aren't tightly connected.
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  Speaker 1: The whole
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  Speaker 0: model just buckles.
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  Speaker 0: And that lack of connection leads straight to what Reuben Myland was talking about.
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  Speaker 0: Roadmaps that just, well, fail.
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  Speaker 0: Not because the ideas are bad, but they turn into these chaotic wish lists totally disconnected from the bigger picture.
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  Speaker 1: Like random acts of product development.
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  Speaker 0: Exactly.
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  Speaker 0: Myland suggests using something like a strategy pyramid to force that alignment, making sure every single initiative actually ladders up to the main business goals.
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  Speaker 0: It's an alignment tool, basically.
• 2:59
  Speaker 1: Okay, but here's where it gets tough, especially for big companies.
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  Speaker 1: We saw a lot of talk about the money side.
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  Speaker 1: Michael James Cyrus pointed out that a really core part of a working POM means shifting away from traditional CapEx project-based funding towards continuous product investment.
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  Speaker 1: That sounds like a huge internal battle.
• 3:16
  Speaker 1: Oh,
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  Speaker 0: it absolutely is.
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  Speaker 0: Because it forces finance departments to stop thinking about products like, you know, construction projects with a start and an end date.
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  Speaker 0: Right,
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  Speaker 1: like building a bridge.
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  Speaker 0: Exactly.
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  Speaker 0: And instead, treat them like living assets that need ongoing investment.
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  Speaker 0: That means rethinking quarterly budgets, risk assessment.
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  Speaker 0: It's fundamental.
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  Speaker 0: If you can't make that shift, your continuous discovery cycle is always going to hit a wall with the annual budget process.
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  Speaker 1: So, assuming you get the structure and the funding model sorted.
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  Speaker 1: The focus then shifts to the PMS themselves and the strategic skills they need.
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  Speaker 1: Roman Pichler was highlighting how critical it is for PMS to craft a really powerful product vision, one that actually unites different teams.
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  Speaker 0: Yeah, and avoiding those common traps, like confusing the long-term vision with the immediate strategy, or worse, changing the vision every quarter.
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  Speaker 1: Which kills credibility.
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  Speaker 1: Totally.
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  Speaker 0: The PMS role shifts, you know, from just overseeing execution
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  Speaker 1: to...
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  Speaker 0: wielding real strategic influence.
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  Speaker 0: And this is where I thought Courtney Jacobson's point was really interesting.
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  Speaker 1: Which was?
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  Speaker 0: That strong writing skills, being able to craft excellent product docs, PRDs, those Amazon style six pages.
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  Speaker 0: It's not just admin anymore.
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  Speaker 0: She argues it's actually a strategic, almost executive level skill now.
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  Speaker 1: I can see that is how you communicate that strategy, how you exercise authority and drive precision when you've got these empowered, maybe distributed.
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  Speaker 1: teams.
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  Speaker 1: High Sum Abdul Malik really drove this home too, saying that as PMs climb the ladder, the skills have to evolve past just execution.
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  Speaker 1: It becomes about pure strategy and leadership influence.
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  Speaker 0: Right.
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  Speaker 0: The higher you go, the more your impact comes from the clarity of your thinking communication, not how many Jira tickets you close, which actually leads us nicely into the next big theme.
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  Speaker 0: If theme one was about getting the internal discipline right,
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  Speaker 1: then theme two is about why that discipline is suddenly so critical.
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  Speaker 0: Precisely.
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  Speaker 0: Let's pivot to theme two.
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  Speaker 0: AI and product development.
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  Speaker 0: I mean, Rishi Kanan just puts it bluntly.
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  Speaker 0: AI has changed the speed of digital product development forever.
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  Speaker 0: Full stop.
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  Speaker 1: Yeah, you really feel that shift.
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  Speaker 1: What might have been, say, a six-month research project?
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  Speaker 1: Now maybe you get prototype results back into your work.
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  Speaker 1: The new approach seems to be running like ten cheap experiments in parallel, just to find the quickest path to any kind of signal.
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  Speaker 0: And it's not just for the coders anymore.
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  Speaker 0: The sources gave some great, really practical examples for non-technical PMs, too.
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  Speaker 0: Nikolai Golo has talked about tools like Claude Code, lowering the barrier for prototyping.
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  Speaker 1: How so?
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  Speaker 0: Well, non-technical PMs can now use it to summarize hours of user interview transcripts, synthesize research from dozens of docs, even draft pretty comprehensive PRDs and get AI feedback on them.
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  Speaker 0: It's basically empowering that discovery phase without needing to write a single line of code yourself.
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  Speaker 1: That is pretty remarkable.
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  Speaker 1: And Akash Gupta showed something similar with Miro AI, pointing towards the future of collaborative building.
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  Speaker 1: Instead of typing a separate prompt, the AI uses the mirror board itself, the stickies, the arrows, the flows as a context.
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  Speaker 0: Right, the canvas is the prompt.
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  Speaker 1: Exactly.
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  Speaker 1: And it instantly generates what was it, three diverse prototypes.
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  Speaker 1: That leap from abstract idea to something you can actually test is almost instantaneous now.
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  Speaker 0: And that speed is driving a whole new class of discovery tools.
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  Speaker 0: Right.
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  Speaker 0: Shannon Vee mentioned usersnap launching AI powered features things like accurate auto tagging of feedback, analyzing trends automatically, intelligently scoring user comments.
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  Speaker 1: So turning that mountain of raw customer feedback into something immediately actionable for the PM.
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  Speaker 0: Pretty much, yeah.
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  Speaker 0: Taking the grunt workout.
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  Speaker 1: But okay, our measurement models need to catch up, right?
• 6:53
  Speaker 1: Yeah.
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  Speaker 1: Craig Sturgis warned that if our mental models for product development are too simplistic, we'll just struggle to measure the ROI of AI accurately.
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  Speaker 1: We can't just count features shipped faster.
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  Speaker 1: We need better ways to tie it to actual business impact.
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  Speaker 0: Which raises a really key structural point about how we even build these AI products.
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  Speaker 0: Olga Saffonova highlighted something crucial.
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  Speaker 0: The old sequential way design mockups, then write specs, often fails with AI.
• 7:18
  Speaker 0: Why is that?
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  Speaker 0: Because she argues successful AI projects have to start with the data structure first.
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  Speaker 0: Nail down the data, then let the AI generate the interface for the behavior around it.
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  Speaker 0: You can't really design a UI effectively for emergent AI behavior if you haven't controlled the input data structure from the start.
• 7:35
  Speaker 1: That really does flip the standard process on its head.
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  Speaker 1: The tech dictates the workflow, not the other way around.
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  Speaker 1: Okay, now related to this, what should companies actually build themselves versus buying off the shelf?
• 7:47
  Speaker 1: Jason Bratzade had some pretty specific advice there.
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  Speaker 0: Yeah, his advice was really crisp and clear.
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  Speaker 0: He said basically only build a strategic twenty percent in-house.
• 7:56
  Speaker 0: And that twenty percent has to hit one of three criteria.
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  Speaker 0: It solves a truly proprietary problem unique to you.
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  Speaker 0: It creates genuine strategic differentiation, or it needs super deep integration into your core proprietary systems.
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  Speaker 1: And everything else.
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  Speaker 0: Everything else you should probably buy as a service.
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  Speaker 0: Saves massive time, massive resources.
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  Speaker 0: Focus your internal firepower where it truly counts.
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  Speaker 1: And Marcus Wagner ties this build versus buy decision directly to how AI companies make money and become sticky in enterprises.
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  Speaker 1: He calls it the strategic dependency
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  Speaker 0: loop.
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  Speaker 0: Right, the idea is when enterprises start paying for AI tools, they integrate them more deeply.
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  Speaker 0: This integration then compounds the switching costs over time.
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  Speaker 0: It's harder and harder to rip it out.
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  Speaker 1: So that dependency explains why some really valuable AI companies might not have huge user numbers, but they generate massive revenue from deep entrenchment in a few large customers.
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  Speaker 1: It's less about volume, more about how embedded they become.
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  Speaker 0: Exactly.
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  Speaker 0: It's a powerful lesson for anyone building B to B tech.
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  Speaker 0: Your real competitive advantage, your moat, is often just the sheer pain and cost of removing your product, not just how shiny your features are.
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  Speaker 1: Okay, that dependency idea sets us up perfectly for the final theme.
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  Speaker 1: Design, UX, and the customer experience.
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  Speaker 1: Because after all this internal alignment and AI acceleration, the rubber meets the road with whether the product actually, you know, works for the person using it.
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  Speaker 1: Let's start with that conceptual shift you mentioned, what design even is now.
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  Speaker 0: Yeah, it feels like we're moving beyond just making things look pretty.
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  Speaker 0: Matt Prisigika and Irwin Zadeh.
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  Speaker 0: both talked about design being fundamentally about creating understanding.
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  Speaker 0: Understanding between the person and the technology, it's managing those invisible forces between people, problems and possibilities.
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  Speaker 1: Invisible forces.
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  Speaker 1: I like
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  Speaker 0: that.
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  Speaker 0: And Alice Lane put it very simply.
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  Speaker 0: People don't need a digital product.
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  Speaker 0: They need a solution that makes their life better.
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  Speaker 0: The design has to serve that core need, that outcome.
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  Speaker 0: And
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  Speaker 1: this focus on practical value over just visuals really comes through when you look at measurable UX quality.
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  Speaker 1: Mujee Baziz pointed to studies showing that really good UX, even on a visually dated site, can seriously outperform a sleek, modern looking one, sometimes by under twenty percent better user retention.
• 10:08
  Speaker 1: Wow,
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  Speaker 0: twenty percent is significant.
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  Speaker 1: Yeah, and the key factors weren't fancy animations.
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  Speaker 1: It was ruthless focus on the basics.
• 10:14
  Speaker 1: Accessibility, can everyone use it?
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  Speaker 1: Load speed and latency is fast.
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  Speaker 1: And clarity in the messaging, does it just work?
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  Speaker 1: Do I get it instantly?
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  Speaker 0: and achieving that instant clarity is particularly tough in the B-to-B space.
• 10:26
  Speaker 0: Malgrzada, Pyrenik, Jezierska brought up the status quo bias.
• 10:30
  Speaker 0: Ah,
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  Speaker 1: yes, we've always done it this way, problem.
• 10:33
  Speaker 0: Exactly.
• 10:34
  Speaker 0: Enterprise users often stick with the clunky old system, even if a new one is objectively better, simply because learning the new tool takes mental effort and time away from their actual job.
• 10:45
  Speaker 0: Innovation gets resisted if it feels like hard work.
• 10:47
  Speaker 1: which connects straight to Shihab Barum's point about our collapsing attention spans.
• 10:52
  Speaker 1: If users are already battling that status quo bias, your product needs to feel almost painfully low effort.
• 10:57
  Speaker 1: It has to deliver that aha moment in like one click.
• 11:01
  Speaker 1: if they have to spend five minutes figuring out the value prop.
• 11:04
  Speaker 0: You've probably lost them already, gone.
• 11:06
  Speaker 0: So given this need for speed and focus, Booster Acuscuner offered some really useful advice on how to balance your research methods.
• 11:13
  Speaker 0: We just don't have time for deep, heavy assumption mapping on every single feature idea anymore.
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  Speaker 0: Right.
• 11:18
  Speaker 1: You'd never ship anything.
• 11:19
  Speaker 0: Precisely.
• 11:20
  Speaker 0: So reserve that deep mapping for the really risky, the radical, the potentially disruptive ideas.
• 11:26
  Speaker 0: But if you already have some initial market signals, some basic insights, just move straight into quick validation loops, test for desirability, test for immediate understanding, get out there fast.
• 11:37
  Speaker 1: That makes a lot of operational sense.
• 11:39
  Speaker 1: And ultimately, I suppose it all comes back to how we define success in the first place.
• 11:43
  Speaker 1: Cato Malley reminded us that success isn't measured by output anymore, how much stuff we ship.
• 11:47
  Speaker 1: It's measured by impact, what actually changes in the world for the user, for the business, because we shipped something.
• 11:52
  Speaker 0: Yeah, and Igor Voth stressed that translating that ambition into measurable outcomes is key.
• 11:57
  Speaker 0: He said most PMs get really good at execution, but far fewer actually mastering measuring and achieving genuine impact.
• 12:04
  Speaker 1: So wrapping up this deep dive then.
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  Speaker 1: The thread connecting the operating models, the AI acceleration, the disciplined UX, it seems to be this relentless demand for ruthless prioritization.
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  Speaker 1: And hyper-focused execution aims squarely at delivering measurable value.
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  Speaker 1: The fact that AI is making execution cheaper and faster just puts even more pressure on getting the strategy and the intent right up front.
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  Speaker 0: Absolutely.
• 12:27
  Speaker 0: Just think about that for a second.
• 12:29
  Speaker 0: In a world where AI can spit out prototypes and code in days, maybe hours.
• 12:34
  Speaker 0: Yeah.
• 12:35
  Speaker 0: The most valuable skill for a product manager probably isn't execution ability anymore.
• 12:39
  Speaker 0: So
• 12:39
  Speaker 1: what is it then?
• 12:40
  Speaker 0: It's defining the right intent, asking the really hard strategic questions, and maybe most importantly, having the organizational courage and the data to decide what not to build that rigorous strategic discipline.
• 12:52
  Speaker 0: That feels like the ultimate competitive advantage right now.
• 12:54
  Speaker 1: A challenging thought to end on.
• 12:56
  Speaker 1: If you enjoyed this deep dive, remember new ones drop every two weeks.
• 13:00
  Speaker 1: You can also check out our other editions covering ICT and tech, artificial intelligence, cloud, sustainability and green ICT, defense tech and health tech.
• 13:10
  Speaker 1: Thanks for joining us and make sure you subscribe so you don't miss the next deep dive.