Latest / Best of LinkedIn: Digital Transformation & Tech / Best of LinkedIn: Digital Products & Services CW 24/ 25

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Best of LinkedIn: Digital Products & Services CW 24/ 25 · 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 twenty four and twenty five.
• 0:08
  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.
• 0:19
  Speaker 1: you can find more info In the description.
• 0:20
  Speaker 0: imagine right spending your entire week just meticulously grooming a product backlog.
• 0:28
  Speaker 0: You know, writing tickets, syncing updates across like five different platforms only to realize an AI agent just did your entire month's workload in about thirty minutes.
• 0:39
  Speaker 1: Oh man yeah that is brutal but honestly welcome todays deep dive because we are looking at the top digital products and services trends circulating among ICT and tech professionals right now
• 0:50
  Speaker 0: Exactly.
• 0:51
  Speaker 0: And we're pulling all of this directly from the most relevant discussions across LinkedIn during calendar weeks, twenty-four and twenty five
• 0:57
  Speaker 1: right?
• 0:57
  Speaker 1: The overarching theme here is just.
• 0:59
  Speaker 1: I mean it's impossible to ignore.
• 1:01
  Speaker 1: the fundamental architecture of how we build and sell software is shifting completely
• 1:05
  Speaker 0: totally.
• 1:06
  Speaker 0: so We're keeping this fast paced very focused on the actual mechanisms behind these changes.
• 1:11
  Speaker 0: no fluff Just what happens when the traditional tech playbook you know stops working.
• 1:16
  Speaker 1: Yeah, so let's start right in the engine room like how AI is fundamentally rewiring the day-to-day reality of product management?
• 1:23
  Speaker 0: Because we're not just talking about using chat GPT to write a polite email to your stakeholders anymore.
• 1:30
  Speaker 1: No!
• 1:30
  Speaker 1: Not at all.
• 1:31
  Speaker 1: The conversation has moved.
• 1:35
  Speaker 1: Like Brandon Poppistine showed this fascinating look at his own setup and he is running His product management entirely on AI agents.
• 1:43
  Speaker 0: I saw that, i read his breakdown And the tech stack He chose actually really surprised me.
• 1:48
  Speaker 1: right because The brain of his operation isn't some I don't know hyper expensive new enterprise sauce platform.
• 1:53
  Speaker 1: It's literally an obsidian vault.
• 1:55
  Speaker 0: wait plain text.
• 1:56
  Speaker 0: That sounds almost like a step backward At first glance doesn't it?
• 1:58
  Speaker 1: It really does.
• 1:59
  Speaker 1: my media thought was just how on earth does a folder of plain text files orchestrate a massive complex engineering build in JIRA.
• 2:08
  Speaker 1: Right,
• 2:08
  Speaker 0: but the mechanism he uses is actually brilliant.
• 2:12
  Speaker 0: every note, every customer signal.
• 2:14
  Speaker 0: Every roadmap decision just lives in that vault as plain
• 2:17
  Speaker 0: text.".
• 2:18
  Speaker 1: Okay so it's all centralized?
• 2:19
  Speaker 0: Yeah and then he runs a primary product management agent—he calls it an orchestrator —that monitors that text.
• 2:25
  Speaker 0: So when he updates the decision of the Vault The Orchestrator reads it figures out what needs to happen And deploys sub-agents To push those Updates where the team actually works.
• 2:34
  Speaker 0: Wow!
• 2:34
  Speaker 0: Yeah...the sub agents automatically update Product Board.
• 2:37
  Speaker 0: They write tickets in Jira Sync with Figma And document.
• 2:41
  Speaker 0: I
• 2:42
  Speaker 1: mean, the time savings alone there are just... It's massive.
• 2:46
  Speaker 1: He completely bypasses that copy-paste tax, you know?
• 2:50
  Speaker 1: Yeah Where product managers spend half their day just keeping different software tools in sync?
• 2:54
  Speaker 0: Exactly.
• 2:55
  Speaker 0: But the strategic advantage is what really caught my attention here because his entire context The history and logic of the product it all lives in plain text he owns on a local machine.
• 3:05
  Speaker 0: So he was completely immune to vendor lockouts.
• 3:08
  Speaker 0: Oh!
• 3:08
  Speaker 0: That's huge.
• 3:08
  Speaker 0: Right...he isn't tied into single large language model.
• 3:12
  Speaker 0: He can use Anthropics Clawed today to write his tickets, and you know if Google's Gemini releases a better model tomorrow he just points the orchestrator at The New Engine.
• 3:21
  Speaker 0: Zero loss of context!
• 3:23
  Speaker 1: It's like building a custom nervous system for your team where you own the brain You do the high-level thinking once And the agents fire the signals out to the limbs.
• 3:32
  Speaker 1: That
• 3:32
  Speaker 0: is great way put it.
• 3:33
  Speaker 1: But this setup forces really uncomfortable realization.
• 3:37
  Speaker 1: If you have an AI agent doing logistics keeping platforms synced Writing the actual Jira tickets.
• 3:44
  Speaker 1: What happens to the product managers whose entire professional identity revolves around backlog management?
• 3:49
  Speaker 0: Yeah, Brian Dunn called this out directly.
• 3:51
  Speaker 0: He argued that AI is systematically exposing what he calls The feature factory a product manager
• 3:56
  Speaker 1: of the future Factory.
• 3:57
  Speaker 1: yeah think
• 3:57
  Speaker 0: about how many companies over the last decade used PMS essentially as just project administrators right.
• 4:03
  Speaker 0: their whole job was keeping the backlog clean running sprint ceremonies translating stakeholder wants into bite-sized tasks for engineers.
• 4:11
  Speaker 1: Well, and that model made perfect sense when engineering time was your absolute scarcest resource.
• 4:16
  Speaker 1: You needed a human shield to protect the developer's time.
• 4:20
  Speaker 0: Exactly!
• 4:20
  Speaker 1: For anyone listening who hasn't had the pleasure... The classic product requirements document-the PRD.
• 4:27
  Speaker 1: It is usually this thirty page blueprint detailing every single edge case before an engineer even allowed to write a line of code
• 4:34
  Speaker 0: Right because Human Coding Was Slow And Expensive.
• 4:36
  Speaker 0: But Akash Gupta points out That This Entire Pipeline Is basically collapsing now.
• 4:42
  Speaker 0: Well, the traditional sprint operating model which you know became default standard for a generation of tech companies it's rapidly becoming legacy infrastructure like why write a thirty page PRD to authorize build?
• 4:54
  Speaker 0: that will take weeks.
• 4:55
  Speaker 0: when an AI agent can build working prototype exact feature in thirty minutes?
• 5:01
  Speaker 1: Oh wow so this sequence completely inverts.
• 5:03
  Speaker 1: then You built at first and evaluated second
• 5:05
  Speaker 0: Precisely.
• 5:06
  Speaker 0: Gupta noted that at OpenAI's Codex team, they actually ship maybe two out of every ten things.
• 5:11
  Speaker 0: They build.
• 5:12
  Speaker 0: the rest just gets thrown away
• 5:14
  Speaker 1: because being wrong simply isn't expensive.
• 5:17
  Speaker 0: Exactly so.
• 5:18
  Speaker 0: if that slow human pipeline disappears The product manager job has to evolve right from managing execution to managing intelligence and
• 5:25
  Speaker 1: Mo Ali brought some really critical nuance To exactly what?
• 5:29
  Speaker 1: That evolution looks like Because building AI products Isn't just taking a legacy web app in duct-taping a chatbot to the home page.
• 5:36
  Speaker 0: Yeah, we've seen enough of those right.
• 5:38
  Speaker 1: Ali explains that The new job is about shaping probabilistic behavior.
• 5:42
  Speaker 1: Traditional software was deterministic like if you user clicks button A the system does action B every single time.
• 5:49
  Speaker 1: sure
• 5:49
  Speaker 0: very predictable
• 5:51
  Speaker 1: But AI systems are probabilistic.
• 5:53
  Speaker 1: they guess the best response.
• 5:55
  Speaker 1: They can hallucinate, they could lose context or they can confidently execute the completely wrong task.
• 6:00
  Speaker 0: Which means the levers you pull to manage product change entirely?
• 6:04
  Speaker 0: Yeah
• 6:04
  Speaker 1: exactly!
• 6:05
  Speaker 1: Ollie highlighted several but let's look at how actually work in practice.
• 6:09
  Speaker 1: Take memory for example.
• 6:10
  Speaker 1: OPM now has design what this system remembers about a user's context and almost more importantly What it forgets.
• 6:18
  Speaker 1: So doesn't hallucinate previous workflow into totally new session.
• 6:23
  Speaker 1: Or look tools.
• 6:24
  Speaker 1: You have to define exactly which APIs the AI is allowed to touch.
• 6:29
  Speaker 1: If the AI hallucinates a command, you need to ensure that tools guardrails prevent it from say mass-deleting a client's database.
• 6:38
  Speaker 1: Right.
• 6:38
  Speaker 0: so your trying to govern system that thinks rather than just follows script
• 6:43
  Speaker 1: Exactly and challenge of governance brings us massive bottleneck.
• 6:47
  Speaker 0: Yeah because if AI agents are compressing execution time and making it essentially free to build features fast.
• 6:53
  Speaker 0: The blast radius of any mistake becomes exponentially larger.
• 6:56
  Speaker 1: Oh, definitely
• 6:57
  Speaker 0: if a team builds the wrong thing or deploys a misaligned workflow that damage happens instantly And at a massive scale
• 7:03
  Speaker 1: which makes how product organizations govern themselves?
• 7:10
  Speaker 1: Denise Tills pointed out that a lot of large tech companies are making really dangerous miscalculation right now.
• 7:15
  Speaker 0: What do they doing?
• 7:16
  Speaker 1: They're using the hype around AI as an excuse to lay off their product operations teams.
• 7:21
  Speaker 1: The executive assumption seems like, well... AI agents can handle all cross-team coordination so we don't need ops.
• 7:28
  Speaker 0: Oh wow!
• 7:29
  Speaker 0: Tills calls this massive failure imagination Right.
• 7:32
  Speaker 1: She does.
• 7:32
  Speaker 1: yeah and I'm incredibly inclined to agree with her.
• 7:35
  Speaker 1: Cutting product ops while simultaneously arming every single developer with AI coding assistance.
• 7:41
  Speaker 1: It's like upgrading your city public transit to hyper speed bullet trains, but firing all the air traffic controllers.
• 7:49
  Speaker 0: Yeah, the trains are moving much faster but collisions at intersections will be catastrophic.
• 7:54
  Speaker 1: Exactly!
• 7:54
  Speaker 1: When every individual squad can ship code faster analyzing cross-impact across a whole platform gets harder.
• 8:02
  Speaker 1: more teams move higher speeds means things break through the seams.
• 8:06
  Speaker 0: Tills argues that product ops needs to step into heavier role right now.
• 8:10
  Speaker 1: Right they have to own clean data layer because your internal AI is totally useless if proprietary data it learns from or outdated.
• 8:20
  Speaker 1: Product ops also needs to govern the AI stack itself, you know deciding which agents are allowed to interact with which
• 8:25
  Speaker 1: systems.".
• 8:26
  Speaker 0: Right and Shuresh Vedal built on this exact point focusing specifically on enterprise trust.
• 8:31
  Speaker 0: he argued that governance in trust cannot be treated as an end-of-the line compliance checklist anymore.
• 8:37
  Speaker 0: yeah...you
• 8:37
  Speaker 1: can't just build an AI feature Handed to legal right before launch.
• 8:42
  Speaker 0: Exactly trust has to be embedded directly into the software development lifecycle from day one.
• 8:48
  Speaker 0: He gave a very practical scenario for this too.
• 8:50
  Speaker 1: Oh, was it?
• 8:51
  Speaker 0: Imagine a grocery store worker using an AI agent to automatically pick up and open shift or A payroll leader using AI to proactively flag compliance risks Before processing payroll.
• 9:04
  Speaker 1: Okay, high-stakes stuff.
• 9:05
  Speaker 1: Very
• 9:06
  Speaker 0: high stakes for those workflows to function.
• 9:08
  Speaker 0: the AI needs real time access to highly sensitive data.
• 9:12
  Speaker 0: It needs hard coded guardrails around labor laws and budget limits.
• 9:15
  Speaker 1: Right.
• 9:15
  Speaker 1: so if you don't build that trust in explainability into the platform layer from The very beginning enterprise buyers will just never let your AI touch their core operations
• 9:24
  Speaker 0: Exactly.
• 9:25
  Speaker 0: And Shadab Q shared a framework That really contextualizes where companies are struggling with this.
• 9:30
  Speaker 0: he breaks AI transformation Into three distinct stages.
• 9:34
  Speaker 1: Okay, let's hear them.
• 9:35
  Speaker 0: Most companies are stuck in stage one which is basic AI optimization.
• 9:39
  Speaker 0: this Is where they just use AI to do legacy tasks a little bit faster.
• 9:43
  Speaker 0: It's the classic you know adding a chatbot as decoration.
• 9:46
  Speaker 0: move right?
• 9:47
  Speaker 1: Just using new technology to run the exact same old playbook
• 9:50
  Speaker 0: exactly.
• 9:50
  Speaker 0: Stage two is a genetic transformation where AI systems actually start coordinating workflows across different tools, much like Brandon Popstein's obsidian setup we talked about earlier.
• 10:01
  Speaker 1: Yeah that makes sense
• 10:02
  Speaker 0: but the real goal is stage three autonomous business transformation.
• 10:07
  Speaker 0: this is where the core business model fundamentally evolves because AI changes what is economically possible and you rearchitect your work flows from the ground up to take advantage of it.
• 10:18
  Speaker 1: I mean, here's the trap with that.
• 10:19
  Speaker 1: If you actually reach that stage and your AI agents are building it deploying at light speed You're gonna run out of runway incredibly fast if you don't know exactly what target you're aiming
• 10:29
  Speaker 0: at.
• 10:30
  Speaker 0: Oh absolutely
• 10:31
  Speaker 1: It makes strategy The only bottleneck...if execution is cheap deciding What to build?
• 10:36
  Speaker 1: Is the whole ballgame?
• 10:37
  Speaker 0: And Swarish Kamath shared a story That perfectly captures how teams get this wrong and How continuous discovery gets it right.
• 10:44
  Speaker 0: He calls the Plovdiv story.
• 10:46
  Speaker 1: The Plovdev Story, I love this one!
• 10:49
  Speaker 0: Right?
• 10:49
  Speaker 0: so a customer submits a request to a software platform asking for a bus ticket from Sofia To PlovDiv which is a city in Bulgaria.
• 10:57
  Speaker 0: now A traditional feature factory team gets that request.
• 11:00
  Speaker 0: They refine the ticket estimate the engineering hours and they ship a bus ticketing feature.
• 11:05
  Speaker 1: Well i mean they gave the user exactly what they asked For?
• 11:07
  Speaker 0: They did.
• 11:08
  Speaker 0: but a team utilizing continuous discovery asks why...they ask the customer Why do you need the Bus Ticket?
• 11:14
  Speaker 0: The customer replies, I need to travel to Plovdiv to buy a specific book.
• 11:18
  Speaker 1: Okay so the team digs deeper Right
• 11:21
  Speaker 0: Why?
• 11:22
  Speaker 0: To prepare for an upcoming lecture.
• 11:24
  Speaker 0: Then the team asks about pain points of this whole process And the customer explains that carrying heavy books is terrible.
• 11:31
  Speaker 0: The commute wastes hours study time and they're on tight deadline.
• 11:34
  Speaker 1: So core problem isn't transportation at all It's knowledge acquisition
• 11:39
  Speaker 0: Precisely.
• 11:40
  Speaker 0: And once they uncover the actual need, the solution evolves rapidly.
• 11:44
  Speaker 0: It goes from a bus ticket to offering a courier service to deliver the book... ...to offering a digital copy of the book….
• 11:51
  Speaker 0: …to providing an audiobook so that it can listen while doing other
• 11:54
  Speaker 0: things.".
• 11:55
  Speaker 0: And
• 11:55
  Speaker 1: finally right?
• 11:55
  Speaker 1: The ultimate solution!
• 11:57
  Speaker 1: Providing in AI-generated summary of the audiobook.
• 12:00
  Speaker 1: Exactly
• 12:00
  Speaker 0: —it's the exact same user.
• 12:02
  Speaker 0: but because the team kept digging into the why—the outcome radically shifted from optimizing travel route delivering the required knowledge.
• 12:11
  Speaker 1: It just goes to show customers consistently express wants with discovery uncovers needs, right?
• 12:17
  Speaker 1: If you stop at the bus ticket You burn engineering cycles Optimizing a commute for someone who honestly never actually wanted to travel in the first place.
• 12:25
  Speaker 1: So how do you practically stopped teams from building The Bus Ticket?
• 12:28
  Speaker 0: Well, Graham McNichol offered A tactical approach For this With the Hay-Em framework.
• 12:32
  Speaker 0: Hayam stands for Hypothesis Actions Metrics MVP.
• 12:35
  Speaker 0: It's basically a structured way to force yourself to validate an idea.
• 12:39
  Speaker 0: before writing a single line of production code, you have to clearly state your hypothesis define what user actions would prove it true set the metrics to track those actions and build the smallest possible MVP detestant.
• 12:53
  Speaker 1: What I really like about his approach is Emphasis on the painted door test.
• 12:58
  Speaker 1: Yes, like instead of spending two months building a back end for new Analytics dashboard You just put a button on the user interface that says New analytics dashboard.
• 13:07
  Speaker 0: It's so simple
• 13:07
  Speaker 1: right.
• 13:08
  Speaker 1: if your user clicks it They get a polite message saying The feature is in beta and asking If they want to join the waitlist.
• 13:14
  Speaker 1: That takes what one afternoon to build?
• 13:21
  Speaker 0: And Baizia Kubica had some incredible advice on that exact concept of product sense.
• 13:27
  Speaker 0: You know, people tend to treat product-sense like it's this magical intuition only Steve Jobs has right?
• 13:32
  Speaker 1: Like you're just born with it!
• 13:33
  Speaker 0: But she breaks down as a highly systematic
• 13:36
  Speaker 1: loop.
• 13:36
  Speaker 0: First...you set up an automated system to pull customer signals from every channel support tickets sales call transcript slack channels
• 13:44
  Speaker 1: Okay.
• 13:44
  Speaker 0: Then the Crucible step
• 13:46
  Speaker 1: You
• 13:46
  Speaker 0: map those qualitative complaints directly against your quantitative usage
• 13:51
  Speaker 1: data.
• 13:52
  Speaker 1: So if ten customers tell sales that the onboarding process is confusing, you don't just take their word for it?
• 13:58
  Speaker 0: Exactly!
• 13:59
  Speaker 0: You cross-checked that against your analytics.
• 14:01
  Speaker 0: If the data shows a forty percent drop off at step three of onboarding... ...you have verified the problem Right
• 14:08
  Speaker 1: And score confidence based on single count.
• 14:11
  Speaker 1: If a stakeholder demands a feature but zero customers have mentioned the pain point and that data shows absolutely no friction, your confidence score is zero.
• 14:20
  Speaker 1: It removes the emotion from
• 14:21
  Speaker 1: prioritization.".
• 14:22
  Speaker 0: And finally once you do build a solution her system requires linking the shipped feature back to the original customer who flagged this problem so sales can immediately close the loop.
• 14:32
  Speaker 1: That's just phenomenal for finding it right!
• 14:34
  Speaker 1: But let me throw a wrench in here….
• 14:36
  Speaker 1: Let say you run the perfect discovery loop….
• 14:39
  Speaker 1: You use the ALM framework, you avoid the bus ticket trap and find an absolute perfect problem to solve.
• 14:47
  Speaker 1: How do you actually convince the executive team fund this build?
• 14:52
  Speaker 0: Sherdal Mehta addressed that exact friction point.
• 14:55
  Speaker 0: He argued product leaders simply have to learn how speak
• 14:59
  Speaker 1: revenue.
• 15:00
  Speaker 0: We get so enamored with our own craft.
• 15:02
  Speaker 0: we talk about customer obsessions, seamless user journeys But executives do not speak in features, they speak in margin growth retention and cash flow.
• 15:12
  Speaker 1: Yeah you have to map the product work directly to the corporate P&L scoreboard.
• 15:17
  Speaker 1: You cannot walk into a boardroom just say funding this AI feature will really reduce friction on user experience.
• 15:23
  Speaker 0: They'll tune out
• 15:24
  Speaker 1: Exactly!
• 15:25
  Speaker 1: You have to translate it.
• 15:26
  Speaker 1: This solution eliminates workflow bottleneck that our data shows is responsible for two percent churn rate Fixing.
• 15:33
  Speaker 1: it preserves four million dollars in annual recurring revenue.
• 15:37
  Speaker 0: Yes, because if you cannot connect the discovery to the business outcome You will be completely locked out of the room where the actual strategic decisions are made.
• 15:45
  Speaker 1: Okay So let's follow this through... And the sauce landscape it is entering, is completely warping right beneath us.
• 16:02
  Speaker 1: It
• 16:02
  Speaker 0: really a totally different battlefield now.
• 16:05
  Speaker 0: Taylor Dank made prediction about this.
• 16:07
  Speaker 0: that's frankly going to make people uncomfortable.
• 16:10
  Speaker 0: He stated within two years UI and UX won't even matter.
• 16:18
  Speaker 0: Wow
• 16:19
  Speaker 1: I know if you were an UI designer listening.
• 16:21
  Speaker 1: His underlying logic is actually very hard to dismiss.
• 16:27
  Speaker 0: It really is!
• 16:28
  Speaker 0: For the last decade, SaaS companies competed by building cleaner dashboards and prettier interfaces.
• 16:33
  Speaker 0: but Denk points out that visual software interfaces are rapidly being replaced by agentic infrastructure.
• 16:39
  Speaker 1: Can you explain it a bit more?
• 16:40
  Speaker 0: Yeah he used his own team as an example.
• 16:43
  Speaker 0: they use linear for issue tracking almost never login to the actual linear web application anymore, they just type requests into Slack and an AI model like Claude processes it.
• 16:54
  Speaker 1: Ah!
• 16:54
  Speaker 1: The enabler here is something called the Model Context Protocol or MCP right?
• 16:58
  Speaker 0: Exactly which sounds incredibly dense I know but think of the MCP as a universal translator.
• 17:05
  Speaker 0: It allows An AI model to securely plug directly in to API tools like Slack GitHub or Salesforce.
• 17:11
  Speaker 1: So the AI can pull data and execute commands without ever needing to look at the visual interface designed for humans.
• 17:17
  Speaker 1: The interface becomes completely
• 17:19
  Speaker 1: invisible.".
• 17:20
  Speaker 0: Yes, And if the interface becomes invisible... ...the entire way a software company wins market share changes.
• 17:25
  Speaker 0: A dichasanthanum calls this the Netflix moment for sauce.
• 17:29
  Speaker 0: Buyers are suffering from severe subscription fatigue.
• 17:32
  Speaker 0: They're exhausted by managing hundred different point solutions with one hundred different logins.
• 17:38
  Speaker 0: Right Because of that Software is rapidly consolidating.
• 17:42
  Speaker 0: The next era of sauce won't be won by the company with the deepest, most niche feature set.
• 17:48
  Speaker 0: it will be one By the platforms that command the attention control the distribution and own the core customer relationship.
• 17:55
  Speaker 1: It's like opening the best restaurant in the world.
• 17:58
  Speaker 1: You know you have Michelin star chefs?
• 17:59
  Speaker 1: The food is unbelievable but Your building has no front door and you weren't listed on any map.
• 18:05
  Speaker 1: Exactly!
• 18:06
  Speaker 1: If a user is just asking an AI agent in Slack to complete the task, And that agent uses an API to do it The depth of your beautifully designed standalone app doesn't save you.
• 18:17
  Speaker 1: You have been abstracted away
• 18:19
  Speaker 0: Which really highlights a harsh truth that Laura Anderson brought up.
• 18:22
  Speaker 0: She noted that products rarely fail, it is commercialization that fails.
• 18:26
  Speaker 1: Oh!
• 18:26
  Speaker 1: That's great point.
• 18:27
  Speaker 0: You can have brilliant engineering team that delivers flawlessly and product managers who prioritize physically.
• 18:32
  Speaker 0: But if the sales team doesn't know how to lead with new capabilities And customer success does not know position it to prevent churn User adoption will just flat line.
• 18:41
  Speaker 1: And her critical insight is that AI cannot solve this for you.
• 18:45
  Speaker 0: Exactly!
• 18:46
  Speaker 0: AI can write your code and synthesize user data, but AI cannot build organizational alignment across revenue teams.
• 18:53
  Speaker 0: It cannot look a buyer in the eye to build market trust.
• 18:57
  Speaker 0: Commercialization remains a distinctly human team sport.
• 19:01
  Speaker 1: And the massive part of winning that team sport is how you position yourself in a crowded market.
• 19:06
  Speaker 1: like Christopher Godwin pointed out, when great BB SaaS products lose deals it's almost never because they had your functionality compared to the winner.
• 19:16
  Speaker 0: Let me guess, they lose deals because they have generic positioning?
• 19:19
  Speaker 1: Exactly!
• 19:19
  Speaker 1: It's the classic We Help Enterprise team scale efficiently pitch.
• 19:23
  Speaker 0: Oh yeah we've all heard that one.
• 19:25
  Speaker 0: Right.
• 19:26
  Speaker 1: If you're website or pitch deck and sales emails sound identical with five other vendors in your category The buyer brain just tunes it out.
• 19:33
  Speaker 1: They can't distinguish their unique value.
• 19:36
  Speaker 0: And here is a mechanical reality of buying behavior.
• 19:38
  Speaker 0: right When buyers are confused defaults for safety They either pick the cheapest option available or they picked the legacy brand name, they heard of first.
• 19:47
  Speaker 0: It wasn't the product that killed the deal it was the failure to distinctly position it
• 19:51
  Speaker 1: which brings us To a really fascinating endpoint.
• 19:53
  Speaker 1: I mean we have traced how AI is automating The logistics of product management turning strategy into the ultimate bottleneck and shifting the market battlefield away from visual interfaces toward commercialization And platform distribution.
• 20:10
  Speaker 0: Yeah, but if we take Tyler Dank's prediction to its logical conclusion that AI agents will replace traditional interfaces and handle user workflows via APIs.
• 20:19
  Speaker 0: We are rapidly approaching a surreal threshold.
• 20:22
  Speaker 1: What do
• 20:24
  Speaker 0: you mean?
• 20:34
  Speaker 1: That is a staggering thought.
• 20:35
  Speaker 0: Right, how do you design user experiences?
• 20:37
  Speaker 0: How to market your value proposition and price software when both the buyer and day-to-day user are an algorithm?
• 20:44
  Speaker 1: Yeah that's what tech industry accelerates towards And it basically changes everything
• 20:49
  Speaker 0: Something to chew on next time.
• 20:50
  Speaker 0: you're grooming backlog.
• 20:54
  Speaker 1: Definitely If you enjoyed this episode.
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