Latest / Best of LinkedIn: Digital Transformation & Tech / Best of LinkedIn: Digital Products & Services CW 10/ 11

Transcript

Best of LinkedIn: Digital Products & Services CW 10/ 11 · Best of LinkedIn: Digital Transformation & Tech

• 0:00
  Speaker 1: This episode is provided by Thomas Allgaier and Frennus, based on the most relevant LinkedIn posts about digital products and services in calendar weeks ten and eleven.
• 0:09
  Speaker 1: Frenness 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.
• 0:19
  Speaker 0: Right, so welcome to our deep dive.
• 0:21
  Speaker 0: Today we're digging into the top digital products and services trends that have been popping up all over LinkedIn for the past two weeks.
• 0:28
  Speaker 1: Yeah And For you listening whether your in the middle of sprint planning or trying to rework your whole product org The mission here is really about practical adoption.
• 0:37
  Speaker 0: Exactly I mean the conversation has decisively shifted.
• 0:40
  Speaker 0: We are moving way.
• 0:41
  Speaker 0: pass that initial AI experimentation height.
• 0:44
  Speaker 1: Oh one hundred percent.
• 0:45
  Speaker 1: yeah The magic trick phase is over right?
• 0:48
  Speaker 1: operating discipline now.
• 0:50
  Speaker 1: Like, how is AI actually rewiring daily workflows?
• 0:54
  Speaker 1: And honestly even the definition of a product manager...
• 0:57
  Speaker 0: It's changing everything!
• 0:58
  Speaker 1: ...it really is.
• 0:59
  Speaker 1: I saw this great example from Nico Noel that just nailed this.
• 1:02
  Speaker 1: he used an AI agent Claude Code to actually ship changes to a landing page.
• 1:09
  Speaker 0: Okay so actually writing in code Yeah
• 1:11
  Speaker 1: but he didn't stop there or you know hand it off to a dev.
• 1:14
  Speaker 1: He stayed right in the interface used productanalyst.ai and immediately wired up a Slack alert for funnel monitoring.
• 1:22
  Speaker 1: He did the coding, tracking or automated reporting all in like ten minutes.
• 1:26
  Speaker 0: Ten minutes?
• 1:27
  Speaker 0: I mean that kind of turnaround is just staggering.
• 1:29
  Speaker 0: but you know it's also massive trap.
• 1:32
  Speaker 1: How so?
• 1:32
  Speaker 1: Well,
• 1:33
  Speaker 0: Akash Gupta broke this down brilliantly.
• 1:35
  Speaker 0: When building becomes that frictionless the danger of unguided-building just skyrockets He pointed out.
• 1:41
  Speaker 0: we're seeing two distinct types of PMs emerge right now.
• 1:45
  Speaker 1: Okay what are they?
• 1:46
  Speaker 0: So the first group he calls The Builders.
• 1:48
  Speaker 0: These are ones who spin up a quick prototype show it off and get green light.
• 1:52
  Speaker 0: because you know working software always looks better than slide deck.
• 1:55
  Speaker 1: Right.
• 1:55
  Speaker 1: but their moving fast probably aren't checking underlying metrics
• 1:59
  Speaker 0: Exactly!
• 1:59
  Speaker 0: They don't check metrics or crucially, API costs.
• 2:04
  Speaker 0: Generative AI isn't a flat rate server cost.
• 2:06
  Speaker 0: you're paying compute per token.
• 2:08
  Speaker 0: Oh, right.
• 2:09
  Speaker 0: So every unoptimized search just drains the budget.
• 2:13
  Speaker 0: but then Gupta says that second group is The Evaluators.
• 2:16
  Speaker 1: I'm guessing they operate a bit differently Completely
• 2:19
  Speaker 0: different and evaluator will prototype something like fifty times And deliberately kill eighty percent of those iterations.
• 2:26
  Speaker 1: Ah so they filter it.
• 2:27
  Speaker 1: Yeah
• 2:27
  Speaker 0: gupta calls having taste at speed It's ability to refine through rapid iteration.
• 2:33
  Speaker 1: That makes total sense.
• 2:34
  Speaker 1: But yeah i have push back here.
• 2:36
  Speaker 1: Go for It's just there.
• 2:37
  Speaker 1: is this really seductive trend right now called vibe coding, where a PM can basically generate an app in the afternoon.
• 2:44
  Speaker 0: Oh yeah!
• 2:45
  Speaker 0: Vibecoding is everywhere
• 2:46
  Speaker 1: Right.
• 2:46
  Speaker 1: So isn't that necessary first step for raptid discovery?
• 2:49
  Speaker 1: I mean why shouldn't a PM own the whole stack if AI allows it?
• 2:54
  Speaker 1: Its like giving everyone on team a nail gun when they only know how to use a hammer.
• 2:58
  Speaker 0: Ok thats great analogy.
• 2:59
  Speaker 1: Sure you build your house way faster but Without structural knowledge, the whole thing might just collapse in a breeze.
• 3:06
  Speaker 1: But still...the speed!
• 3:08
  Speaker 0: The Speed is exactly why Tanya Maldonado and Marlon Davis were raising massive red flags about this
• 3:14
  Speaker 1: Really?
• 3:15
  Speaker 1: What did they say?
• 3:16
  Speaker 0: They pointed out that PMs absolutely should not be replacing developers on enterprise production grade systems.
• 3:23
  Speaker 0: Scalability and security are not, you know side quests.
• 3:26
  Speaker 1: Yeah
• 3:27
  Speaker 0: if your building fifty times faster with your nail gun You can ship disasters fifty time's faster Which means launch readiness has to become the new bottleneck.
• 3:35
  Speaker 1: Launch Readiness Okay.
• 3:37
  Speaker 1: so how do we actually manage without losing speed?
• 3:40
  Speaker 1: Because Shardle Nyak actually shared a twenty-three item AI product launch checklist on this.
• 3:46
  Speaker 0: Twenty three items?
• 3:47
  Speaker 0: That's rigorous!
• 3:48
  Speaker 1: Very, and he highlighted risks that traditional saws just doesn't face like prompt injection defenses.
• 3:54
  Speaker 1: Oh,
• 3:54
  Speaker 0: prompt injection is terrifying.
• 3:56
  Speaker 0: users can just trick the system into ignoring its own security guardrails
• 4:00
  Speaker 1: Exactly.
• 4:01
  Speaker 1: And he also mandates strict budget alerts, like setting a warning at eighty percent of budget but literally killing the feature if it hits two hundred percent.
• 4:10
  Speaker 0: Because those runaway API costs we mentioned
• 4:12
  Speaker 1: right?
• 4:12
  Speaker 1: He says gradual rollouts are absolute necessities.
• 4:14
  Speaker 1: now you go five percent to twenty-five to fifty to one hundred launching to a hundred percent on day One is just asking to lose user trust.
• 4:23
  Speaker 0: one hundred percent agree And it's a systemic risk too.
• 4:26
  Speaker 0: Dr Bart Jaworski had this fascinating advice about setting up.
• 4:34
  Speaker 0: Its only job is strictly to rate and evaluate the answers of the main AI product.
• 4:39
  Speaker 1: Oh, wow!
• 4:39
  Speaker 1: So it's an AI babysitting your
• 4:41
  Speaker 1: A.I.,
• 4:41
  Speaker 1: to block bad outputs?
• 4:42
  Speaker 0: Exactly...and it ties right into what Patricia Bertini was saying about compliance
• 4:46
  Speaker 1: Like The AI Act in GDPR.
• 4:48
  Speaker 1: Yes,
• 4:48
  Speaker 0: Ann Diara She pointed out that Compliance Is Shifting Entirely Upstream.
• 4:52
  Speaker 0: You cannot wait until the end To check these things anymore.
• 4:55
  Speaker 0: you don't want Find Out Your Non-compliant Via Massive Fine Or An Audit.
• 4:59
  Speaker 1: Okay But Wait If We Are Slowing Down To Check a twenty-three item list, and running secondary LLM evaluations.
• 5:08
  Speaker 1: Doesn't that completely neutralize the incredible speed AI gave us in first place?
• 5:12
  Speaker 1: It
• 5:13
  Speaker 0: feels like it right!
• 5:14
  Speaker 0: Yeah
• 5:14
  Speaker 1: I mean, how do we balance safety with the intense pressure from leadership to ship?
• 5:19
  Speaker 0: Well.
• 5:20
  Speaker 0: The hard truth is you can't balance it without changing the underlying structure of the organization.
• 5:25
  Speaker 0: dreaming You can just add AI to a broken system and expect it to work.
• 5:29
  Speaker 0: You have to fix the product operating
• 5:32
  Speaker 1: model POM.
• 5:33
  Speaker 1: people talk about that all the time but It's usually so abstract
• 5:36
  Speaker 0: It is, but Ralph Joachim brought some brutal reality to it with the time-poverty paradox.
• 5:41
  Speaker 0: Oh
• 5:42
  Speaker 1: I saw those stats.
• 5:42
  Speaker 1: they were wild!
• 5:43
  Speaker 0: Yeah, ninety two percent of product leaders own revenue outcomes But they spend sixty six percent of their week on manual coordination work
• 5:51
  Speaker 1: Which leaves them zero time for actual strategic analysis.
• 5:54
  Speaker 0: Exactly
• 5:55
  Speaker 1: Zero Time.
• 5:55
  Speaker 0: That's insane.
• 5:56
  Speaker 0: its like being hired as a ships navigator... ...but you're forced to spent ninety percent your time below deck shoveling coal into engine.
• 6:03
  Speaker 0: How are ya supposed steer?
• 6:04
  Speaker 0: You aren't!
• 6:05
  Speaker 0: And that's why Catherine Shepard King argues a real product operating model isn't just renaming your project managers.
• 6:12
  Speaker 1: Or copying Spotify's squad template from ten years ago?
• 6:15
  Speaker 0: Right, A Real POM is about the mechanics of how work actually gets funded and decisions are made.
• 6:22
  Speaker 1: How do we fix those mechanics?
• 6:24
  Speaker 0: Igor Voth shared a really battle-tested five layer model that I
• 6:28
  Speaker 1: loved.
• 6:28
  Speaker 1: Okay, bring it down for me!
• 6:29
  Speaker 0: It goes company priority which leads to root cause then to strategic bet...down to opportunities and finally experiments.
• 6:38
  Speaker 1: So every experiment has to tie all the way back up to a company priority?
• 6:42
  Speaker 0: Exactly if doesn't trace back to a priority you are just shipping features.
• 6:46
  Speaker 0: You aren't driving outcomes.
• 6:47
  Speaker 0: Ok.
• 6:48
  Speaker 1: so If we fix the operating model and we actually give our leaders the time to navigate from the deck of a ship, they still need to be looking at the right maps.
• 6:56
  Speaker 1: Right?
• 6:56
  Speaker 0: Absolutely!
• 6:57
  Speaker 0: Which leads us into the evolution of product intelligence.
• 7:00
  Speaker 1: Because Mauricio Cardin has pointed out that this problem isn't lack-of-beta
• 7:06
  Speaker 0: Oh...we have too much data.
• 7:07
  Speaker 1: Exactly The problem is interpretation.
• 7:10
  Speaker 1: He says AI shouldn't just use to build chat features.
• 7:14
  Speaker 1: It should an intelligence layer That connects fragmented telemetry And user feedback
• 7:19
  Speaker 0: to spot the patterns that humans just miss.
• 7:21
  Speaker 1: Yes, and Nigs or Voodies highlighted how Netflix does this by treating data as a product
• 7:26
  Speaker 0: meaning it has clear ownership in life cycle management.
• 7:29
  Speaker 1: right but there's a massive warning sign here from boost Raku skooner about optimizing for the wrong metrics.
• 7:36
  Speaker 0: oh this is such a common trap.
• 7:37
  Speaker 0: what were her examples?
• 7:38
  Speaker 1: so one was a mahjong app.
• 7:40
  Speaker 1: they made the game easier to boost retention And sure it did temporarily, but it completely killed the challenge of game which was actual value.
• 7:47
  Speaker 1: Oh!
• 7:48
  Speaker 0: So they alienated their core users?
• 7:50
  Speaker 1: Completely.
• 7:51
  Speaker 1: and her other example is a puzzle game called Screwdom.
• 7:53
  Speaker 1: They basically forced monetization and just lost user immediately.
• 7:57
  Speaker 1: Optimizing dashboard metric without understanding user values destroys product.
• 8:02
  Speaker 0: It really does.
• 8:03
  Speaker 0: But
• 8:03
  Speaker 1: let's be real for second.
• 8:04
  Speaker 1: If your PM and retention metrics revenue graphs are pointing up into right aren't we doing our jobs?
• 8:11
  Speaker 0: It looks like it on paper.
• 8:12
  Speaker 0: Right,
• 8:13
  Speaker 1: so how does a PM justify ignoring a green dashboard to tell the CFO trust me?
• 8:19
  Speaker 1: The users actually hate it.
• 8:21
  Speaker 1: Well
• 8:21
  Speaker 0: that right there is the definition of true product discipline...it means looking beyond that immediate dopamine hit of vanity metric.
• 8:29
  Speaker 0: If you spike revenue this month but churn triples next month..the product is dying.
• 8:34
  Speaker 1: Yeah!
• 8:35
  Speaker 1: That makes sense.
• 8:35
  Speaker 1: its about sustainable outcomes
• 8:37
  Speaker 0: Exactly.
• 8:39
  Speaker 0: Well, as we wrap up the steep dive We want to leave you with a final thought inspired by Pavel Fabrikantov.
• 8:43
  Speaker 0: Oh
• 8:44
  Speaker 1: I love his take on this.
• 8:45
  Speaker 0: Yeah he reminded us that buying AI tools and writing strategy docs is The easy part.
• 8:50
  Speaker 0: anybody can do That.
• 8:51
  Speaker 0: but true competitive advantage comes from Process knowledge
• 8:55
  Speaker 1: process.
• 8:56
  Speaker 1: Knowledge yes
• 8:57
  Speaker 0: that tacit know-how That only accumulates when a team ships fails and adjusts together over time.
• 9:03
  Speaker 0: So ask yourself are you just lawyering your roadmap with new tools or?
• 9:06
  Speaker 0: Are you actually engineering the deep process knowledge, but your competitors simply can't copy?
• 9:12
  Speaker 1: that is The real question to ask your team this week.
• 9:14
  Speaker 1: it really
• 9:14
  Speaker 0: does
• 9:15
  Speaker 1: well.
• 9:15
  Speaker 1: if you enjoyed This episode new episodes drop every two weeks.
• 9:18
  Speaker 1: also check out our other editions on ICT in tech artificial intelligence cloud sustainability in green ICT, defense tech and health tech.