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

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

00:00:02: This is where we cut through the digital noise, really bringing you the pulse of the product world.

00:00:07: We're drawing on the most relevant LinkedIn insights from the last couple of weeks.

00:00:11: Yep,

00:00:11: all curated.

00:00:12: And we're able to bring you these insights thanks to Thomas Allgeier and Frennus.

00:00:16: This deep dive is based on LinkedIn posts about digital products and services in calendar weeks.

00:00:21: thirty six and thirty seven

00:00:23: and just quickly furnaces a bpb market research company.

00:00:26: they help enterprises get those market customer competitive insights.

00:00:30: you know the stuff you need for customer centric and cost efficient product development.

00:00:34: exactly so our mission today is well pretty clear yeah

00:00:37: cut through the noise give you a really clear picture of what shaping digital products and services.

00:00:41: right now

00:00:42: We want to pull out the key nuggets, help you navigate what feels like a constantly shifting tech landscape.

00:00:48: With confidence and hopefully some clarity.

00:00:51: Okay,

00:00:51: let's unpack this then, this landscape.

00:00:54: It feels like it changes almost daily.

00:00:56: Doesn't it?

00:00:56: And look, if there's one massive theme, it's got to be AI, but it feels different now, not just hype.

00:01:05: No, definitely not.

00:01:06: We're seeing this shift, this fundamental transformation moving AI from just, you know, potential on paper.

00:01:12: to actual concrete practices, applied stuff in digital products.

00:01:15: What are the biggest shifts you're seeing there?

00:01:17: Well, what's really grabbing me is that it is practical now.

00:01:22: SHUDS observed this too, techniques like agents are rage, it's retrieval, augmented generation, and even fine tuning.

00:01:29: Right.

00:01:30: They're being used where they add clear, measurable value.

00:01:34: It's about, you know, real world use cases now, not just the big grand promises.

00:01:39: that really reshapes product delivery.

00:01:41: So it's less about having AI slapped on and more how you use it for actual gains.

00:01:45: Precisely.

00:01:46: In this practical side, it sounds like it's directly boosting productivity, especially for SaaS teams.

00:01:51: Oh, absolutely.

00:01:52: Simon Heiberg talked about something he called vibe coding, using tools like livable.

00:01:56: Vibe

00:01:56: coding,

00:01:57: okay.

00:01:57: Yeah, it's basically an AI-driven workflow.

00:01:59: 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.

00:02:05: Wow.

00:02:06: It's not just faster code.

00:02:07: It's changing team structures.

00:02:10: Small teams can suddenly manage much bigger product portfolios.

00:02:14: Well, that's strategically huge.

00:02:15: Huge acceleration for sauce growth, definitely.

00:02:18: Tiny teams managing large portfolios.

00:02:21: And that hints at something deeper, doesn't it?

00:02:24: How's the actual strategy for AI changing?

00:02:26: Is it still just add AI features?

00:02:29: Not anymore, really.

00:02:30: Basia Kubica pointed this out.

00:02:32: Teams are building now for probabilistic outcomes and task success, not just adding features.

00:02:38: But probabilistic

00:02:39: outcomes.

00:02:40: Yeah, it challenges those traditional SaaS playbooks we all know.

00:02:42: It's shifting from, we built it, will it work, to more like, what's the likelihood that actually solves the problem?

00:02:48: That makes a lot of sense, especially thinking about AI speeding up research.

00:02:52: James Lucas gave examples in healthcare, right?

00:02:54: AI churning through massive data sets.

00:02:57: But, and this seems key, human judgment, human oversight, still absolutely essential.

00:03:02: AI can crunch the data, sure, but it doesn't get human motivations.

00:03:06: Doesn't navigate the tricky office politics.

00:03:09: It's an assistant, a powerful one, but not a replacement.

00:03:12: And that brings up a big question for product folks.

00:03:16: What skills do AI product managers actually need now?

00:03:20: Good question.

00:03:21: Dr.

00:03:21: Bartowarski analyzed job roles.

00:03:23: He flagged five key areas.

00:03:25: AI product lifecycle management, knowing that whole cycle.

00:03:28: Strong cross-functional collaboration, always important, but maybe more so now.

00:03:33: Fluency in Gen AI and LLMs, obviously.

00:03:35: Robust data analytics skills.

00:03:38: And crucially, a real focus on AI-driven customer experience.

00:03:42: It's specialized, but it impacts everything.

00:03:45: That's a solid list, really reflects that blend of tech know-how and strategic thinking.

00:03:50: And we're seeing new collaboration patterns emerge too, aren't we?

00:03:53: Like human-centered co-creation speeding things up.

00:03:56: We are.

00:03:56: Danielle Dresselain shared her experience with How About Wrapped.

00:03:59: They used AI as a creative partner, like through the whole end-to-end process.

00:04:02: A creative

00:04:03: partner, interesting.

00:04:04: And Simon Blake described a continuous building flow with tools like lovable or replet.

00:04:09: Humans and AI actually co-creating in like real time super

00:04:12: fast, but is there a risk there?

00:04:14: Speed over substance.

00:04:15: That's the caution.

00:04:16: Yeah Thomas Lottis highlighted this.

00:04:18: You need balance, speed's great, but you have to protect quality.

00:04:22: AI should enhance outcomes, not diminish them because you rushed

00:04:25: it.

00:04:25: Otherwise, you're just building bad products faster.

00:04:27: Exactly.

00:04:28: Which is a critical point.

00:04:29: Yeah.

00:04:30: And Ashish Sinha gave a pretty stark reminder about AI initiative failure rates.

00:04:35: What was it?

00:04:35: Forty-two percent abandoned?

00:04:36: Yeah,

00:04:37: and ninety-five percent of JNI projects failing to deliver revenue growth.

00:04:41: Staggering number.

00:04:41: Why?

00:04:42: unclear objectives, scalability problems.

00:04:45: He really stressed that PMs have to shift from proof of concept thinking.

00:04:48: To

00:04:49: proof of value.

00:04:50: Yeah.

00:04:50: Absolutely.

00:04:51: That's a serious wake-up

00:04:52: call.

00:04:52: It really is.

00:04:53: So moving from AI's promise to its practical value, that takes us neatly into our next theme, product operations.

00:05:00: Yeah, products.

00:05:02: Super critical right now.

00:05:03: The focus seems to be intense clarity alignment and Definitely measurable outcomes.

00:05:08: Okay.

00:05:08: So if AI speeds things up products is like the steering wheel making sure that speed is going in the right direction.

00:05:13: analogy

00:05:14: What is this clarity and alignment actually look like on the ground?

00:05:17: Well, John Jay fuchs talked about leaders really pushing to turn ambiguity into clarity Using intentional prescriptive actions in product ops

00:05:25: meaning

00:05:26: breaking down those vague requests, you know Make it better doesn't cut.

00:05:31: it needs to be clear, actionable steps.

00:05:34: Right, getting specific.

00:05:35: And I guess that clarity has to flow right into strategic planning too.

00:05:39: It

00:05:39: absolutely must.

00:05:40: Yeah.

00:05:40: Roman Pitchler.

00:05:41: reinforced how vital it is to really link product strategy to OKRs and KPIs.

00:05:47: That's like prerequisite number one for execution.

00:05:49: Next sense.

00:05:50: And Christy Marie Kanopka also hit on aligning product and platform roadmaps.

00:05:55: Needs a clear product operating model underpinning it all, otherwise you're just hoping things line up by chance.

00:06:00: Building on shaky ground.

00:06:02: Yeah.

00:06:02: And tracking progress.

00:06:04: And it sounds like it's not about micromanaging every little detail.

00:06:07: No, not at all.

00:06:08: Tanguy Krusen, from the Jira Product Discovery team, mentioned they prefer simple status signals, like on track, at risk, off track.

00:06:16: Forget the super granular delivery metrics.

00:06:18: The simpler approach, he said, allows for more honest commentary from the team.

00:06:22: More

00:06:22: honest talk, less gaming the numbers, I like

00:06:25: that.

00:06:25: Me too.

00:06:27: And those clear metric definitions are just fundamental.

00:06:30: Nils Stotz shared how crucial they are for good decisions and keeping teams aligned.

00:06:35: It prevents what he called chaos.

00:06:37: Right.

00:06:38: Everyone needs to speak the same data language.

00:06:40: Exactly.

00:06:41: If you don't agree on what success or engagement actually means, consistently, you're just adding confusion.

00:06:47: That's a classic problem.

00:06:49: John Kutler talked about that messy middle, didn't he?

00:06:52: Companies jumping from high-level goals straight to features, missing the connection.

00:06:57: Yeah, that gap between planning and delivery.

00:06:59: Models that bridge strategy to concrete features are key there.

00:07:03: And it seems stakeholder misalignment is still a massive risk.

00:07:06: Dan Lawyer discussed a survey.

00:07:08: It's still stalling product leaders' success.

00:07:10: Sounds familiar.

00:07:11: Oh, definitely a persistent challenge.

00:07:13: And it raises an interesting point about influence, actually.

00:07:16: Ginny Wanger learned that encouraging more financial thinking and product work.

00:07:20: Talk and mutt.

00:07:20: Yeah, basically, connecting features to revenue or cost savings.

00:07:24: It helps secure funding, gain strategic influence.

00:07:27: You guys speak the language of leadership, show that tangible impact.

00:07:30: Makes sense.

00:07:31: And roles are getting clearer, too.

00:07:33: Prasoon Sarkar looked at the producer role in digital delivery.

00:07:36: Right, clarifying that

00:07:38: role

00:07:38: to cut down on confusion, smooth out handoffs, defining who owns what.

00:07:43: That sounds helpful.

00:07:44: It is.

00:07:45: But a little cautionary note from Andre Rodin.

00:07:48: Not every operational issue needs a product feature as a solution.

00:07:52: Ah, right.

00:07:53: Don't just build a button for everything.

00:07:55: Exactly.

00:07:55: Sometimes a process change or something non-product is way faster and cheaper.

00:08:00: Don't over engineer the fix.

00:08:01: Great practical point.

00:08:03: OK, so.

00:08:04: We're building faster with AI.

00:08:06: We're clarifying operations.

00:08:08: How do teams make sure they're building the right thing?

00:08:10: Solving real customer problems.

00:08:13: Which brings us perfectly to customer discovery and product practice.

00:08:16: The heart of it, really.

00:08:17: Absolutely.

00:08:18: And Tim Herbie had a great way to frame evidence-based discovery.

00:08:22: He called it investment protection.

00:08:23: Investment protection, I like that.

00:08:25: Yeah, apparently it really resonates with engineering teams to get risk management.

00:08:29: And Igor Volf shared this powerful case study.

00:08:32: Proper discovery literally saved a company from failing because they actually targeted the right problem up front instead of just chasing assumptions.

00:08:41: That's compelling, makes discovery a clear business need, not just a nice to have.

00:08:48: And Seth Cronin had a strong take on consultants giving away discovery work for free.

00:08:53: Oh yeah, quite provocative.

00:08:54: Yeah.

00:08:55: 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.

00:09:03: Controversial maybe, but it definitely highlights the value of that deep customer understanding.

00:09:08: For

00:09:08: sure.

00:09:09: But the reality is, time is still the biggest barrier for PMs doing discovery.

00:09:13: David Pereira highlighted

00:09:14: that.

00:09:14: Always the challenge.

00:09:15: Which

00:09:15: is why we're seeing lighter, faster methods pop up, often boosted by AI.

00:09:20: Ola Kapilova noted, AI can streamline things, turn messy notes into clear insights, speed up validation.

00:09:26: Yeah, and Evan Ravenstiel mentioned AI helping with assumptions modeling too, finding shortcuts without, you know, cutting critical corners.

00:09:34: Exactly.

00:09:35: It's about efficiency without losing rigor.

00:09:37: And this all feeds into driving better user outcomes, right?

00:09:41: Rika Barath discussed using behavioral economics and product design.

00:09:45: Yeah,

00:09:45: applying principles like the decoy effect, loss aversion, the Ikea effect.

00:09:49: Sounds

00:09:49: powerful, maybe a bit manipulative.

00:09:52: Well, the emphasis was strongly on ethical guidance.

00:09:54: It's about nudging users towards meaningful outcomes, not tricking them.

00:09:58: A fine line, but an important one.

00:10:00: Okay,

00:10:00: ethical nudging, got it.

00:10:01: But there's still this pressure, isn't there?

00:10:03: The MVP pressure, the half-baked work.

00:10:05: Oh, it's a huge anti-pattern.

00:10:07: Dr.

00:10:07: Bartowarski talked about this.

00:10:08: PMs often abandon successful MVPs because stakeholders push for the next shiny thing, the new risky bet.

00:10:15: Leading to tech debt and frustrated teams, I bet.

00:10:18: Exactly.

00:10:19: His advice.

00:10:20: Design MVPs that actually meet a quality bar.

00:10:23: Plan for the iterations.

00:10:24: And, crucially, educate stakeholders on the real cost of constantly shifting focus.

00:10:30: That education piece sounds key.

00:10:31: It feels like a constant battle.

00:10:33: It can be.

00:10:34: Which is why teams are doubling down on collaboration, clear purpose.

00:10:38: Mirko Seifert stressed product development with love.

00:10:41: A clear why that's focused squarely on the user.

00:10:45: Love and purpose.

00:10:46: Keeps you grounded.

00:10:47: It

00:10:47: does.

00:10:48: And it all circles back to balancing velocity and rigor for results that actually last.

00:10:53: Like Ishii Sensei and Melissa Perry pointed out, success is building the right AI product, not just building an AI product correctly.

00:11:00: Building perfectly something nobody needs is Still a failure.

00:11:04: Precisely.

00:11:04: All right, let's shift news a bit.

00:11:06: We've talked themes.

00:11:07: Now let's hit some specific highlights, launches, and moves.

00:11:10: we saw buzzing on LinkedIn.

00:11:12: What caught your eye?

00:11:13: Okay,

00:11:13: first up, Alibaba.

00:11:15: They introduced something called Accio Agent.

00:11:17: Matt Village described it basically an AI tool set to revolutionize product research and market validation.

00:11:23: Revolutionize.

00:11:24: How so?

00:11:25: Well, it has AI product research for supplier recommendations, smart product discovery to find potentially winning products, even visual product search using images.

00:11:34: It's like a whole suite.

00:11:35: Wow,

00:11:35: that sounds like you could seriously lower the barrier for entrepreneurs everywhere.

00:11:39: Could be a game changer.

00:11:39: Then there's free play.

00:11:41: They rolled out automated prompt optimization.

00:11:44: Automated

00:11:44: prompt optimization.

00:11:45: What does that help with?

00:11:47: Ian Karen's explained it helps teams improve their AI models without needing manual reengineering.

00:11:51: So you don't get stuck on older models when better ones come along.

00:11:55: It's about continuous improvement, but easier.

00:11:58: Keeping things fresh without all the heavy lifting.

00:12:00: Nice.

00:12:01: And talking about fresh data.

00:12:02: Google DeepMind released URL context.

00:12:05: This feature lets products pull live data straight from URLs, webpages, PDFs, images.

00:12:10: Live data directly.

00:12:11: Yeah.

00:12:12: Nicholas A. Doerr pointed out this means integrating new data sources can happen in hours, not sprints.

00:12:18: Think about that.

00:12:19: For validation cycles, real-time responsiveness, huge implications.

00:12:23: Hours, not sprints.

00:12:24: That's incredibly fast.

00:12:25: A massive boost for agility.

00:12:27: For sure.

00:12:28: And Amplitude's product benchmark report also got a lot of buzz.

00:12:31: Right.

00:12:31: I saw that.

00:12:32: What's the key takeaway there?

00:12:34: You do me on the almond.

00:12:34: Detailed.

00:12:35: It gives actual insights on product to marketing metrics.

00:12:39: Acquisition, activation, engagement, retention, monetization, all that good stuff.

00:12:44: Plus strategies for growth and stickiness.

00:12:47: It's basically data to see where you stand and where to focus.

00:12:50: Super useful for companies trying to benchmark themselves.

00:12:52: Definitely.

00:12:53: And one last thing, the importance of multi-LLM capability.

00:12:57: meaning not relying on just one AI model provider.

00:13:00: Exactly.

00:13:01: Seth Merrick observed Microsoft making a strategic shift using Anthropic instead of OpenAI for some Office, three sixty-five features.

00:13:09: Interesting move.

00:13:10: It

00:13:10: really highlights mitigating vendor lock-in and, you know, choosing the best model for the specific job.

00:13:15: It's not about loyalty to one AI, it's about optimizing.

00:13:18: Smart strategy.

00:13:19: Okay, so wrapping this all up, we've covered a lot of ground.

00:13:21: What's the big takeaway for you, our listener?

00:13:24: Well, it seems clear AI is rapidly changing how we build digital products.

00:13:28: Faster, more efficient processes, that's undeniable.

00:13:31: But the overwhelming message woven through all these insights is that the fundamentals haven't changed.

00:13:39: Understanding human problems, ensuring quality, real collaboration, ethical oversight.

00:13:44: These are more vital than ever.

00:13:46: So the tools evolve, maybe dramatically, but the core essence of great product development.

00:13:51: It's still deeply human-centered.

00:13:53: It's about building a better future, not just building things faster.

00:13:56: Couldn't agree more.

00:13:57: And if you enjoyed this deep dive, remember, new ones drop every two weeks.

00:14:01: You can also check out our other editions, ICT in Tech, Artificial Intelligence, Cloud, and Sustainability in Green ICT.

00:14:08: Thank you for joining us on the deep dive.

00:14:10: Make sure to subscribe so you don't miss our next exploration into what's shaping your world.