Best of LinkedIn: Digital Products & Services CW 14/ 15

00:00:00: This episode is provided by Thomas Allgaier and Frennus based on the most relevant LinkedIn posts about digital products in services, calendar weeks fourteen and fifteen.

00:00:09: Frenness is a B to B market research company that supports enterprise product teams with building feature-by-feature competitive intelligence that shows exactly how their product stacks up against competition.

00:00:19: You can find more info in description.

00:00:22: Welcome to the deep dive everyone.

00:00:23: we've got A lot of ground to cover today.

00:00:26: To give you a trajectory where we're heading, We are going to explore how the AI native product workflow is fundamentally shifting The day-to-day reality of the Product Manager role.

00:00:35: Then will dive into why this massive increase in execution speed Is actually making product discovery incredibly dangerous And finally Will unpack those hidden operating model flaws and structural issues Nobody really wants talk about.

00:00:46: that Are costing tech companies serious money?

00:00:49: Right, exactly.

00:00:49: Because I mean imagine finding out that the shiny new AI feature your product team just rushed at the door has double the failure rate of traditional software like double.

00:01:00: and what if the reason it failed isn't a technology at all but simple fact?

00:01:04: you built absolute wrong thing record speed?

00:01:08: So our mission today is cutting through theoretical hype to look at what real product teams in the ICT and tech industry are actually doing in the trenches.

00:01:18: Because

00:01:19: if you're listening to this, You already know that conversation is shifting from like What Is AI?

00:01:23: To hardcore daily execution.

00:01:26: We need to separate signal from noise.

00:01:28: Exactly So.

00:01:28: let's start right on the desk of modern product manager Everyone talking about AI But i really want to.

00:01:34: how the daily workflow changing for you & me Beyond just asking a chatbot to rewrite quick email, what does an AI native workflow look like right now?

00:01:43: Yeah.

00:01:43: That's the big question and The best window into this comes from A breakdown shared by power heron.

00:01:49: he laid out This very practical Seven-step guide To how PMs are utilizing clod code Right Now And the most striking part of his analysis is that the barrier to entry has Just it's completely collapsed.

00:02:04: Oh

00:02:04: really

00:02:05: Yeah, I mean you don't need to know how write a single line of software.

00:02:07: To use this stuff anymore.

00:02:08: okay wait How does that actually work in practice?

00:02:10: because if you aren't writing code?

00:02:12: What are you doing with the coding tool?

00:02:13: so her and suggests setting it up inside VS Code which for anyone unfamiliar is basically The digital workbench or text editor where software engineers usually right their code.

00:02:25: But instead of typing out code yourself You just treat this environment like a highly capable assistant.

00:02:31: Okay used features like plan mode.

00:02:33: So let's say you have a massive data set of customer feedback.

00:02:36: You just ask the model to analyze it.

00:02:38: in plan mode, The AI proposes a step-by-step strategy for how It wants to parse that data and then it pauses.

00:02:44: Oh so doesn't do this automatically?

00:02:46: Right exactly!

00:02:47: It waits For your human approval before it executes anything at all...

00:02:50: So essentially acting as like the manager Just reviewing battle plan Before troops move out.

00:02:57: That is perfect way To put it.

00:02:58: yeah.

00:02:58: And heron also highlights This other feature called auto memory.

00:03:02: The system actually observes your workspace patterns over time.

00:03:05: It learns how you're specific files are structured, what terminology your company uses...

00:03:10: Right it learns your companies' specific jargon.

00:03:13: Yeah exactly.

00:03:14: and Hearn actually uses to automate his market monitoring.

00:03:17: he has the system pulling emerging markets signals while he sleeps essentially tracking his product hypotheses in the background.

00:03:24: The whole PM role is shifting from, you know writing manual requirements specs to designing these automated systems.

00:03:31: I gotta play devil's advocate here though.

00:03:33: If it sounds that seamless like having an AI monitor the market while you sleep Why isn't every single product manager on earth doing this by now?

00:03:40: Well because the learning curve is highly deceptive.

00:03:43: Akash Gupta observed this fascinating pattern.

00:03:46: regarding this exact friction He noted that most PMS will try and advance AI workflow for like a day.

00:03:53: They don't get that instant magical result.

00:03:55: they saw in some demo video on LinkedIn, right?

00:03:57: The flashy

00:03:58: demos

00:03:58: exactly and so they quietly abandon it.

00:04:01: but the product managers who are actually pulling ahead of the pack?

00:04:04: Gupta says they've logged something Like fifteen hundred hours of mileage.

00:04:07: these tools wait

00:04:08: fifteen hundred hour.

00:04:09: That's almost an entire year Of full-time work.

00:04:12: just tinkering with AI workflows

00:04:14: I know requires immense persistence.

00:04:17: The gap isn't raw talent.

00:04:19: You Know deeply awkward first week where the AI is making constant mistakes because it doesn't have your company's context loaded yet.

00:04:27: So how do you actually get through that without giving up?

00:04:30: Well, Gupta pointed to Hannah Stolberg.

00:04:31: she's a PM at DoorDash and she's essentially a power user in this space.

00:04:35: her core advice is brilliant Because its so counterintuitive.

00:04:39: She says You really shouldn't sit down And try build a massively complex fully automated workflow on day one.

00:04:45: Okay

00:04:45: what should we instead?

00:04:47: Instead, you find just one single repetitive task to automate today.

00:04:51: Give me an example.

00:04:52: like what kind of tasks?

00:04:54: Something like formatting user interview transcripts or pulling a weekly metric report and might take three hours for automated tasks.

00:05:02: that usually only takes twenty minutes to do manually Which

00:05:05: feels like huge waste time initially

00:05:07: Right!

00:05:08: Feels terrible.

00:05:09: But once it's automated You've freed up six hour your free time next week.

00:05:13: Stolberg's whole philosophy is that as the actual work.

00:05:18: You take those six reclaimed hours and reinvest them into learning.

00:05:34: If I'm a PM listening to this right now, and I hear that AI is taking over the data analysis on the coding.

00:05:40: I might start feeling a little terrified about my job security.

00:05:43: like Sid Aurora mentioned that PMS at Anthropic are running their own complex big query data analyses.

00:05:49: Now they're generating fifty testing scenarios from just two examples in a matter of minutes.

00:05:54: Yeah That's happening Right?

00:05:55: So

00:05:56: if the AI Is doing all The heavy lifting Of engineering And data science doesn't the PM Become redundant?

00:06:01: it feels A bit Like you know going From a massive orchestra of developers and analysts to just playing a synthesizer all by yourself in an empty room.

00:06:10: That orchestra analogy, I mean it captures the anxiety perfectly.

00:06:14: but let's reframe the reality.

00:06:15: what is actually happening Is not about replacing the PM It´s fundamental upgrade on role.

00:06:21: Jonathan Pearson wrote really fantastic piece addressing this exact fear.

00:06:26: He argues we are entering an era called judgment velocity

00:06:30: Judgment Velocity.

00:06:31: OK break that down for me.

00:06:32: So for the last twenty years, the tech industry has basically operated on a model of scarcity.

00:06:37: The ultimate bottleneck was always engineering time right?

00:06:40: Right!

00:06:41: Getting resources is impossible

00:06:42: Exactly Because development was so slow and expensive.

00:06:46: A PM's primary job Was essentially managing the queue structuring sprints Writing tickets Keeping engineers unblocked.

00:06:53: But today we're moving into this age of abundance.

00:06:56: Building software is becoming incredibly cheap And fast.

00:07:00: So if the building part is cheap, The bottleneck shifts to what?

00:07:04: Deciding What To Build

00:07:05: Precisely.

00:07:06: In a world where you can generate working prototype in an afternoon... ...the most valuable skill is no longer managing a Jira board!

00:07:13: The most valuable skills are selectivity.

00:07:16: It's having pattern recognition.

00:07:17: build a prototype test it and definitively kill the idea after just four

00:07:22: hours.

00:07:23: Wow not because code failed but your judgement tells that market signal wasn't strong enough.

00:07:28: Exactly We need PMs who possess the instinct to build less, even when building is practically free.

00:07:34: Because When execution becomes commoditized The quality of your strategic thinking Is literally the only differentiator.

00:07:41: Your company has left

00:07:43: And we see that Strategic Thinking play out in how companies deploy the AI models themselves.

00:07:49: Jean-François L brought up an incredible point about this.

00:07:51: He highlights it having a powerful AI model It's just table stakes.

00:07:55: now Like everyone has access to the same foundational intelligence.

00:07:59: Right, anyone can buy an API key!

00:08:01: Exactly The real product decision—the place where human PMs are absolutely vital is what he calls Harness Engineering.

00:08:08: Harness engineering?

00:08:10: Meaning like the guardrails around AI.

00:08:12: Yes The harness is the product.

00:08:15: He used this wild, real-world example to illustrate it and Thropix model was use to scan some production software And It found a hundred eighty one zero day exploits.

00:08:24: Oh my god.

00:08:24: for anyone outside of cybersecurity A Zero Day exploit Is a critical vulnerability in a system that hackers can Use which the original creators have zero days to fix because they didn't even know what existed

00:08:34: right?

00:08:34: Some Of the bugs the AI Found had been hiding In the open BSD operating System For twenty seven years.

00:08:40: Right?

00:08:40: Twenty Seven Years

00:08:42: That is terrifying power to just hand over into a chatbot.

00:08:45: Exactly, and Anthropic didn't just hand that capability out everyone with an API key they gated it.

00:08:51: They heavily restricted access To critical infrastructure organizations And vetted security researchers.

00:08:57: Okay I see

00:08:58: Defining those rules.

00:08:59: who gets access?

00:09:00: Under what conditions?

00:09:01: What data's restricted?

00:09:03: What the fail safes are?

00:09:04: That harness requires profound human judgment.

00:09:08: You just cannot automate the ethical and strategic deployment of a technology that powerful.

00:09:13: Right, which actually brings us to our next major theme because That level of speed and power introduces A terrifying new problem.

00:09:21: if you can build a prototype in four hours instead Of four weeks You can also build The completely wrong thing at record speed.

00:09:27: Speed without rigorous direction is really just arriving At it very extensive mistake.

00:09:32: faster

00:09:32: yeah.

00:09:33: And your hana shot warned about a new incredibly subtle danger in product discovery that he calls synthetic certainty.

00:09:40: And honestly, if you're building anything with AI right now this should keep you up at night.

00:09:44: Walk us through synthetic certainty.

00:09:45: how does it trick teams?

00:09:47: The mechanism is entirely psychological.

00:09:49: You see Ai doesn't just generate interfaces or spit out code.

00:09:53: It generates explanations That sound incredibly convincing Its articulate structures its arguments With neat little bullet points and speaks to total authority.

00:10:03: Right, sounds like an expert.

00:10:04: Exactly!

00:10:05: So a product team asks the AI to generate user persona or explain market trend and the AI produces this beautiful logical breakdown.

00:10:14: the team reads it, and they start believing that truly understand their users.

00:10:18: But they don't actually have any underlying evidence to support it?

00:10:21: None!

00:10:22: It is a mere appearance of understanding—it's completely hallucinated confidence —the human brain sees well-formatted documents and just assumes rigorous research went into them.

00:10:31: so teams bypassed talking with actual human customers because interviewing this synthetic customer was much easier…

00:10:38: And you know... The fallout from that false confidence is showing up in market data right now.

00:10:43: General Lingus Warren shared a staggering statistic regarding this.

00:10:47: Eighty percent of AI projects currently fail, that is double the failure rate for traditional software projects.

00:10:53: Wait!

00:10:54: Double the Failure Rate?

00:10:55: Why are companies striking out so consistently with AI?

00:10:59: Because they're falling into that exact trap of synthetic certainty and skipping the discovery phase entirely... Ling just warned detail the story of an agency founder who is completely bypassing product discovery to get straight into development.

00:11:13: They were chasing this dream of launching a fourteen-day minimum viable product.

00:11:18: The classic tech obsession with speed at all costs?

00:11:21: Oh, absolutely!

00:11:22: But when you skip discovery with AI... ...you're ignoring the foundational questions.

00:11:27: You don't validate if an AI solution is even what the customer wants….

00:11:31: …You don't check if your proprietary data's actually clean enough to train a model.

00:11:34: So what happens?

00:11:36: The team spends their entire highly expensive development phase.

00:11:39: figuring out what they are supposed to be building in first place and fixing a fundamental architecture mistake in month.

00:11:45: three cost ten times more than discovering it on week one.

00:11:49: As Lingerswarm puts, the discovery phase is cheap but failed.

00:11:52: AI products are incredibly expensive.

00:11:54: Let's put ourselves into shoes of a PM facing this reality though.

00:11:59: David Pereira had an honest take as to what the pressure teams under right now.

00:12:03: He pointed out that chasing AI hype, even when it makes zero sense for your specific customer might actually get you promoted in the short term.

00:12:11: Oh for sure!

00:12:12: The CEO on board they love seeing a shiny new AI feature and press release

00:12:17: But long-term cramming AI into product where doesn't belong completely destroys user experience.

00:12:25: So how do you navigate this?

00:12:27: When the C-suite is demanding an AI launched by next quarter, how do you do real discovery without getting fired for moving too slowly?

00:12:35: Yeah.

00:12:35: It's tough!

00:12:36: You really need a framework that turns that executive pressure into rigorous testing.

00:12:41: Anisambath offered a brilliant approach to this exact scenario—you have build what could break your AI product first not with users or executives are asking for.

00:12:50: Build What Could Break it.

00:12:51: That sounds like you're actively trying to sabotage our own project.

00:12:54: Well...You're sabotaging assumptions Not the project itself.

00:12:59: In traditional software, at the biggest risk is usually whether user will click button right?

00:13:04: In AI, the riskiest assumptions are usually internal technical constraints Like what?

00:13:10: Is model accurate enough so it doesn't embarrass brand?

00:13:13: Do we even have legal rights to use training data?

00:13:16: Can system process a user query in under two seconds?

00:13:20: If you don't aggressively test those failure points in week one, they will remain hidden and absolutely sink the entire project.

00:13:40: And

00:13:45: N-Ball S wrote a fascinating piece expanding on this discipline.

00:13:49: Coming from the background as an applied scientist, she notes his huge gap between scientific capability and product reality.

00:13:57: Just because AI model can do something brilliant in controlled R&D lab doesn't mean it translates to actual real world customer value

00:14:06: Because a lab setting does not have noisy data weird edge cases confused users clicking wrong things.

00:14:12: Exactly.

00:14:13: The companies dominating the market right now aren't necessarily ones with absolute best foundational science.

00:14:19: They're the companies that deliberately build a bridge between R&D discoveries and practical customer impact, they don't just throw raw technology at wall to see what sticks...they engineer application of this technology!

00:14:31: But you know even if you manage to do everything we've discussed perfectly You master the AI native workflow.

00:14:36: You avoid the hallucination of synthetic certainty, you practice rigorous risk-first discovery.

00:14:42: There's still a massive structural trap waiting for

00:14:44: you by the operating model.

00:14:45: Yes

00:14:46: If your company's underlying operating model is fundamentally broken all those great ideas will still go to die.

00:14:52: The operating models basically the invisible engine of a company when it misfires the smartest product strategies in the world just grind to a halt.

00:15:01: Right, and

00:15:02: Mauricio Carden was observed a brutal truth across the tech industry regarding this.

00:15:07: he points out that countless companies loudly claim to be product-led in their marketing but internally they're actually just running project management organizations disguised with products titles.

00:15:17: let's unpack the difference between a project organization any product organization because it is the root cause of so much friction.

00:15:24: yeah So In A Project Led Company It's All About Shipping a Specific Deliverable on a specific date.

00:15:30: A team is hastily formed, they scramble to ship a new feature and the moment it goes live The team dissolves.

00:15:35: They rotate away into next burning initiative.

00:15:38: Right leaving no one behind.

00:15:39: Exactly!

00:15:40: There's absolutely NO ONE left behind To own that life cycle of product.

00:15:45: No One is monitoring if users actually adopted this feature over the next six months.

00:15:49: No one is iterating based on feedback.

00:15:52: It's fundamental failure of accountability.

00:15:55: You celebrate launch but ignore outcome.

00:15:58: And you see the symptoms of that confusion manifest directly in tools teams use to communicate.

00:16:03: Jerome Morleau provided a great diagnostic for this, he explained critical distinction between product roadmap and project plan.

00:16:10: People used those terms interchangeably all time

00:16:12: They do!

00:16:13: It causes total chaos.

00:16:15: A product road map is designed set strategic direction.

00:16:18: it answers where products are going over next year.

00:16:21: crucially why matters business?

00:16:24: Project Plan on other hand drives granular execution.

00:16:28: It answers how we will deliver a specific feature step-by-step by next Tuesday.

00:16:32: So what happens when leadership team treats high level road map like detailed project plan?

00:16:37: The organization becomes incredibly rigid.

00:16:40: If you demand exact delivery dates for concepts that are twelve months out, teams stop innovating because they're terrified of missing arbitrary deadlines.

00:16:49: They stopped responding to shifts in the market

00:16:51: And vice versa.

00:16:52: Exactly If you treat a tactical project plan like a vague roadmap, the team drifts.

00:16:58: They lack structure and constantly slip on immediate deliverables.

00:17:02: The top organizations understand that you don't choose between them—you integrate them.

00:17:07: You align on strategy first And that dictates execution second

00:17:11: which perfectly sets up the daily nightmare of prioritization.

00:17:15: I mean, if you've ever been in a sprint planning meeting—you know the pain of trying to prioritize the backlog!

00:17:20: You have the sales team screaming for new revenue future... The engineering team begging for time to fix technical debt and customer support just drowning in bug reports.

00:17:29: Dr Bart Dwarfsky has an incredible analogy for this — he says trying to stack-rank all those different items against each other on one master list is like asking whether you should buy cookies or wet

00:17:39: cement."

00:17:40: You literally

00:17:41: cannot

00:17:42: compare the value of cookies and what cement.

00:17:45: They serve completely, entirely different

00:17:47: purposes.".

00:17:48: But PMs try to do it every single day—and the result is always the same!

00:17:53: The shiny revenue features win the argument… And the core product slowly degrades under the weight of bugs in tech debt... So how do teams escape this trap where the loudest stakeholder ALWAYS gets their feature built?

00:18:06: Well, Jaworski proposes a structural solution — portfolio allocation.

00:18:10: You have to stop trying to stack rank the unrankable.

00:18:13: Instead, leadership needs to divide engineering capacity into strategic buckets up front.

00:18:19: Give me a breakdown of what those buckets look

00:18:20: like.

00:18:21: So for example you might decide as company that forty percent your engineering time goes new growth initiative.

00:18:26: Thirty percent go to quality improvements and bug fixes.

00:18:29: Twenty percent goes UX polish And ten percent is strictly reserved paying down technical debt.

00:18:35: Okay, so you're cordoning off resources.

00:18:37: The sales team can't just scale from the bug fix bucket to fund a new feature?

00:18:41: That is the magic of it!

00:18:42: You only prioritize within the buckets... ...you compare bugs to other bugs and new revenue opportunities to other revenue opportunities.

00:18:51: It completely changes the dynamic of stakeholder conversations.

00:18:54: How

00:18:54: so?!

00:18:55: Well instead of a PM defensively explaining why they aren't building a salesperson's pet project….

00:19:00: …the conversation shifts to business strategy.

00:19:03: If sales wants a massive new feature, they have to argue for expending the growth bucket from forty percent to fifty percent which forces leadership to consciously accept that quality will drop.

00:19:14: Right and if an organization refuses to adopt portfolio allocation then entire machine just clogs up.

00:19:20: Igor both pointed out.

00:19:21: when product teams are moving slowly executives usually assume it's lack of effort but both argues its almost always structural root cause like invisible capacity.

00:19:32: Invisible capacity is a silent killer of product velocity.

00:19:35: It really it's the illusion that you have one hundred percent Of your engineering teams time dedicated to building The roadmap but in reality fifty percent of their week Is consumed by undocumented support escalations emergency bug patches and useless status meetings.

00:19:48: yeah add-in scope creep where?

00:19:50: The original simple idea gets buried under twelve new requirements from different executives, And the team is completely paralyzed.

00:19:57: Bad strategy certainly creates the wrong work,

00:20:00: but

00:20:00: a chaotic operating model kills the team's ability to do the right work.

00:20:05: Let us talk about the literal financial cost of a Chaotic Operating Model specifically when organizational roles start blurring together.

00:20:14: Jamie Walsh brought some incredible data to the table regarding companies that force their product teams.

00:20:31: Walsh tracked the literal cost of this.

00:20:33: He found that when you force a PM to cover a PM roll for just ninety days, it costs the company between seventy-five thousand dollars and one hundred twenty thousand dollars in direct wasted time and lost sales

00:20:43: pipeline.".

00:20:44: Wait

00:20:44: walk me through why a PM fails so spectacularly at product marketing?

00:20:48: Why does it cost the company over a hundred

00:20:49: grand?".

00:20:50: Well think about where a PM spends their time!

00:20:52: They sit in sprint planning... Their brain is optimized for managing technical constraints edge cases and feature functionality.

00:20:59: A product marketer, on the other hand sits on sales calls.

00:21:03: Their brain is optimized for buyer psychology, market positioning and business outcomes.

00:21:08: Okay that makes sense.

00:21:09: So when you ask a PM to write a sales deck what do they do?

00:21:11: They write a forty-five slide presentation That reads like a technical product requirements document at PRD.

00:21:18: Oh man so instead of telling the buyer how software will save them money their explaining architecture database

00:21:24: Exactly And the buyer completely tunes out.

00:21:27: Walsh's data show.

00:21:28: the results are universally terrible.

00:21:30: Product launches are delayed by an average of forty percent because the PM is overwhelmed.

00:21:34: more importantly, The sales pipeline in those first ninety days dropped sixty percent below forecast Because the messaging so technically dense that it just fails to resonate with a market.

00:21:44: Wow!

00:21:45: It is a glaring expensive example Of how a flawed operating model asking the wrong discipline To execute the wrong job directly sabotages revenue.

00:21:55: That is a staggering cost for just ninety days of misalignment.

00:21:59: So whose job is it to fix this?

00:22:02: If the operating model is this broken, who has the power to untangle

00:22:06: it?".

00:22:06: Ultimately comes back at very top in the organization.

00:22:09: Melissa Perry made crucial observation about Chief Product Officers.

00:22:14: She pointed out that when CPOs get fired they rarely fail because lack product craft Like, they don't fail because... They don't know how to do discovery or write a roadmap.

00:22:23: Right!

00:22:23: They failed because the lack of executive relationships and boardroom dynamics required To fix these exact structural issues.

00:22:31: So soft skills are actually hardest at the executive level?

00:22:34: They're only skills that matter At this level.

00:22:36: A CPO has to be able read board dynamics.

00:22:38: They have to firmly push back on CEO's pet project Without losing respect in their room.

00:22:44: They need to partner deeply with CFO To secure funding for technical debt rather than fighting over budgets.

00:22:50: And if

00:22:50: they can't do that?

00:22:51: If a CPO cannot navigate those intricate executive relationships, it cannot implement the portfolio allocation we discussed—they cannot stop project-led feature factories and operating model remains permanently broken.

00:23:06: That is a massive amount to digest.

00:23:08: I mean we've zoomed all the way from the granular keystroke level of setting up Claude code on a PM's laptop, up through the psychological dangers of synthetic certainty and finally into the boardroom dynamics of the CPO.

00:23:21: The tools We use To build products are changing faster than ever but the fundamental need for intense strategic clarity Is only intensifying.

00:23:29: Yeah, the AI is accelerating everything which means that structural cracks in your strategy and organization will just reveal themselves much faster.

00:23:37: Absolutely!

00:23:38: As we bring this deep dive to a close do you have final thoughts on how to leave our listeners with as they go back into their product teams?

00:23:43: I

00:23:44: do – it ties us all together perfectly.

00:23:47: Earlier we briefly touched upon an observation from Nico Nall regarding how AI models actually function.

00:23:53: He noted every AI feature has a finite context budget.

00:23:57: Context Budget.

00:23:59: A model can only hold and process a specific amount of information, a certain number of tokens before it gets overwhelmed loses the plot and breaks down.

00:24:09: You have to carefully engineer what context matters

00:24:12: most.".

00:24:12: Oh that is brilliant metaphor for human attention!

00:24:15: Right?

00:24:15: so if you're listening this right now here's a provocative thought for you all over today What Is Your Personal Context Budget As Product Professional?

00:24:25: You Only Have So Much Cognitive Band With Every Week.

00:24:28: Are you filling your finite budget with the endless noise of LinkedIn hype?

00:24:32: Chasing fourteen-day MVPs and falling for The Dangerous Illusion Of Synthetic Certainty.

00:24:36: Which is so easy to do right now!

00:24:38: Exactly, or are you aggressively filtering out the noise To protect Your Context Budget?

00:24:43: because the PMs who will actually win this era are the ones deliberately making room For actual customer truths rigorous risk first discovery And sharp uncompromising judgment.

00:24:54: Protect Your Contacts Budget.

00:24:55: That's powerful.

00:24:56: Thank you so much for exploring these insights with us today!

00:24:59: If you enjoyed this episode, new episodes drop every two weeks.

00:25:03: Also check out our other editions on ICT and tech Artificial Intelligence Cloud Sustainability in Green ICT Defense Tech & HealthTech.

00:25:11: Don't forget to subscribe And keep digging deep.