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Best of LinkedIn: Digital Products & Services CW 36/ 37 · Best of LinkedIn: Digital Transformation & Tech

• 0:00
  Speaker 0: Welcome to the deep dive.
• 0:02
  Speaker 0: This is where we cut through the digital noise, really bringing you the pulse of the product world.
• 0:07
  Speaker 0: We're drawing on the most relevant LinkedIn insights from the last couple of weeks.
• 0:11
  Speaker 0: Yep,
• 0:11
  Speaker 1: all curated.
• 0:12
  Speaker 0: And we're able to bring you these insights thanks to Thomas Allgeier and Frennus.
• 0:16
  Speaker 0: This deep dive is based on LinkedIn posts about digital products and services in calendar weeks.
• 0:21
  Speaker 0: thirty six and thirty seven
• 0:23
  Speaker 1: and just quickly furnaces a bpb market research company.
• 0:26
  Speaker 1: they help enterprises get those market customer competitive insights.
• 0:30
  Speaker 1: you know the stuff you need for customer centric and cost efficient product development.
• 0:34
  Speaker 0: exactly so our mission today is well pretty clear yeah
• 0:37
  Speaker 1: cut through the noise give you a really clear picture of what shaping digital products and services.
• 0:41
  Speaker 1: right now
• 0:42
  Speaker 0: We want to pull out the key nuggets, help you navigate what feels like a constantly shifting tech landscape.
• 0:48
  Speaker 1: With confidence and hopefully some clarity.
• 0:51
  Speaker 1: Okay,
• 0:51
  Speaker 0: let's unpack this then, this landscape.
• 0:54
  Speaker 0: It feels like it changes almost daily.
• 0:56
  Speaker 1: Doesn't it?
• 0:56
  Speaker 0: And look, if there's one massive theme, it's got to be AI, but it feels different now, not just hype.
• 1:05
  Speaker 1: No, definitely not.
• 1:06
  Speaker 1: We're seeing this shift, this fundamental transformation moving AI from just, you know, potential on paper.
• 1:12
  Speaker 0: to actual concrete practices, applied stuff in digital products.
• 1:15
  Speaker 0: What are the biggest shifts you're seeing there?
• 1:17
  Speaker 1: Well, what's really grabbing me is that it is practical now.
• 1:22
  Speaker 1: SHUDS observed this too, techniques like agents are rage, it's retrieval, augmented generation, and even fine tuning.
• 1:29
  Speaker 1: Right.
• 1:30
  Speaker 1: They're being used where they add clear, measurable value.
• 1:34
  Speaker 1: It's about, you know, real world use cases now, not just the big grand promises.
• 1:39
  Speaker 1: that really reshapes product delivery.
• 1:41
  Speaker 0: So it's less about having AI slapped on and more how you use it for actual gains.
• 1:45
  Speaker 0: Precisely.
• 1:46
  Speaker 0: In this practical side, it sounds like it's directly boosting productivity, especially for SaaS teams.
• 1:51
  Speaker 1: Oh, absolutely.
• 1:52
  Speaker 1: Simon Heiberg talked about something he called vibe coding, using tools like livable.
• 1:56
  Speaker 1: Vibe
• 1:56
  Speaker 0: coding,
• 1:57
  Speaker 1: okay.
• 1:57
  Speaker 1: Yeah, it's basically an AI-driven workflow.
• 1:59
  Speaker 1: The AI does a lot of the heavy lifting with the code, leaving the humans to focus on maybe like, twenty-five percent of the task.
• 2:05
  Speaker 1: Wow.
• 2:06
  Speaker 1: It's not just faster code.
• 2:07
  Speaker 1: It's changing team structures.
• 2:10
  Speaker 1: Small teams can suddenly manage much bigger product portfolios.
• 2:14
  Speaker 1: Well, that's strategically huge.
• 2:15
  Speaker 0: Huge acceleration for sauce growth, definitely.
• 2:18
  Speaker 0: Tiny teams managing large portfolios.
• 2:21
  Speaker 0: And that hints at something deeper, doesn't it?
• 2:24
  Speaker 0: How's the actual strategy for AI changing?
• 2:26
  Speaker 0: Is it still just add AI features?
• 2:29
  Speaker 1: Not anymore, really.
• 2:30
  Speaker 1: Basia Kubica pointed this out.
• 2:32
  Speaker 1: Teams are building now for probabilistic outcomes and task success, not just adding features.
• 2:38
  Speaker 1: But probabilistic
• 2:39
  Speaker 0: outcomes.
• 2:40
  Speaker 1: Yeah, it challenges those traditional SaaS playbooks we all know.
• 2:42
  Speaker 1: It's shifting from, we built it, will it work, to more like, what's the likelihood that actually solves the problem?
• 2:48
  Speaker 0: That makes a lot of sense, especially thinking about AI speeding up research.
• 2:52
  Speaker 0: James Lucas gave examples in healthcare, right?
• 2:54
  Speaker 0: AI churning through massive data sets.
• 2:57
  Speaker 0: But, and this seems key, human judgment, human oversight, still absolutely essential.
• 3:02
  Speaker 0: AI can crunch the data, sure, but it doesn't get human motivations.
• 3:06
  Speaker 0: Doesn't navigate the tricky office politics.
• 3:09
  Speaker 0: It's an assistant, a powerful one, but not a replacement.
• 3:12
  Speaker 1: And that brings up a big question for product folks.
• 3:16
  Speaker 1: What skills do AI product managers actually need now?
• 3:20
  Speaker 1: Good question.
• 3:21
  Speaker 1: Dr.
• 3:21
  Speaker 1: Bartowarski analyzed job roles.
• 3:23
  Speaker 1: He flagged five key areas.
• 3:25
  Speaker 1: AI product lifecycle management, knowing that whole cycle.
• 3:28
  Speaker 1: Strong cross-functional collaboration, always important, but maybe more so now.
• 3:33
  Speaker 1: Fluency in Gen AI and LLMs, obviously.
• 3:35
  Speaker 1: Robust data analytics skills.
• 3:38
  Speaker 1: And crucially, a real focus on AI-driven customer experience.
• 3:42
  Speaker 1: It's specialized, but it impacts everything.
• 3:45
  Speaker 0: That's a solid list, really reflects that blend of tech know-how and strategic thinking.
• 3:50
  Speaker 0: And we're seeing new collaboration patterns emerge too, aren't we?
• 3:53
  Speaker 0: Like human-centered co-creation speeding things up.
• 3:56
  Speaker 1: We are.
• 3:56
  Speaker 1: Danielle Dresselain shared her experience with How About Wrapped.
• 3:59
  Speaker 1: They used AI as a creative partner, like through the whole end-to-end process.
• 4:02
  Speaker 1: A creative
• 4:03
  Speaker 0: partner, interesting.
• 4:04
  Speaker 1: And Simon Blake described a continuous building flow with tools like lovable or replet.
• 4:09
  Speaker 1: Humans and AI actually co-creating in like real time super
• 4:12
  Speaker 0: fast, but is there a risk there?
• 4:14
  Speaker 0: Speed over substance.
• 4:15
  Speaker 1: That's the caution.
• 4:16
  Speaker 1: Yeah Thomas Lottis highlighted this.
• 4:18
  Speaker 1: You need balance, speed's great, but you have to protect quality.
• 4:22
  Speaker 1: AI should enhance outcomes, not diminish them because you rushed
• 4:25
  Speaker 0: it.
• 4:25
  Speaker 0: Otherwise, you're just building bad products faster.
• 4:27
  Speaker 1: Exactly.
• 4:28
  Speaker 0: Which is a critical point.
• 4:29
  Speaker 0: Yeah.
• 4:30
  Speaker 0: And Ashish Sinha gave a pretty stark reminder about AI initiative failure rates.
• 4:35
  Speaker 0: What was it?
• 4:35
  Speaker 0: Forty-two percent abandoned?
• 4:36
  Speaker 0: Yeah,
• 4:37
  Speaker 1: and ninety-five percent of JNI projects failing to deliver revenue growth.
• 4:41
  Speaker 1: Staggering number.
• 4:41
  Speaker 1: Why?
• 4:42
  Speaker 0: unclear objectives, scalability problems.
• 4:45
  Speaker 0: He really stressed that PMs have to shift from proof of concept thinking.
• 4:48
  Speaker 0: To
• 4:49
  Speaker 1: proof of value.
• 4:50
  Speaker 1: Yeah.
• 4:50
  Speaker 1: Absolutely.
• 4:51
  Speaker 1: That's a serious wake-up
• 4:52
  Speaker 0: call.
• 4:52
  Speaker 0: It really is.
• 4:53
  Speaker 0: So moving from AI's promise to its practical value, that takes us neatly into our next theme, product operations.
• 5:00
  Speaker 1: Yeah, products.
• 5:02
  Speaker 1: Super critical right now.
• 5:03
  Speaker 1: The focus seems to be intense clarity alignment and Definitely measurable outcomes.
• 5:08
  Speaker 0: Okay.
• 5:08
  Speaker 0: So if AI speeds things up products is like the steering wheel making sure that speed is going in the right direction.
• 5:13
  Speaker 1: analogy
• 5:14
  Speaker 0: What is this clarity and alignment actually look like on the ground?
• 5:17
  Speaker 1: Well, John Jay fuchs talked about leaders really pushing to turn ambiguity into clarity Using intentional prescriptive actions in product ops
• 5:25
  Speaker 0: meaning
• 5:26
  Speaker 1: breaking down those vague requests, you know Make it better doesn't cut.
• 5:31
  Speaker 1: it needs to be clear, actionable steps.
• 5:34
  Speaker 0: Right, getting specific.
• 5:35
  Speaker 0: And I guess that clarity has to flow right into strategic planning too.
• 5:39
  Speaker 0: It
• 5:39
  Speaker 1: absolutely must.
• 5:40
  Speaker 1: Yeah.
• 5:40
  Speaker 1: Roman Pitchler.
• 5:41
  Speaker 1: reinforced how vital it is to really link product strategy to OKRs and KPIs.
• 5:47
  Speaker 1: That's like prerequisite number one for execution.
• 5:49
  Speaker 0: Next sense.
• 5:50
  Speaker 1: And Christy Marie Kanopka also hit on aligning product and platform roadmaps.
• 5:55
  Speaker 1: Needs a clear product operating model underpinning it all, otherwise you're just hoping things line up by chance.
• 6:00
  Speaker 0: Building on shaky ground.
• 6:02
  Speaker 0: Yeah.
• 6:02
  Speaker 0: And tracking progress.
• 6:04
  Speaker 0: And it sounds like it's not about micromanaging every little detail.
• 6:07
  Speaker 1: No, not at all.
• 6:08
  Speaker 1: Tanguy Krusen, from the Jira Product Discovery team, mentioned they prefer simple status signals, like on track, at risk, off track.
• 6:16
  Speaker 1: Forget the super granular delivery metrics.
• 6:18
  Speaker 1: The simpler approach, he said, allows for more honest commentary from the team.
• 6:22
  Speaker 1: More
• 6:22
  Speaker 0: honest talk, less gaming the numbers, I like
• 6:25
  Speaker 1: that.
• 6:25
  Speaker 1: Me too.
• 6:27
  Speaker 1: And those clear metric definitions are just fundamental.
• 6:30
  Speaker 1: Nils Stotz shared how crucial they are for good decisions and keeping teams aligned.
• 6:35
  Speaker 1: It prevents what he called chaos.
• 6:37
  Speaker 0: Right.
• 6:38
  Speaker 0: Everyone needs to speak the same data language.
• 6:40
  Speaker 1: Exactly.
• 6:41
  Speaker 1: If you don't agree on what success or engagement actually means, consistently, you're just adding confusion.
• 6:47
  Speaker 0: That's a classic problem.
• 6:49
  Speaker 0: John Kutler talked about that messy middle, didn't he?
• 6:52
  Speaker 0: Companies jumping from high-level goals straight to features, missing the connection.
• 6:57
  Speaker 1: Yeah, that gap between planning and delivery.
• 6:59
  Speaker 1: Models that bridge strategy to concrete features are key there.
• 7:03
  Speaker 0: And it seems stakeholder misalignment is still a massive risk.
• 7:06
  Speaker 0: Dan Lawyer discussed a survey.
• 7:08
  Speaker 0: It's still stalling product leaders' success.
• 7:10
  Speaker 0: Sounds familiar.
• 7:11
  Speaker 1: Oh, definitely a persistent challenge.
• 7:13
  Speaker 1: And it raises an interesting point about influence, actually.
• 7:16
  Speaker 1: Ginny Wanger learned that encouraging more financial thinking and product work.
• 7:20
  Speaker 0: Talk and mutt.
• 7:20
  Speaker 1: Yeah, basically, connecting features to revenue or cost savings.
• 7:24
  Speaker 1: It helps secure funding, gain strategic influence.
• 7:27
  Speaker 1: You guys speak the language of leadership, show that tangible impact.
• 7:30
  Speaker 0: Makes sense.
• 7:31
  Speaker 0: And roles are getting clearer, too.
• 7:33
  Speaker 0: Prasoon Sarkar looked at the producer role in digital delivery.
• 7:36
  Speaker 1: Right, clarifying that
• 7:38
  Speaker 0: role
• 7:38
  Speaker 1: to cut down on confusion, smooth out handoffs, defining who owns what.
• 7:43
  Speaker 0: That sounds helpful.
• 7:44
  Speaker 1: It is.
• 7:45
  Speaker 1: But a little cautionary note from Andre Rodin.
• 7:48
  Speaker 1: Not every operational issue needs a product feature as a solution.
• 7:52
  Speaker 0: Ah, right.
• 7:53
  Speaker 0: Don't just build a button for everything.
• 7:55
  Speaker 1: Exactly.
• 7:55
  Speaker 1: Sometimes a process change or something non-product is way faster and cheaper.
• 8:00
  Speaker 1: Don't over engineer the fix.
• 8:01
  Speaker 0: Great practical point.
• 8:03
  Speaker 0: OK, so.
• 8:04
  Speaker 0: We're building faster with AI.
• 8:06
  Speaker 0: We're clarifying operations.
• 8:08
  Speaker 0: How do teams make sure they're building the right thing?
• 8:10
  Speaker 0: Solving real customer problems.
• 8:13
  Speaker 1: Which brings us perfectly to customer discovery and product practice.
• 8:16
  Speaker 0: The heart of it, really.
• 8:17
  Speaker 1: Absolutely.
• 8:18
  Speaker 1: And Tim Herbie had a great way to frame evidence-based discovery.
• 8:22
  Speaker 1: He called it investment protection.
• 8:23
  Speaker 0: Investment protection, I like that.
• 8:25
  Speaker 1: Yeah, apparently it really resonates with engineering teams to get risk management.
• 8:29
  Speaker 1: And Igor Volf shared this powerful case study.
• 8:32
  Speaker 1: Proper discovery literally saved a company from failing because they actually targeted the right problem up front instead of just chasing assumptions.
• 8:41
  Speaker 0: That's compelling, makes discovery a clear business need, not just a nice to have.
• 8:48
  Speaker 0: And Seth Cronin had a strong take on consultants giving away discovery work for free.
• 8:53
  Speaker 1: Oh yeah, quite provocative.
• 8:54
  Speaker 1: Yeah.
• 8:55
  Speaker 1: He argued discoveries of the product now and should be charged for accordingly, especially since, as he put it, engineering is commodity work now with AI tools.
• 9:03
  Speaker 0: Controversial maybe, but it definitely highlights the value of that deep customer understanding.
• 9:08
  Speaker 0: For
• 9:08
  Speaker 1: sure.
• 9:09
  Speaker 1: But the reality is, time is still the biggest barrier for PMs doing discovery.
• 9:13
  Speaker 1: David Pereira highlighted
• 9:14
  Speaker 0: that.
• 9:14
  Speaker 0: Always the challenge.
• 9:15
  Speaker 0: Which
• 9:15
  Speaker 1: is why we're seeing lighter, faster methods pop up, often boosted by AI.
• 9:20
  Speaker 1: Ola Kapilova noted, AI can streamline things, turn messy notes into clear insights, speed up validation.
• 9:26
  Speaker 0: Yeah, and Evan Ravenstiel mentioned AI helping with assumptions modeling too, finding shortcuts without, you know, cutting critical corners.
• 9:34
  Speaker 1: Exactly.
• 9:35
  Speaker 1: It's about efficiency without losing rigor.
• 9:37
  Speaker 0: And this all feeds into driving better user outcomes, right?
• 9:41
  Speaker 0: Rika Barath discussed using behavioral economics and product design.
• 9:45
  Speaker 0: Yeah,
• 9:45
  Speaker 1: applying principles like the decoy effect, loss aversion, the Ikea effect.
• 9:49
  Speaker 1: Sounds
• 9:49
  Speaker 0: powerful, maybe a bit manipulative.
• 9:52
  Speaker 1: Well, the emphasis was strongly on ethical guidance.
• 9:54
  Speaker 1: It's about nudging users towards meaningful outcomes, not tricking them.
• 9:58
  Speaker 1: A fine line, but an important one.
• 10:00
  Speaker 1: Okay,
• 10:00
  Speaker 0: ethical nudging, got it.
• 10:01
  Speaker 0: But there's still this pressure, isn't there?
• 10:03
  Speaker 0: The MVP pressure, the half-baked work.
• 10:05
  Speaker 1: Oh, it's a huge anti-pattern.
• 10:07
  Speaker 1: Dr.
• 10:07
  Speaker 1: Bartowarski talked about this.
• 10:08
  Speaker 1: PMs often abandon successful MVPs because stakeholders push for the next shiny thing, the new risky bet.
• 10:15
  Speaker 0: Leading to tech debt and frustrated teams, I bet.
• 10:18
  Speaker 1: Exactly.
• 10:19
  Speaker 1: His advice.
• 10:20
  Speaker 1: Design MVPs that actually meet a quality bar.
• 10:23
  Speaker 1: Plan for the iterations.
• 10:24
  Speaker 1: And, crucially, educate stakeholders on the real cost of constantly shifting focus.
• 10:30
  Speaker 0: That education piece sounds key.
• 10:31
  Speaker 0: It feels like a constant battle.
• 10:33
  Speaker 1: It can be.
• 10:34
  Speaker 1: Which is why teams are doubling down on collaboration, clear purpose.
• 10:38
  Speaker 1: Mirko Seifert stressed product development with love.
• 10:41
  Speaker 1: A clear why that's focused squarely on the user.
• 10:45
  Speaker 0: Love and purpose.
• 10:46
  Speaker 0: Keeps you grounded.
• 10:47
  Speaker 0: It
• 10:47
  Speaker 1: does.
• 10:48
  Speaker 1: And it all circles back to balancing velocity and rigor for results that actually last.
• 10:53
  Speaker 1: Like Ishii Sensei and Melissa Perry pointed out, success is building the right AI product, not just building an AI product correctly.
• 11:00
  Speaker 0: Building perfectly something nobody needs is Still a failure.
• 11:04
  Speaker 1: Precisely.
• 11:04
  Speaker 0: All right, let's shift news a bit.
• 11:06
  Speaker 0: We've talked themes.
• 11:07
  Speaker 0: Now let's hit some specific highlights, launches, and moves.
• 11:10
  Speaker 0: we saw buzzing on LinkedIn.
• 11:12
  Speaker 0: What caught your eye?
• 11:13
  Speaker 0: Okay,
• 11:13
  Speaker 1: first up, Alibaba.
• 11:15
  Speaker 1: They introduced something called Accio Agent.
• 11:17
  Speaker 1: Matt Village described it basically an AI tool set to revolutionize product research and market validation.
• 11:23
  Speaker 0: Revolutionize.
• 11:24
  Speaker 0: How so?
• 11:25
  Speaker 1: Well, it has AI product research for supplier recommendations, smart product discovery to find potentially winning products, even visual product search using images.
• 11:34
  Speaker 1: It's like a whole suite.
• 11:35
  Speaker 1: Wow,
• 11:35
  Speaker 0: that sounds like you could seriously lower the barrier for entrepreneurs everywhere.
• 11:39
  Speaker 1: Could be a game changer.
• 11:39
  Speaker 1: Then there's free play.
• 11:41
  Speaker 1: They rolled out automated prompt optimization.
• 11:44
  Speaker 1: Automated
• 11:44
  Speaker 0: prompt optimization.
• 11:45
  Speaker 0: What does that help with?
• 11:47
  Speaker 1: Ian Karen's explained it helps teams improve their AI models without needing manual reengineering.
• 11:51
  Speaker 1: So you don't get stuck on older models when better ones come along.
• 11:55
  Speaker 1: It's about continuous improvement, but easier.
• 11:58
  Speaker 0: Keeping things fresh without all the heavy lifting.
• 12:00
  Speaker 0: Nice.
• 12:01
  Speaker 1: And talking about fresh data.
• 12:02
  Speaker 1: Google DeepMind released URL context.
• 12:05
  Speaker 1: This feature lets products pull live data straight from URLs, webpages, PDFs, images.
• 12:10
  Speaker 0: Live data directly.
• 12:11
  Speaker 0: Yeah.
• 12:12
  Speaker 1: Nicholas A. Doerr pointed out this means integrating new data sources can happen in hours, not sprints.
• 12:18
  Speaker 1: Think about that.
• 12:19
  Speaker 1: For validation cycles, real-time responsiveness, huge implications.
• 12:23
  Speaker 0: Hours, not sprints.
• 12:24
  Speaker 0: That's incredibly fast.
• 12:25
  Speaker 0: A massive boost for agility.
• 12:27
  Speaker 0: For sure.
• 12:28
  Speaker 1: And Amplitude's product benchmark report also got a lot of buzz.
• 12:31
  Speaker 0: Right.
• 12:31
  Speaker 0: I saw that.
• 12:32
  Speaker 0: What's the key takeaway there?
• 12:34
  Speaker 1: You do me on the almond.
• 12:34
  Speaker 1: Detailed.
• 12:35
  Speaker 1: It gives actual insights on product to marketing metrics.
• 12:39
  Speaker 1: Acquisition, activation, engagement, retention, monetization, all that good stuff.
• 12:44
  Speaker 1: Plus strategies for growth and stickiness.
• 12:47
  Speaker 1: It's basically data to see where you stand and where to focus.
• 12:50
  Speaker 0: Super useful for companies trying to benchmark themselves.
• 12:52
  Speaker 0: Definitely.
• 12:53
  Speaker 1: And one last thing, the importance of multi-LLM capability.
• 12:57
  Speaker 0: meaning not relying on just one AI model provider.
• 13:00
  Speaker 1: Exactly.
• 13:01
  Speaker 1: Seth Merrick observed Microsoft making a strategic shift using Anthropic instead of OpenAI for some Office, three sixty-five features.
• 13:09
  Speaker 0: Interesting move.
• 13:10
  Speaker 0: It
• 13:10
  Speaker 1: really highlights mitigating vendor lock-in and, you know, choosing the best model for the specific job.
• 13:15
  Speaker 1: It's not about loyalty to one AI, it's about optimizing.
• 13:18
  Speaker 0: Smart strategy.
• 13:19
  Speaker 0: Okay, so wrapping this all up, we've covered a lot of ground.
• 13:21
  Speaker 0: What's the big takeaway for you, our listener?
• 13:24
  Speaker 1: Well, it seems clear AI is rapidly changing how we build digital products.
• 13:28
  Speaker 1: Faster, more efficient processes, that's undeniable.
• 13:31
  Speaker 1: But the overwhelming message woven through all these insights is that the fundamentals haven't changed.
• 13:39
  Speaker 1: Understanding human problems, ensuring quality, real collaboration, ethical oversight.
• 13:44
  Speaker 1: These are more vital than ever.
• 13:46
  Speaker 0: So the tools evolve, maybe dramatically, but the core essence of great product development.
• 13:51
  Speaker 0: It's still deeply human-centered.
• 13:53
  Speaker 0: It's about building a better future, not just building things faster.
• 13:56
  Speaker 1: Couldn't agree more.
• 13:57
  Speaker 1: And if you enjoyed this deep dive, remember, new ones drop every two weeks.
• 14:01
  Speaker 1: You can also check out our other editions, ICT in Tech, Artificial Intelligence, Cloud, and Sustainability in Green ICT.
• 14:08
  Speaker 0: Thank you for joining us on the deep dive.
• 14:10
  Speaker 0: Make sure to subscribe so you don't miss our next exploration into what's shaping your world.