Best of LinkedIn: Digital Products & Services CW 38/ 39

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 thirty-eight and thirty-nine.

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.

00:00:19: Welcome back to the deep dive.

00:00:22: So today we're digging into the the sharpest insights we've seen from the digital products and services world over the last couple weeks.

00:00:28: Yeah, that's right.

00:00:28: We're looking at LinkedIn trends mainly covering strategy the whole AI impact piece and Execution.

00:00:36: getting things done right

00:00:37: exactly from fundamental strategy shifts to AI is growing role and that you know that nitty gritty of execution excellence

00:00:44: and it really feels like from looking at the sources.

00:00:46: the industry is kind of going through this aggressive self-correction moment.

00:00:51: Self-correction?

00:00:51: How so?

00:00:52: Well, the general vibe is all about getting back to discipline strategy, being really pragmatic about AI adoption, not just hype, and seriously tackling that internal friction that slows everything down.

00:01:01: Uh, the organizational sludge.

00:01:03: Precisely.

00:01:04: The big message seems to be leaders are saying, look, we need measurable customer value, real outcomes.

00:01:10: Rather than just, you know, being busy, activity isn't the goal anymore.

00:01:14: Okay,

00:01:14: that makes sense.

00:01:15: Let's unpack that, then, this precision mandate, starting with Themai, reinforcing product strategy and leadership.

00:01:23: It feels almost like back to basics.

00:01:26: Totally.

00:01:26: It's less about flashy new things and more foundational.

00:01:29: Folis Adani really nailed this core idea.

00:01:35: strategy has got to be anchored in solving audience

00:01:37: problems.

00:01:38: Real problems.

00:01:38: Not just hitting internal revenue targets.

00:01:40: Exactly.

00:01:41: Because if you just chase income goals, you end up building stuff people don't actually need or want.

00:01:46: She points out pretty bluntly that products built for existing undeniable pain points.

00:01:51: Those are the ones that actually stick around.

00:01:53: Makes perfect sense.

00:01:53: But okay, say you're a new product leader coming in, how do you get that strategy operational like quickly?

00:01:58: Right.

00:01:59: You need traction fast.

00:02:00: Ed Biden offered this interesting shortcut.

00:02:03: He calls the snap strategy method.

00:02:04: Snap strategy.

00:02:05: Yeah.

00:02:06: It's basically built around answering seven key questions.

00:02:09: Things like objective, users, superpowers, vision, pillars, impact, and then the roadmap.

00:02:15: The idea is to structure all your initial knowledge.

00:02:19: in maybe an hour.

00:02:19: An hour?

00:02:20: That sounds... Ambitious.

00:02:22: It does.

00:02:23: Right.

00:02:23: But I think the point isn't perfection for a straff.

00:02:25: It's about getting something down fast.

00:02:28: A single shared source of truth to get everyone aligned immediately.

00:02:32: Instead

00:02:32: of getting bogged down for months in analysis.

00:02:34: paralysis.

00:02:35: Exactly.

00:02:36: Force clarity right away.

00:02:37: Yeah.

00:02:37: Because, you know, that strategic alignment often gets derailed by just internal chaos.

00:02:42: And Jen Swanson had a great point here.

00:02:43: Oh,

00:02:43: yeah.

00:02:44: She highlighted that a lot of organizations struggling with speed, they think it's tech debt, but often they're drowning in organizational debt.

00:02:50: Organizational debt I like that term you mean like the mess of bad communication pathways and endless handoffs.

00:02:56: spot on The handoff hell as she put it between tech teams and the business side.

00:03:02: if things feel sticky and slow the fix isn't just better project management tools

00:03:07: It's redesigning how people actually work together.

00:03:10: Yeah, you need to fundamentally redesign how those groups collaborate Eliminate the bottlenecks the bureaucracy

00:03:17: and that friction that organizational debt.

00:03:19: It surely puts a cap on individual careers too, right?

00:03:22: Absolutely.

00:03:23: Elena Leonova talked about this ceiling.

00:03:26: PMs hit when they want to step up to VP or CPO.

00:03:29: You can't just rely on your execution chops anymore.

00:03:32: What's the shift then?

00:03:33: The toolkit changes completely.

00:03:35: It becomes about negotiation, influence, really understanding the financials and communicating effectively with executives.

00:03:42: And

00:03:42: Bernadette von Victor and really doubled down on that financial literacy point.

00:03:46: Oh, massively.

00:03:47: It's not optional anymore.

00:03:49: It's mandatory.

00:03:50: You have to be able to build a compelling business case, show the ROI.

00:03:53: If you can't talk money with the exec team, you're not moving up.

00:03:56: Simple as that.

00:03:57: That really sets us up nicely for the next big area everyone's talking about.

00:04:01: Theme two, AI and the evolving product manager role.

00:04:04: There's a bit of gold rush happening, isn't there?

00:04:06: Definitely feels like it.

00:04:08: Akasha Gupta laid out the numbers.

00:04:10: AIPM is apparently the fastest growing, highest paying subsector in product management.

00:04:15: We're talking salaries upwards of three hundred K plus in some cases.

00:04:18: Wow.

00:04:19: OK.

00:04:20: But what does that actually mean for skills?

00:04:22: Is it just about knowing AI basics?

00:04:25: Not quite.

00:04:26: That salary bump reflects demand for really specialized knowledge.

00:04:30: Just saying I have AI experience isn't cutting it.

00:04:33: So what are they looking for?

00:04:34: It's getting quite technical.

00:04:36: Things like context engineering, AI observability and AI evils.

00:04:41: OK.

00:04:41: Hang on.

00:04:42: Let's break this down quickly.

00:04:43: Sounds a bit like jargon bingo.

00:04:45: Context engineering.

00:04:46: Ah, fair enough.

00:04:47: Context engineering is basically the skill of getting the prompts right, refining the data.

00:04:50: you feed the AI so it actually performs reliably and gives you what you need.

00:04:54: Got it.

00:04:55: And AI observability.

00:04:57: That's crucial.

00:04:57: It's about monitoring the AI model in the wild.

00:05:00: Not just is it up or down, but is its performance drifting?

00:05:03: Is it developing bias?

00:05:04: Is it degrading over time?

00:05:06: Right, keeping an eye on it post launch.

00:05:08: And AI evils.

00:05:09: That's how you systematically measure the quality and safety of what the AI is putting out.

00:05:14: Setting benchmarks, testing against them, it's often way trickier than standard software QA.

00:05:19: Okay, that clarifies the rigor needed.

00:05:22: Diego Granados also helped break down the roles, didn't he?

00:05:24: He classified different types of AI PMs.

00:05:27: Yeah, he made a useful distinction.

00:05:29: There are AI experiences, PMs, focusing on the user-facing features, and AI builder PMs, more on the underlying models or platforms.

00:05:37: And the key point.

00:05:38: The key point is companies want to see proof you've actually launched AI features.

00:05:43: Using chat GPT to write your emails, that's AI.

00:05:46: enhanced productivity maybe.

00:05:48: But it doesn't make you an AI PM in their eyes.

00:05:50: They want tangible product launches.

00:05:52: Right.

00:05:53: But this rush to build and deploy, does it come with risks?

00:05:57: Oh, absolutely.

00:05:57: Itamar Gillad raised a really important warning flag here.

00:06:00: Which is?

00:06:01: That

00:06:01: generative AI, while amazing for efficiency, could actually breed a sort of copycat mentality.

00:06:07: Interesting.

00:06:08: Because it can just churn out strategies or designs easily.

00:06:10: Is that a real risk or just, you know, fear of the new?

00:06:13: I think Gillette's concern is pretty valid.

00:06:15: The sheer ease of generating stuff might tempt teams to skip the hard critical thinking.

00:06:21: You could end up just producing slightly generic, non-differentiated outputs, almost like falling back into a lazy kind of waterfall process.

00:06:29: Just

00:06:29: taking the first thing the AI spits

00:06:31: out.

00:06:31: Kind of, yeah.

00:06:32: Yeah.

00:06:32: Prioritizing speed over deep... problem-solving.

00:06:35: So how do we balance that, keep the efficiency, but also the insight?

00:06:40: Well, Diana Stepner offered a fantastic perspective based on what they've seen at Notion.

00:06:44: Go on.

00:06:45: She suggested the secret weapon in AI product development might actually be a humanity's background.

00:06:51: Really?

00:06:51: Humanities?

00:06:52: Yeah.

00:06:53: Qualities like taste, good judgment, understanding nuance.

00:06:56: These very human things are becoming super valuable.

00:06:58: Especially for effective prompting, making sure the AI's output isn't just technically correct, but actually useful, well-designed, maybe even elegant.

00:07:06: That human judgment brings the precision we were talking about.

00:07:08: Exactly.

00:07:09: Which leads us perfectly into theme three.

00:07:11: Precision in execution and discovery.

00:07:14: Right.

00:07:14: And precision has to start with tackling assumptions and risks smartly.

00:07:18: Jeff Gothelve had a great reminder on this.

00:07:20: What was that?

00:07:21: He basically said, look.

00:07:22: All product development is built on assumptions.

00:07:24: That's not the problem.

00:07:26: The mistake is making an assumption and then waiting like six months to see if it was right.

00:07:31: Burning time and money on a maybe.

00:07:33: Exactly.

00:07:34: His advice.

00:07:35: Better day.

00:07:36: not six months.

00:07:37: Run a quick interview, sketch a paper prototype, just do something small and fast to test that core hypothesis.

00:07:43: And

00:07:43: those quick tests, those feedback loops are getting even faster now with new tools, right?

00:07:47: Yeah, Kim Sullivan and Marty Markinson highlighted how AI prototyping, even using things like Claude creatively, can seriously speed things up, like ten X faster.

00:07:58: How so?

00:07:59: By letting you stress test really specific, narrow feature interactions very quickly, you avoid spending age building out complex clothes only to realize later half of it needs to be thrown away.

00:08:09: Makes sense.

00:08:10: Get validation on the micro interactions early.

00:08:12: And this isn't just for brand new products.

00:08:14: Prototyping on existing complex products is notoriously hard.

00:08:18: True.

00:08:19: It's tough to make it look realistic.

00:08:20: But Dr.

00:08:21: Bartowarski and Simon Kibuka talked about the launch of Alloy.

00:08:25: It's an AI prototyping tool designed specifically for existing products.

00:08:29: It aims to mirror your actual products UI closely.

00:08:33: Oh,

00:08:33: that's huge.

00:08:34: Because getting internal buy-in is so much easier when stakeholders see a prototype that looks and feels like the real thing they use every day.

00:08:42: Right.

00:08:42: The conversations about feasibility, about impact, they just become much more concrete, much faster.

00:08:46: So beyond the prototyping, precision also means tackling quality and... UX debt proactively.

00:08:55: Yes.

00:08:56: Dennis Cartepi brought up the Ritz Carlton model here, interestingly.

00:08:59: The hotel.

00:08:59: How does that apply?

00:09:00: The idea is empowerment.

00:09:02: Just like Ritz Carlton empowers staff to fix guest problems on the spot, digital product companies should empower their teams with the resources, time, budget to fix UX debt right when they see it.

00:09:12: Instead of waiting for the next quarterly planning cycle and hoping it gets prioritized.

00:09:16: Exactly.

00:09:16: Trust the team closest to the user, closest to the code to make those small fixes immediately.

00:09:22: If it takes less than an hour, why wait three months?

00:09:25: That feels like a prerequisite for being truly user-centric, doesn't it?

00:09:28: Empowering the edge.

00:09:30: Absolutely.

00:09:30: Okay, let's wrap things up with our last theme.

00:09:33: Theme for metrics, scale, and new market dynamics.

00:09:36: If we're aiming for precision, we need to tie everything back to measurable results and cost.

00:09:41: Definitely.

00:09:42: Ivan Roke offered a really clear framework for this in lean product development.

00:09:48: He talked about aligning four key things.

00:09:50: Feature, function, technology, and cost.

00:09:53: Feature, function, tech, cost.

00:09:55: And he said failure usually happens when one of those gets out of whack or one group loses sight of the others like Engineers falling in love with cool tech that doesn't add function or desires pushing for features that blow up the cost without clear value.

00:10:08: You need that balance.

00:10:09: balance is key and for driving real impact with execution

00:10:12: Evan Ravensdale had some pretty direct advice almost brutal Oh, he said.

00:10:16: if your teams are struggling to make an impact enforce a singular metric that truly matters and then prioritize the big risky problems over just tinkering with small incremental stuff.

00:10:29: Go for the big swings based on that one metric.

00:10:31: Yeah.

00:10:32: He shared an example where focusing fiercely on one metric drove a fourteen percent conversion increase for a portfolio that was previously just spinning its wheels.

00:10:41: Focus creates leverage.

00:10:42: And we need that leverage because the ground is shifting under us, right?

00:10:46: Particularly with AI entering new domains.

00:10:48: Big

00:10:49: time.

00:10:49: Remi Gealing and Caitlin Robinson flagged the launch of OpenAI's Instant Checkout.

00:10:54: That's basically turning platforms like ChatGPT into potential shopping platforms.

00:10:59: Wow.

00:11:00: So, AI not just finding info, but facilitating transactions.

00:11:04: Exactly.

00:11:05: And the implication.

00:11:05: The new game is all about data density.

00:11:07: Data

00:11:08: density.

00:11:08: What does that mean practically for, say, merchants or product teams?

00:11:12: It means you need ruthlessly complete... Structured and accurate product data.

00:11:16: Material size, color, price, stock levels, everything.

00:11:19: If the AI discovery layer can't easily read and understand your product details perfectly,

00:11:24: it just won't show your product to the user.

00:11:25: Precisely.

00:11:26: Your data quality becomes a massive competitive differentiator in this new world of generative search and AI-driven commerce.

00:11:33: That's a fundamental shift, all right.

00:11:34: Requires immediate focus.

00:11:37: Okay.

00:11:38: One last point on broadening our view.

00:11:40: Andrew Constable's point about looking beyond just product innovation.

00:11:44: Right.

00:11:44: He encouraged leaders to use the classic four-piece framework product, process, position, and paradigm.

00:11:51: How does that help?

00:11:52: It reminds us that innovation isn't only about the software you ship.

00:11:56: You can find equally valuable and sometimes cheaper or faster ways to create value by improving a process, shifting your market position, or even changing the underlying business paradigm.

00:12:08: Innovation levers exist across the whole organization.

00:12:11: That's a great strategic perspective.

00:12:13: So pulling it all together, what's the macro trend here?

00:12:16: It feels like the industry is moving on from speed just for speed sake.

00:12:19: Yeah, Deep Raj put it nicely.

00:12:20: It's about speed with precision now.

00:12:22: You need both the fluency with tools like AI and that crucial strong human judgment.

00:12:27: And that human element, that judgment, that's really where we need to land this discussion, especially thinking about

00:12:32: AI.

00:12:32: How so?

00:12:33: Well, Ken Pryor made this striking observation that users are already treating AI systems like therapists, sharing really intimate... personal stuff.

00:12:43: That's concerning.

00:12:44: It is because those conversations currently lack any kind of professional privilege or protection.

00:12:50: So the final thought we want to leave you the listener with is this.

00:12:53: Given that users are having these intimate therapy-like chats with AI, what ethical guardrails, what legal frameworks do your product teams need to be proactively embedding today?

00:13:04: Right now.

00:13:05: How do you protect that user data and maintain trust when the interactions become so personal?

00:13:10: That is indeed the stakes questioned to Molover.

00:13:13: Absolutely.

00:13:14: If you enjoyed this deep dive, new episodes drop every two weeks.

00:13:17: Also check out our other editions covering ICT and tech, artificial intelligence, cloud sustainability and green ICT, defense tech and health

00:13:25: tech.

00:13:25: Thanks for tuning in.

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