Best of LinkedIn: Digital Products & Services CW 40/ 41

00:00:00: This deep dive is provided by Thomas Algeyer and Franis, based on the most relevant LinkedIn posts about digital products and services in calendar weeks, forty and forty one.

00:00:09: Franis 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.

00:00:20: Welcome back to the deep dive.

00:00:22: Today, we're digging into what digital product professionals are really focusing on right now, and you know, it's less about the hype cycles and much more about disciplined execution.

00:00:31: Pragmatic growth.

00:00:32: We filtered through the noise on LinkedIn for you.

00:00:34: Exactly.

00:00:35: The vibe over the last couple of weeks wasn't chasing the next shiny object.

00:00:39: It was much more about judgment, accountability, and getting crystal clear on outcomes.

00:00:44: And AI, well, it's definitely moving from just talk into like actual operational reality.

00:00:50: And.

00:00:51: Okay, so our mission today is to really unpack those key takeaways.

00:00:54: We're talking strategy, organizational alignment, and yeah, where AI is actually making a difference or maybe causing headaches.

00:01:01: Ready to get into it?

00:01:02: Let's start at the beginning.

00:01:03: Strategy and discovery.

00:01:04: Yeah, this was huge.

00:01:05: There's a real push away from just, you know, churning out features, getting back to fundamentally understanding the problem you're solving.

00:01:12: Because if you don't get that right, well, you build the wrong solution.

00:01:16: Simple as that.

00:01:16: And one of the sharpest points we saw, I thought, was Kathleen Mayfield's take on user stories.

00:01:23: the standard format.

00:01:24: Oh, yeah.

00:01:25: That was fascinating.

00:01:26: She argues that the classic, as a role, I want feature template, it actually kind of blinds you.

00:01:31: Humans just don't talk like that when they describe their real frustrations or needs.

00:01:35: They tell stories, right?

00:01:36: They complain about specific pains.

00:01:37: Precisely.

00:01:38: So I want reports as one thing.

00:01:41: It leads to a basic report builder, maybe.

00:01:44: But the story about staying late every single Tuesday, copying data by hand, that points you towards something much deeper, maybe an integration, an automation.

00:01:52: That's strategic.

00:01:53: That difference is critical.

00:01:55: And it links straight to needing validation, not just applause.

00:02:00: Josh Payne had that really powerful story after hitting number one on product hunt.

00:02:03: Right.

00:02:04: Huge lesson there.

00:02:05: Applause is an adoption.

00:02:07: He was really clear.

00:02:08: Talk to at least twenty potential users before you even think about writing code.

00:02:12: Hype might get you attention, but it doesn't mean you have a real market

00:02:16: fit.

00:02:16: It's that dose of reality.

00:02:18: And Inamar Gillard's work.

00:02:20: backs this up too, showing how often products get built on just belief, hope, basically.

00:02:26: Not hard evidence.

00:02:27: Yeah, and Haissam Adomalak added to this.

00:02:29: He said, PM should never ask users what to build.

00:02:31: Users describe symptoms.

00:02:33: Our job is to diagnose the root cause and then validate our hypothesis with data.

00:02:38: Uh-huh.

00:02:39: And Nancy Chu brought in the emotional angle too, didn't she, that real creativity isn't just following a framework.

00:02:44: Exactly.

00:02:45: It's about tapping into the deeper stuff, like the dread someone feels using a clunky system, not just the time at waste.

00:02:51: That's where breakthrough insights often lie.

00:02:53: Okay, so if step one is nailing the real problem and validating it, step two has to be organizing the team to actually deliver the solution effectively, which brings us neatly to product operations and organization.

00:03:06: Yeah, because if the strategy is right but the execution is noisy and misaligned, you still fail.

00:03:11: So the community is definitely looking hard at systems for better alignment, getting everyone on the same page.

00:03:17: The Opportunity Solution Tree, or OST framework, kept popping up here.

00:03:21: Jason Burchard shared how it really helped his team.

00:03:24: It seemed to really simplify things for them, aligning everyone, marketing, product, other stakeholders around concrete company outcomes, making it instantly clear what matters.

00:03:33: So for anyone maybe not using it day to day, the OST visually connects everything back to the main business goal, right?

00:03:40: From the outcome down through opportunities to the actual solutions being proposed.

00:03:45: Right.

00:03:45: And Bertrand's rule was simple.

00:03:47: If an activity doesn't clearly map back up to that.

00:03:49: top-level business outcome on the tree, it's noise.

00:03:52: It's not important right now.

00:03:54: It creates that shared language, that focus.

00:03:56: And having that kind of clarity.

00:03:58: I guess it really throws a spotlight on structural issues, like role definitions.

00:04:01: We saw quite a bit of pushback on splitting the product manager and product owner roles.

00:04:07: Oh, definitely.

00:04:08: Both Pauie Hurin and Chris Belknap basically called that split an anti-pattern.

00:04:12: a flawed organizational design.

00:04:15: That point is that the product owner is the product manager.

00:04:18: They own the value, the vision, the strategy.

00:04:20: Not just managing the backlog.

00:04:22: Exactly.

00:04:23: Reducing the role to just a tactical backlog administrator.

00:04:26: That's knee-capping your strategic capability.

00:04:29: You're not getting what you're paying

00:04:30: for.

00:04:31: And that strategic leader, the true PMPO, needs support.

00:04:36: which is where product ops comes in, but maybe not in the way some people think.

00:04:39: Right.

00:04:40: Jenny Wanger and Christina Waru were really clear.

00:04:42: Product ops isn't just a task list or glorified project management.

00:04:46: It should be a strategic system focused on enablement.

00:04:50: Freeing up product leaders to focus on value creation, not just operational friction.

00:04:55: Makes sense.

00:04:56: And Katie Hudson added that velocity isn't just about shipping fast.

00:04:59: No, it's about sustainable direction.

00:05:01: Speed without the right direction, or speed that burns everyone out because of internal chaos.

00:05:06: That's not real velocity.

00:05:08: So how do you even start building that kind of product ops function?

00:05:11: Well, Melissa Perry had some very practical advice.

00:05:14: Start small, don't try to fix everything at once, find a specific pain point, maybe dashboard visibility for one team, fix it, show the value, build trust, and then scale.

00:05:25: Incremental wins.

00:05:26: Okay, scaling, organizational change.

00:05:29: That leads us right into the next big theme, where things got particularly interesting, AI in product.

00:05:37: The conversation seems to be shifting less about the models themselves.

00:05:40: And much more about operational risk, governance, and what AI leadership actually

00:05:44: means.

00:05:46: So what's driving that shift?

00:05:47: Well, a key finding from Harshit Krishnachatteri really stood out.

00:05:51: He found most AI agents don't fail because the underlying models are weak.

00:05:55: They fail because of operational gaps.

00:05:57: Things like security, compliance, messy feedback loops, broken internal processes.

00:06:01: So it's the plumbing, not the magic box.

00:06:03: Pretty much.

00:06:04: The takeaway is you need security and compliance baked in from day one.

00:06:07: Robust observability.

00:06:09: Treat it like launching any other critical system, basically.

00:06:12: You can't just bolt it on later when you scale.

00:06:14: And that operational challenge directly elevates the need for real AI product leadership, doesn't it?

00:06:21: You mentioned those huge salaries.

00:06:22: Carlos Gonzalez de Villambrosia highlighted up to a million dollars.

00:06:25: Yeah, eye-watering sums at places like Meta and Netflix.

00:06:28: But he was very specific about why.

00:06:30: It's not for coding skills or just knowing the tools.

00:06:33: It's for the strategic heavy lifting.

00:06:35: Like what exactly?

00:06:36: The ability to fundamentally rewire the company's operating model around AI to navigate that tricky balance between pushing innovation hard and managing the risks, the governance, the compliance, that takes serious judgment and vision.

00:06:49: Which really highlights where the human value remains critical.

00:06:52: Patrick Gill will put it really well saying AI exposes fake value.

00:06:56: Yeah, I loved that phrase.

00:06:57: His point was, if eighty percent of your job is routine process work, grooming backlogs meticulously, writing perfectly formatted user stories based on templates, AI is coming for that.

00:07:08: So

00:07:08: what's left?

00:07:09: What's the irreplaceable part?

00:07:10: Judgment.

00:07:11: Connecting those fuzzy customer insights to tangible business impact.

00:07:16: Taking a vague idea and turning it into a validated bet that actually makes money or solves a real problem.

00:07:22: That's the core PM skill.

00:07:23: And Itamar Gillett echoed that, saying AI's true power isn't replacement, it's augmentation.

00:07:29: Exactly.

00:07:30: It frees up the PM from the tedious stuff to focus more on judgment, empathy, understanding users, making smarter evidence-based decisions.

00:07:38: It amplifies the human element.

00:07:40: and we're seeing practical examples of that amplification already.

00:07:43: For

00:07:43: sure.

00:07:43: Johnny Longdon showed how methods like semantic similarity rating SSR are making analysis of free text feedback almost as reliable as humans doing it manually.

00:07:52: Okay, hang on.

00:07:53: Semantic similarity rating SSR.

00:07:55: Can you break that down quickly?

00:07:57: Sure.

00:07:57: Think of it as a smart AI technique to consistently score, categorize, and find patterns in huge amounts of open-ended text feedback, like survey responses or interview notes.

00:08:06: It lets you get near human reliability on analyzing that qualitative data, but much, much faster.

00:08:11: It's about refining the method using AI.

00:08:13: Wow, okay.

00:08:14: That's a concrete example of AI-enhancing insight, not just replacing tasks.

00:08:20: Let's carry that evidence first idea into our final theme, analytics and decision quality.

00:08:26: Making sure the data actually leads to better outcomes.

00:08:29: Yeah, this ties everything together.

00:08:31: We already heard from Gillette about building on belief, not evidence.

00:08:34: This theme really hammered home the need for analytical rigor.

00:08:38: Amir Rezai flagged a worrying trend.

00:08:40: Shipping more, learning less.

00:08:42: Right.

00:08:43: His point wasn't just do more analytics.

00:08:45: It was ask sharper questions.

00:08:47: Stop running experiments just for the sake of it.

00:08:49: And Jonathan Cordo offered a framework for that, the OAA.

00:08:52: Yeah, optimization, attribution analysis.

00:08:55: It's basically a structured way to think through your tests, ensure they're designed smartly, targeted correctly, and will actually give you useful answers, not just confusing data points.

00:09:04: He had that specific warning about multivariate testing too.

00:09:07: Oh yeah, the multivariate trap.

00:09:08: His point was stark.

00:09:10: It needs something like sixteen times the sample size of a simple A-B test to be reliable.

00:09:15: If you don't have that traffic volume, you're likely just misleading yourself.

00:09:19: Stick to simpler, cleaner tests.

00:09:21: So getting quality data requires real discipline.

00:09:24: But then you have to sell the insights, right?

00:09:26: especially upwards.

00:09:27: Absolutely.

00:09:28: Emmy Mitchell had great points on building executive conviction.

00:09:31: Executives don't buy raw metrics.

00:09:34: They buy compelling stories backed by those metrics.

00:09:36: You need the narrative.

00:09:37: Connect the dots from user behavior change to business impact.

00:09:42: Exactly.

00:09:42: Show the ROI.

00:09:43: You have to translate engagement one up.

00:09:45: five percent into which we project will increase retention by X leading to Y dollars.

00:09:51: Speak their language.

00:09:52: And sometimes that language is just Brutally pragmatic.

00:09:55: I thought Buzrakov Skinner's anecdote about the packaging was so telling.

00:09:59: Oh,

00:09:59: the classy packaging rejected by the CEO.

00:10:02: Yeah, that was a perfect reality check.

00:10:03: It wasn't about whether the packaging looked better.

00:10:05: It slightly hurt the monthly profit margin.

00:10:08: end of discussion.

00:10:09: It

00:10:09: shows where the C-suite focus often is.

00:10:11: Monthly cash flow.

00:10:12: Hard ROI.

00:10:14: Your beautiful idea needs numbers, not just maybes, about brand perception.

00:10:19: Totally.

00:10:19: They're playing a numbers game, and that pragmatism seems to be spreading.

00:10:23: Demitra Derivianco noted a shift in the market,

00:10:25: too.

00:10:25: About consultancies.

00:10:27: Yeah, a growing feeling that internal teams are starting to deliver more value than some high-cost, low-implementation consultancy projects.

00:10:35: A push towards building and owning that execution capability in-house.

00:10:40: Companies want the results, not just the advice deck.

00:10:43: It all comes back to ownership, accountability, and rigor, doesn't it?

00:10:47: So wrapping up this deep dive, it feels like the core message from LinkedIn these past weeks is clear.

00:10:54: Success now hinges on strategic discipline, really finding the pain point, plus organizational clarity, making decisions based on solid evidence and using AI smartly to boost human judgment, not try to replace it.

00:11:07: It's about mastering the fundamentals.

00:11:08: Well said.

00:11:09: And if you enjoyed this deep dive, remember new episodes drop every two weeks.

00:11:13: Also check out our other editions covering ICT and tech, artificial intelligence, cloud sustainability and green ICT, defense tech, and health tech.

00:11:20: Okay,

00:11:21: before we sign off, a final thought sparked by Dylan Angler.

00:11:24: He suggested that Sass isn't really being killed by AI, it's being killed by founders unwilling to kill their own legacy products.

00:11:29: Ooh,

00:11:30: provocative.

00:11:31: Right.

00:11:32: So the question we'll leave you with is this.

00:11:34: What legacy product feature or maybe even process are you still defending in your organization that maybe needs to be cannibalized, cleared away so you can truly evolve?

00:11:45: Something to think about.

00:11:46: Definitely something to ponder.

00:11:47: Thanks for joining us on the deep dive.

00:11:49: We hope you found it valuable and don't forget to subscribe for more insights.