Latest / Best of LinkedIn: Digital Transformation & Tech / Best of LinkedIn: Digital Products & Services CW 20/ 21

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Best of LinkedIn: Digital Products & Services CW 20/ 21 · 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 in services.
• 0:07
  Speaker 1: In calendar weeks twenty-and-twenty one.
• 0:09
  Speaker 1: Frenness is a BDB market research company that supports enterprise product teams with building feature by feature competitive intelligence That shows exactly how their product stacks up against the competition.
• 0:21
  Speaker 1: you can find more info in the description.
• 0:23
  Speaker 1: So imagine building a million line software application where your human engineers are like strictly banned from typing A single line of code.
• 0:31
  Speaker 0: I mean it sounds completely absurd, right?
• 0:33
  Speaker 1: Yeah But that's exactly what we're looking into today for this deep dive.
• 0:37
  Speaker 1: We're exploring how AI is fundamentally rewiring product management Right
• 0:41
  Speaker 0: and we're pulling us From the top digital products & services trends across LinkedIn over The past two weeks curated specifically For you the professionals in the ict and tech industry
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  Speaker 1: Exactly because way past treating AI like some novelty text generator.
• 0:56
  Speaker 1: Yeah,
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  Speaker 0: oh for sure.
• 0:57
  Speaker 0: we're tracking a real fundamental paradigm shift here.
• 1:01
  Speaker 0: the conversation on LinkedIn lately makes it so clear.
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  Speaker 0: We are redefining what our product team actually is when the cost of executing code basically drops to zero.
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  Speaker 1: yeah let's start right there with that sheer speed of execution because Akash Gupta posted this incredible story about an open AI product manager.
• 1:19
  Speaker 1: Oh
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  Speaker 0: I saw this one.
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  Speaker 1: So, they wrote a document on a Monday and by Friday it shipped as working feature.
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  Speaker 1: And no engineer wrote single line of code for
• 1:29
  Speaker 0: this.
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  Speaker 0: Not one line?
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  Speaker 0: Nope!
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  Speaker 1: The team lead, Ryan LaPoppolo.
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  Speaker 1: they built this million line app using a totally new workflow where humans were literally forbidden from touching the syntax.
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  Speaker 1: Which
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  Speaker 0: is wild because that forces you to completely rethink what an engineer even does like.
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  Speaker 0: if and AI agent makes mistake in set up You can't
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  Speaker 1: just go ahead fix it
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  Speaker 0: Exactly!
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  Speaker 0: Human can't jump into file and fix typo.
• 1:50
  Speaker 0: That's bandaid.
• 1:52
  Speaker 0: Instead had write test or update documentation.
• 1:55
  Speaker 0: so AI never make specific mistakes
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  Speaker 1: again Right?
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  Speaker 1: Akash noted that sometimes they had to recurse, like six or eight levels deep.
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  Speaker 1: just give the agent the right credentials.
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  Speaker 0: Wow!
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  Speaker 1: Yeah Like if AI hits an API endpoint and fails because of an auth error The human doesn't hand over the token.
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  Speaker 0: They have build scaffolding for it.
• 2:17
  Speaker 0: Exactly
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  Speaker 1: Build a scaffolding define permissions And teach environment how pass those credentials securely.
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  Speaker 1: After a month of this, the codebase itself literally became The Quality Gate.
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  Speaker 1: I mean they
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  Speaker 0: weren't building the feature at all.
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  Speaker 0: They're building the environment that allows... ...the machine to build the features safely.
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  Speaker 1: Right it's like we stopped throwing the bowling ball down the lane ourselves And instead were just meticulously building the bumpers You know?
• 2:44
  Speaker 1: So AI can't possibly throw a gutterball.
• 2:47
  Speaker 0: Oh, I like that analogy.
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  Speaker 0: The bumpers are the compounding asset and that actually ties perfectly into what Knit and Ms.
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  Speaker 0: Ra was talking about.
• 2:53
  Speaker 0: All
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  Speaker 1: right!
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  Speaker 1: The storyteller thing?
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  Speaker 0: Yeah.
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  Speaker 0: Knit observed in this AI era... ...the best product managers aren't just going to be the story-tellers anymore Like we're so used to.
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  Speaker 0: PMs writing a perfect Jura ticket with all of these user empathy Right.
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  Speaker 0: But now they need people who can turn human intent into what Nitin calls executable truth
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  Speaker 1: Executable truth.
• 3:17
  Speaker 1: Man, that's a great phrase!
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  Speaker 0: It really is.
• 3:19
  Speaker 0: The instructions have to be so mechanical and precise That the machine processes it with zero ambiguity Which Prank Jamr actually calls context engineering
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  Speaker 1: Context engineer?
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  Speaker 1: Wait explain that a bit more.
• 3:32
  Speaker 1: Well
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  Speaker 0: Prank pointed out that traditional team scaling Is just dead
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  Speaker 1: Like just hiring more developers.
• 3:37
  Speaker 0: Exactly...the old way was linear.
• 3:39
  Speaker 0: More features meant more headcount But now scale comes from providing the exact right boundaries Defining the guardrails.
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  Speaker 0: So
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  Speaker 1: what does a good boundary actually look like then?
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  Speaker 0: It's defining ATI payload limits, data schemas error handling.
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  Speaker 0: all of that because given AI to little context you get generic output give it too much Like dumping your whole company history into a prompt
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  Speaker 1: and it just starts hallucinating.
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  Speaker 0: exactly it invents features That shouldn't even exist.
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  Speaker 0: so orchestrating that context is The new superpower.
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  Speaker 1: okay.
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  Speaker 1: But but let me push back on this a little bit sure if we've removed All the friction of coding right mm-hmm and we can just execute instantly with some boundaries.
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  Speaker 1: Aren't we just opening the floodgates to churn out a massive amount of garbage features?
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  Speaker 0: That is the big question!
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  Speaker 1: Right, because manual coding was slow but that friction protected us from building bad ideas.
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  Speaker 1: it's too expensive to build everything.
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  Speaker 0: You're definitely not alone in thinking that Shippo Michele actually posted very blunt warning about this exact trap.
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  Speaker 0: Oh, really?
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  Speaker 0: Yeah.
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  Speaker 0: He explicitly warned product teams not to use AI.
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  Speaker 0: to quote build slop efficiently.
• 4:47
  Speaker 1: Build slop efficiently.
• 4:48
  Speaker 1: that's exactly it right
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  Speaker 0: because execution is so fast now the bottleneck hasn't actually disappeared It just moved upstream its fully relocated into R&D and discovery
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  Speaker 1: meaning validating The customer problem before you unleash.
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  Speaker 0: Exactly because if you can build a terrible idea in a day instead of six months You're still polluting your product with the terrible idea.
• 5:10
  Speaker 1: Yeah, and SF mayors shared a really practical tactic to combat that urge to just ship immediately.
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  Speaker 0: Oh what did he suggest?
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  Speaker 1: He said that when you use AI To Build prototypes...you should actually prompt The tool..to make it look rough Like intentionally sketchy
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  Speaker 0: Really like a whiteboard drawing
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  Speaker 1: squiggly lines, mismatch fonts the whole deal.
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  Speaker 1: Yeah because the psychology there is fascinating.
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  Speaker 1: The moment you put a polished screen in front of a stakeholder Oh
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  Speaker 0: they think it's final exactly.
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  Speaker 1: They see high fidelity and they instantly start arguing about like pixel spacing at button colors?
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  Speaker 1: They totally ignore the core workflow
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  Speaker 0: that makes so much sense.
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  Speaker 0: A rough sketch forces them to actually discuss the problem
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  Speaker 1: right as half argues.
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  Speaker 1: the smart move isn't shipping more features to market It's exploring more options internally.
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  Speaker 1: generate three rough prototypes in an hour, test them and see what actually resonates before you commit.
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  Speaker 0: Using AI to stress-test your thinking a
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  Speaker 1: pre-mortem.
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  Speaker 1: How does that work?
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  Speaker 0: So he feeds his project files, market research all of it into the AI's context window and then prompts to imagine its six months post launch.
• 6:27
  Speaker 0: Oh wow!
• 6:28
  Speaker 0: Yeah He tells the AI look initially we had great adoption but then usage dropped churn spiked And product failed miserably.
• 6:36
  Speaker 0: Brutally analyze why this happened based on my documents.
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  Speaker 1: That is so ruthless, I love it.
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  Speaker 0: It instantly cross-references everything like.
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  Speaker 0: you'll find where your acquisition strategy clashes with Your technical debt and point out exactly Where the logic breaks?
• 6:50
  Speaker 1: Basically tearing your assumptions apart before You write a single line of executable truth.
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  Speaker 0: Exactly.
• 6:55
  Speaker 0: but um okay.
• 6:56
  Speaker 0: So If rough prototypes and pre-mortems help us filter out the noise upstream, we're still left with this massive pile of validated ideas.
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  Speaker 0: How do you map those without causing a massive traffic jam on the roadmap?
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  Speaker 1: Oh!
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  Speaker 1: The Roadmap Traffic Jam.
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  Speaker 1: Shahab Jain actually painted a picture of it that I think basically everyone in tech has lived through.
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  Speaker 0: Staring at road maps with thirty items?
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  Speaker 0: Yes,
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  Speaker 1: every single one has a champion... a solid business case, and they're all mission critical.
• 7:27
  Speaker 0: And then you try to solve it with a scoring model?
• 7:29
  Speaker 1: Right everyone defaults too like rice reach impact confidence effort.
• 7:33
  Speaker 1: You run the math add the weights and then you still end up debating The same five items for three hours anyway
• 7:39
  Speaker 0: because those scoring models totally break down in an AI world Like think about the effort variable in Rice.
• 7:45
  Speaker 1: Right, if AI drops the effort to near zero.
• 7:47
  Speaker 0: Exactly!
• 7:48
  Speaker 0: If an AI can code it in an afternoon then math gets completely messed up.
• 7:52
  Speaker 0: Every feature suddenly looks like a high ROI quick win.
• 7:55
  Speaker 1: Yeah Shilab realized prioritization isn't actually a math problem anymore It's clarity problems.
• 8:01
  Speaker 0: That is great point And Raj Travedi expended on this saying that how has gotten so easy.
• 8:05
  Speaker 0: The complexity just moved up.
• 8:07
  Speaker 0: decision stack
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  Speaker 1: Meaning defining WAY Is now hardest
• 8:11
  Speaker 0: part Exactly.
• 8:13
  Speaker 0: Deciding what actually deserves to exist, because if execution is instant but your strategy is fuzzy... Your product will just bloat in every direction It
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  Speaker 1: drifts
• 8:23
  Speaker 0: Right!
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  Speaker 0: Dennis Cupins posted that road maps shouldn't even be gaunt charts anymore.
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  Speaker 0: They should act like universal traffic signs.
• 8:30
  Speaker 1: Traffic signs?
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  Speaker 1: That instantly makes me think of what Strape did.
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  Speaker 1: Panya Kakos posted about Stripes' new public-product roadmap.
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  Speaker 1: Oh
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  Speaker 0: I missed one.
• 8:40
  Speaker 0: What'd they do?
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  Speaker 1: They didn't publish some huge laundry list of requested features, they organized everything around these massive crystal clear themes like programmability network activation and AI infrastructure.
• 8:53
  Speaker 0: Oh wow that kind of thematic clarity changes Everything for the engineers.
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  Speaker 1: exactly if The theme is programmability And an engineer Is picking between two API structures?
• 9:03
  Speaker 1: They don't need a meeting.
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  Speaker 0: just look at the traffic sign.
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  Speaker 1: yes They pick the one that gives developers more flexibility, because that's their destination.
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  Speaker 1: The strategy does all
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  Speaker 0: of
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  Speaker 1: this
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  Speaker 0: heavy
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  Speaker 0: lifting.".
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  Speaker 0: People tend to think AI is this great equalizer, right?
• 9:27
  Speaker 1: Yeah.
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  Speaker 1: It levels the playing field for everyone
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  Speaker 0: but The data shows it's not!
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  Speaker 0: AI Is an amplifier.
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  Speaker 0: Smaller mature executive-led product teams are seeing huge benefits But large orgs like over five hundred people AIs actually making things worse For them.
• 9:43
  Speaker 0: Wait
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  Speaker 1: Making things worse.
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  Speaker 1: So what's the actual ceiling here?
• 9:46
  Speaker 1: Is the AI tech just not capable enough yet for complex enterprise stuff?
• 9:51
  Speaker 0: No, The Tech is fine!
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  Speaker 0: The Ceiling isn't capability...the Ceiling is
• 9:54
  Speaker 1: trust Trust.
• 9:56
  Speaker 0: Yeah, Malton Schultz shared this AI maturity gap report.
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  Speaker 0: Forty percent of product leaders ranked trust as their number one blocker.
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  Speaker 0: Wow
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  Speaker 1: Higher than security.
• 10:05
  Speaker 0: Higher then security, higher than data quality.
• 10:07
  Speaker 0: because think about the friction if an AI writes ten thousand lines of code in a day but it has to sit in a queue for monthly security review board
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  Speaker 1: Your system speed is still zero
• 10:16
  Speaker 0: Exactly!
• 10:17
  Speaker 0: The AI just hits those legacy bottlenecks hundred times faster and creates massive gridlock.
• 10:22
  Speaker 0: And Marisia Cardin brought up another angle on this.
• 10:25
  Speaker 0: In enterprise software users will totally tolerate a missing feature.
• 10:29
  Speaker 0: They'll build or work around.
• 10:30
  Speaker 0: Oh yeah
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  Speaker 1: they use a spreadsheet
• 10:31
  Speaker 0: Right But they will instantly lose trust if the product's behavior becomes inconsistent.
• 10:37
  Speaker 1: Because, If AI ships code fast but like breaks a validation rule in a finance module
• 10:43
  Speaker 0: Or permission structure changes subtly The accountants stop trusting this system.
• 10:48
  Speaker 0: They export everything.
• 10:49
  Speaker 0: It does not matter how fast you ship.
• 10:51
  Speaker 0: if users don't trust output.
• 10:53
  Speaker 1: That makes total sense And it really ties into what Diego Granados was saying about future of product design.
• 10:58
  Speaker 0: Oh!
• 10:58
  Speaker 0: About next user?
• 10:59
  Speaker 0: Yeah
• 11:00
  Speaker 1: He pointed out that your product team's next user is actually an AI agent.
• 11:04
  Speaker 1: Like, Diego uses Claude Code to interact with SaaS products for him.
• 11:08
  Speaker 0: He literally lets the agent test software before he buys it?
• 11:11
  Speaker 0: Yes!
• 11:12
  Speaker 1: The agent checks APIs and documentation to see if they can execute tasks.
• 11:17
  Speaker 1: If the agent can't navigate the product... Diego just doesn't buy it.
• 11:20
  Speaker 0: That is incredible.
• 11:21
  Speaker 0: So we aren't designing human psychology anymore.
• 11:24
  Speaker 0: We have design machine execution.
• 11:27
  Speaker 1: Right Diego argues there are three distinct users now.
• 11:31
  Speaker 1: You have the human who wants intuitive UI, then you have the agent that doesn't care about the UI at all.
• 11:38
  Speaker 1: it just want predictable APIs high rate limits and perfect documentation.
• 11:42
  Speaker 0: And who's third?
• 11:43
  Speaker 1: The admin.
• 11:44
  Speaker 1: Yeah!
• 11:44
  Speaker 1: And the Admin is terrified of the Agent.
• 11:46
  Speaker 0: Oh I bet
• 11:47
  Speaker 1: They need scope permissions bulletproof audit logs and instant kill switches to stop a runaway agent.
• 11:54
  Speaker 0: Wow.
• 11:54
  Speaker 0: So if your operating model is messy and you're code base lacks the consistency Marisa was warning about, an external agent just won't be able to use your product?
• 12:02
  Speaker 1: It'll hit an undocumented inconsistency in just
• 12:05
  Speaker 0: break And then your product becomes practically invisible to machine users that are starting to dominate enterprise.
• 12:10
  Speaker 0: Exactly
• 12:11
  Speaker 1: so taking a step back looking at everything we've synthesized today it's very clear narrative.
• 12:16
  Speaker 1: We have officially transitioned out of era defined by ruthless feature prioritization based on limited developer capacity.
• 12:24
  Speaker 0: Right, the bottleneck is no longer the coding itself.
• 12:27
  Speaker 0: we're entering an era where your ultimate competitive advantages are extreme strategic focus enterprise grade trust and context engineering
• 12:36
  Speaker 1: Which means knowing exactly what deserves to be built And building that heavily guarded environment for AI to execute safely
• 12:42
  Speaker 0: Precisely.
• 12:44
  Speaker 1: Well We always like leave you with a final provocative thought to mull over.
• 12:48
  Speaker 1: Based On The Deep Dive
• 12:49
  Speaker 0: Let's hear it.
• 12:50
  Speaker 1: So We just talked about how your next user is likely an AI agent, and how you're code base is basically evolving into a constraint layer for coding agents.
• 12:58
  Speaker 1: Right?
• 12:59
  Speaker 1: So what happens when an AI-agent user acting on behalf of the client starts negotiating API access in feature requests directly with your AI generated product roadmap?
• 13:10
  Speaker 1: Oh wow!
• 13:12
  Speaker 1: In world where machines define demand and machines write codes to supply it What exactly becomes the definition of a product manager?
• 13:19
  Speaker 1: It
• 13:19
  Speaker 0: basically becomes a pure exercise in defining intent.
• 13:22
  Speaker 1: Exactly!
• 13:23
  Speaker 1: If you enjoyed this episode, new episodes drop every two weeks.
• 13:26
  Speaker 1: Also check out our other editions on ICT and tech Artificial Intelligence Clouds Sustainability and Green ICT Defense Tech And HealthTech.
• 13:33
  Speaker 1: Thank You so much for joining us On This Deep Dive.
• 13:35
  Speaker 1: Don't forget to subscribe.
• 13:37
  Speaker 1: We will catch you next time.