Best of LinkedIn: Strategy & Consulting CW 02/ 03

00:00:00: provided by Thomas Hallgeier and Frennis based on the most relevant LinkedIn posts about strategy and consulting from CW two and three.

00:00:07: Frennis specializes in BDB market research for strategy and consulting teams with a focus on tech and ICT.

00:00:15: Welcome back to the deep dive.

00:00:17: So today we are digging into strategy and consulting.

00:00:20: for what is it?

00:00:21: calendar weeks two and three of twenty twenty six.

00:00:25: And you know if I had to put a single theme on it.

00:00:28: It feels like the lights just came on at the end of a very long, very loud party.

00:00:33: That's a great way to put it.

00:00:33: And when the lights come on, that's when you see the mess you didn't notice when the music was playing.

00:00:37: Exactly.

00:00:37: The music stopped.

00:00:38: Everyone's looking at the debris.

00:00:40: And the big theme we're seeing across LinkedIn is this massive shift away from ambition narratives, you know, the shiny promises to just operational truth.

00:00:49: A

00:00:49: move from what could be... to what is actually broken right now.

00:00:52: And we're not just talking about high level trends here, we're putting on some pretty harsh realities.

00:00:56: We've got companies posting jobs, they have zero intention of filling.

00:00:59: We have CEOs starting to kill AI pilots instead of celebrating them.

00:01:03: And a consulting industry being told to just, well, stop improvising.

00:01:07: It's a reality check across the board, talent tech, you name it.

00:01:10: Okay, so let's unpack that.

00:01:11: I think we have to start with the most

00:01:13: sort of

00:01:13: human part of this problem.

00:01:15: Ghost jobs.

00:01:16: Oh, this is fascinating and frankly a little cynical.

00:01:20: We're looking at a post from made to stare of it who really you know pulls the curtain back on.

00:01:25: this whole practice

00:01:26: pulls the curtain back as an understatement.

00:01:28: I mean he basically says a huge number of those now hiring posts are just Theater.

00:01:35: they're not about recruitment.

00:01:36: They're marketing.

00:01:37: it's performative hiring.

00:01:38: and made breaks it down into three main reasons why companies do this.

00:01:42: and while none of them are We need to hire someone today.

00:01:46: Right.

00:01:46: First one he lists is pipelining.

00:01:47: So just building a database for a role they might open in what, six months?

00:01:52: Which

00:01:52: is so frustrating if you're an applicant.

00:01:54: It's basically data harvesting.

00:01:56: But the second reason is even more deceptive.

00:01:59: Investor perception.

00:02:00: The illusion of scale.

00:02:02: Precisely.

00:02:03: You look like you're growing if you're always hiring.

00:02:05: It signals health to shareholders.

00:02:07: I mean, if you're a consultancy with zero open roles, the market wonders if you're stagnant, so you post jobs just to look alive.

00:02:14: It's market manipulation using the careers page.

00:02:16: Wild.

00:02:17: And the third one, internal benchmarking.

00:02:20: That just sounds like a way to keep current employees on their toes.

00:02:23: That's part of it.

00:02:24: See what the talent pool looks like.

00:02:26: but made to Derevich didn't just point out the problem.

00:02:29: He gave some really solid actionable advice.

00:02:32: He calls it the thirty day expiry

00:02:35: rule.

00:02:35: I love this.

00:02:36: Such a simple filter.

00:02:37: If a job's been up for more than a month and has like five hundred plus applicants, it's a lead magnet.

00:02:43: It's a lead magnet, not a job.

00:02:45: move on.

00:02:46: And he adds another check, which I think is genius.

00:02:48: Go to the people tab on the company's LinkedIn.

00:02:50: If they haven't actually added headcount in that department recently, that rolls a ghost.

00:02:55: But this isn't just an HR problem, right?

00:02:57: There's a much deeper strategic risk here.

00:02:59: A huge one.

00:03:01: If your hiring is performative, or if you only hire for culture fit, you get what Frank Powers calls cultural cloning.

00:03:07: Cultural cloning.

00:03:09: It sounds like something out of a sci-fi movie.

00:03:11: It's a corporate horror movie.

00:03:12: Frank Powers argues that culture fit is often just a code word for hiring people who look, think, and act just like everyone already there.

00:03:20: And that feels safe, but it absolutely murders innovation.

00:03:23: Because it's a echo chamber.

00:03:25: Exactly.

00:03:26: And that ties into a brilliant point by Zabeen Hirji.

00:03:29: She says HR can't just be adjacent to strategy anymore.

00:03:33: Right, it can't just be the department processing the ghost jobs.

00:03:36: It has to be the core.

00:03:37: If your talent strategy is just for show, your whole business strategy is built on sand.

00:03:42: You can't fake growth forever.

00:03:45: And you definitely can't fake innovation, which I think is a perfect segue to our next big theme, the AI reality check.

00:03:51: Oh, yes, the AI ambition narrative is really crashing into a wall.

00:03:55: In twenty twenty four, twenty twenty five, it was all about the cool factor, right?

00:03:59: Look at this cool thing the bot can do.

00:04:00: But now.

00:04:01: The cool factor is dead.

00:04:03: All that matters is the ROI factor.

00:04:05: We saw some brutal stats on this from Mark Byershoder.

00:04:08: He surveyed what, thirty-two hundred executives?

00:04:10: And the numbers are just stark.

00:04:12: Employee access to AI tools is up fifty percent.

00:04:15: So

00:04:15: the tools are out there.

00:04:16: The tools are everywhere.

00:04:18: But only twenty-five percent of companies have actually moved an experiment into production.

00:04:23: That's a massive gap.

00:04:24: So everyone's playing in the sandbox, but nobody is building the castle.

00:04:28: And here's the key insight from Byershoder.

00:04:31: The companies that are winning aren't doing more pilots.

00:04:34: They're doing fewer.

00:04:35: They're killing pilots faster.

00:04:36: I love that.

00:04:37: It's so aggressive.

00:04:38: If it doesn't pass the integration or security tests, just kill it.

00:04:42: Stop celebrating the experiment.

00:04:44: Demand the outcome.

00:04:45: But that creates a ton of tension in the C-suite.

00:04:48: Neha Cabra writes about this battle between the CFO and the CIO.

00:04:52: The classic showdown.

00:04:53: The CFO was saying, slow down.

00:04:55: Show me the value.

00:04:56: And the CIO is yelling, move now or we die.

00:04:59: And the CEO is stuck in the middle.

00:05:01: But Neha Cabra brings up a great point from Bob Sternfels at McKinsey.

00:05:05: He says, it's not a vote.

00:05:07: You don't pick one over the other.

00:05:08: The answer is, org rewiring.

00:05:10: The mortgage journey example, right?

00:05:12: Yes, it's perfect.

00:05:13: Getting a mortgage traditionally passes through like five different departments.

00:05:17: WISC,

00:05:18: legal, credit.

00:05:19: If you just sprinkle some AI on each of those silos, you don't get much.

00:05:23: The process itself is still broken.

00:05:24: Exactly.

00:05:25: Sternfeld's point is you have to break down the walls between those departments.

00:05:29: The tech has to flow end to end.

00:05:30: That's the rewiring.

00:05:31: You have to change how you work.

00:05:33: And speaking of costs, Jim Rowan introduced a term that I think is going to be huge, tokenomics.

00:05:39: It sounds like crypto, but it's becoming critical for any CIO.

00:05:43: Tokens are the currency of AI.

00:05:46: Every single prompt costs money.

00:05:48: And those costs are volatile.

00:05:50: So CIOs can't just be technologists anymore.

00:05:52: Nope.

00:05:53: They have to think like CFOs.

00:05:55: They need to understand the unit economics of a prompt.

00:05:57: If you're burning tokens on something that doesn't create value, you are literally burning cash.

00:06:02: Which perfectly sums up what Vinci and Bo Sheen said.

00:06:05: Her wish for twenty twenty six was less experimentation, more transformation.

00:06:09: The tech isn't the problem anymore.

00:06:11: We are.

00:06:11: And that transformation is leading to a specific kind of AI.

00:06:15: We're moving from just chatbots to agentic AI.

00:06:19: Okay.

00:06:19: Theme three.

00:06:20: agentic AI.

00:06:22: For anyone listening, this isn't AI that just talks to you.

00:06:25: This is AI that does things.

00:06:27: It executes entire workflows.

00:06:29: Yulia D from SAP had a perfect use case for this.

00:06:32: They tested cash and accruals agents in finance.

00:06:35: Sounds incredibly dry, but the concept is huge.

00:06:38: It is.

00:06:39: The shift, as Yulia puts it, is we state the goal, the agent finds the solution.

00:06:44: So

00:06:44: instead of me clicking ten buttons to move cash, I just tell the agent cover this shortfall and it just

00:06:50: It figures it out,

00:06:51: but,

00:06:52: and this is the most critical part, tying back to our theme trust comes from auditability.

00:06:56: It can't be a black box.

00:06:57: Never.

00:06:58: If that agent moves a single dollar, it has to leave a detailed log of every step it took.

00:07:03: It has to be a verifiable workflow.

00:07:05: You have to be able to audit the robot.

00:07:07: That makes perfect sense.

00:07:08: But building these things sounds incredibly expensive, retraining a whole model for your company.

00:07:13: It used

00:07:13: to be, but Pascal B shared a really clever framework.

00:07:16: He calls it large model, small sub agents.

00:07:19: Okay, break that down.

00:07:20: The idea is you don't retrain the massive, expensive foundation model.

00:07:25: You keep that frozen.

00:07:26: The big brain stays the same.

00:07:27: The big brain that understands language and logic stays put.

00:07:31: And you train these small, cheap sub agents around it.

00:07:35: One might be an expert searcher of your internal database.

00:07:38: Another is an expert at planning.

00:07:40: He's

00:07:40: like a brilliant CEO with a team of hyper-specialized interns.

00:07:44: Great analogy.

00:07:45: And Pascal says it's seventy times more data efficient.

00:07:48: It's cheaper, faster, easier to update.

00:07:50: And Leanne Allen and Holger Harris are saying this is going mainstream.

00:07:54: We're moving from AI doing a task to AI delivering an outcome.

00:08:00: Which demands a whole other level of reliability from your infrastructure.

00:08:03: Right.

00:08:04: And that brings us to the backbone of it all, theme four.

00:08:07: Cloud resilience and systems thinking.

00:08:10: Gilvin and Boom had a line that stuck with me.

00:08:12: He said running cloud without guardrails is like driving without brakes.

00:08:15: That's

00:08:15: terrifying.

00:08:16: But it's true.

00:08:17: He's saying cloud resilience isn't a nice to have anymore.

00:08:20: You have to build in robust risk frameworks from day one.

00:08:23: And Patrishmucky takes this idea even higher.

00:08:26: He says we need to treat strategy itself as a complex adaptive system.

00:08:30: This is deep systems thinking.

00:08:33: In a complex system, the risk isn't one thing failing.

00:08:37: The risk is in the connections between things, the network.

00:08:41: Tristan Oblinger connected this to the Cinefin framework, which I know you love.

00:08:44: I do.

00:08:45: And his point is so important for twenty twenty-six.

00:08:48: He says, stop using best practices for complex problems.

00:08:52: What's the practical difference there?

00:08:54: A best practice?

00:08:55: works for simple problems where there's a known right answer like beaking a cake.

00:09:01: But for a complex problem like navigating a volatile market or integrating AI, there is no manual.

00:09:06: The manual hasn't been written.

00:09:08: So you need a different method.

00:09:09: You have to run experiments.

00:09:10: Probe, sense, respond.

00:09:13: You test things to find what works.

00:09:15: You don't look for the correct answer in a book.

00:09:17: Okay.

00:09:17: So on one hand, we have Oblinger saying experiment.

00:09:20: But then we get this truth bomb from Luke Smyers about consulting.

00:09:24: that seems to say the opposite.

00:09:25: This one probably heard a few people to read.

00:09:27: Smyers just says it flat out.

00:09:28: Improvisation is the root cause of profit erosion.

00:09:32: So how do we square that?

00:09:33: Experiment but don't improvise.

00:09:35: It sounds like a contradiction but it's not.

00:09:37: Smires is talking about delivering solutions.

00:09:40: If every single client project is a brand new invention, you're creating massive operational strain.

00:09:46: You're being

00:09:46: an artist when you need to be a manufacturer.

00:09:48: Exactly.

00:09:49: It kills your margins.

00:09:51: Smires says firms need repeatable propositions.

00:09:54: You compound your expertise, not your complexity.

00:09:58: So the idea is you experiment in the market to find a winning solution.

00:10:04: But once you've found it, You standardize the hell out of the delivery.

00:10:07: That is the operational truth.

00:10:09: That's rigor.

00:10:10: Let's hit our final theme, which kind of brings it all back to people.

00:10:14: Brand strategy in professional services.

00:10:16: Frank Aldors had a very strong take.

00:10:18: He says if your brand team reports to marketing.

00:10:20: you've already made a strategic

00:10:22: error.

00:10:22: Which is where it sits in ninety-nine percent of companies.

00:10:25: Eldorf's argument is that brand should be the operating system for the whole company.

00:10:30: It's not the logo.

00:10:31: It's how you hire, how you make decisions, how you treat your customers.

00:10:34: So it's a boardroom issue, not a marketing issue.

00:10:36: Exactly.

00:10:37: And Scott Major connects this right down to the individual.

00:10:40: In professional services, who is the brand?

00:10:42: It's the expert in the room.

00:10:43: The people doing the work are the best influencers.

00:10:46: Authenticity crushes corporate messaging every single time.

00:10:50: In a world of ghost jobs and AI hype, people want to hear from the actual human who knows how to solve their problem.

00:10:58: So Scott Major is basically telling everyone listening, you're the brand.

00:11:01: Your expertise is the asset.

00:11:03: That's it.

00:11:04: OK, let's try to wrap this up.

00:11:06: We've gone from ghost jobs.

00:11:08: to tokenomics, from auditable AI to the tension between complexity and standardization.

00:11:13: The common thread for me is just that shift to operational truth.

00:11:17: The hype cycle is over.

00:11:19: Twenty-twenty-six is about showing your work.

00:11:21: Right.

00:11:21: Whether it's being honest that a job post is just data collection or being brave enough to kill an AI pilot that isn't working, the winner this year is the one who values rigor over a good story.

00:11:31: I agree.

00:11:32: And maybe the thought to leave with is this.

00:11:34: We talked about agentic AI needing an audit log so we can trust it.

00:11:38: Maybe we should hold our own strategies to that same standard.

00:11:40: What would that look like?

00:11:41: If you had to produce an audit log for your last six months of strategic decisions, why you launched that project, why you posted that job, would it show a clear logical path to value or would it just look like improvisation?

00:11:54: That is a tough question to ask on a Tuesday morning, but a necessary one.

00:11:58: If you can't audit your own strategy, you can't expect the market to trust it.

00:12:03: Indeed.

00:12:04: If you enjoyed this episode, new episodes drop every two weeks.

00:12:07: Also check out our other editions on Private Equity, Venture Capital, and M&A.

00:12:11: Thanks for listening.

00:12:12: Don't forget to subscribe.

00:12:13: We'll catch you on the next Deep Dive.