Latest / Best of LinkedIn: Strategy, M&A & Investments / Best of LinkedIn: Strategy & Consulting CW 02/ 03

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Best of LinkedIn: Strategy & Consulting CW 02/ 03 · Best of LinkedIn: Strategy, M&A & Investments

• 0:00
  Speaker 0: provided by Thomas Hallgeier and Frennis based on the most relevant LinkedIn posts about strategy and consulting from CW two and three.
• 0:07
  Speaker 0: Frennis specializes in BDB market research for strategy and consulting teams with a focus on tech and ICT.
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  Speaker 1: Welcome back to the deep dive.
• 0:17
  Speaker 1: So today we are digging into strategy and consulting.
• 0:20
  Speaker 1: for what is it?
• 0:21
  Speaker 1: calendar weeks two and three of twenty twenty six.
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  Speaker 1: And you know if I had to put a single theme on it.
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  Speaker 1: It feels like the lights just came on at the end of a very long, very loud party.
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  Speaker 0: That's a great way to put it.
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  Speaker 0: And when the lights come on, that's when you see the mess you didn't notice when the music was playing.
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  Speaker 1: Exactly.
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  Speaker 1: The music stopped.
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  Speaker 1: Everyone's looking at the debris.
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  Speaker 1: 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.
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  Speaker 1: A
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  Speaker 0: move from what could be... to what is actually broken right now.
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  Speaker 0: And we're not just talking about high level trends here, we're putting on some pretty harsh realities.
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  Speaker 0: We've got companies posting jobs, they have zero intention of filling.
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  Speaker 0: We have CEOs starting to kill AI pilots instead of celebrating them.
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  Speaker 0: And a consulting industry being told to just, well, stop improvising.
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  Speaker 1: It's a reality check across the board, talent tech, you name it.
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  Speaker 0: Okay, so let's unpack that.
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  Speaker 0: I think we have to start with the most
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  Speaker 1: sort of
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  Speaker 0: human part of this problem.
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  Speaker 0: Ghost jobs.
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  Speaker 1: Oh, this is fascinating and frankly a little cynical.
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  Speaker 1: We're looking at a post from made to stare of it who really you know pulls the curtain back on.
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  Speaker 1: this whole practice
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  Speaker 0: pulls the curtain back as an understatement.
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  Speaker 0: I mean he basically says a huge number of those now hiring posts are just Theater.
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  Speaker 0: they're not about recruitment.
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  Speaker 0: They're marketing.
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  Speaker 1: it's performative hiring.
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  Speaker 1: and made breaks it down into three main reasons why companies do this.
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  Speaker 1: and while none of them are We need to hire someone today.
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  Speaker 0: Right.
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  Speaker 0: First one he lists is pipelining.
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  Speaker 0: So just building a database for a role they might open in what, six months?
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  Speaker 0: Which
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  Speaker 1: is so frustrating if you're an applicant.
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  Speaker 1: It's basically data harvesting.
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  Speaker 1: But the second reason is even more deceptive.
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  Speaker 1: Investor perception.
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  Speaker 0: The illusion of scale.
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  Speaker 1: Precisely.
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  Speaker 1: You look like you're growing if you're always hiring.
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  Speaker 1: It signals health to shareholders.
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  Speaker 1: 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.
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  Speaker 0: It's market manipulation using the careers page.
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  Speaker 0: Wild.
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  Speaker 0: And the third one, internal benchmarking.
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  Speaker 0: That just sounds like a way to keep current employees on their toes.
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  Speaker 1: That's part of it.
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  Speaker 1: See what the talent pool looks like.
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  Speaker 1: but made to Derevich didn't just point out the problem.
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  Speaker 1: He gave some really solid actionable advice.
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  Speaker 1: He calls it the thirty day expiry
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  Speaker 0: rule.
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  Speaker 0: I love this.
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  Speaker 0: Such a simple filter.
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  Speaker 0: If a job's been up for more than a month and has like five hundred plus applicants, it's a lead magnet.
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  Speaker 0: It's a lead magnet, not a job.
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  Speaker 0: move on.
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  Speaker 1: And he adds another check, which I think is genius.
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  Speaker 1: Go to the people tab on the company's LinkedIn.
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  Speaker 1: If they haven't actually added headcount in that department recently, that rolls a ghost.
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  Speaker 0: But this isn't just an HR problem, right?
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  Speaker 0: There's a much deeper strategic risk here.
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  Speaker 1: A huge one.
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  Speaker 1: If your hiring is performative, or if you only hire for culture fit, you get what Frank Powers calls cultural cloning.
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  Speaker 0: Cultural cloning.
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  Speaker 0: It sounds like something out of a sci-fi movie.
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  Speaker 1: It's a corporate horror movie.
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  Speaker 1: 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.
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  Speaker 1: And that feels safe, but it absolutely murders innovation.
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  Speaker 0: Because it's a echo chamber.
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  Speaker 1: Exactly.
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  Speaker 1: And that ties into a brilliant point by Zabeen Hirji.
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  Speaker 1: She says HR can't just be adjacent to strategy anymore.
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  Speaker 0: Right, it can't just be the department processing the ghost jobs.
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  Speaker 1: It has to be the core.
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  Speaker 1: If your talent strategy is just for show, your whole business strategy is built on sand.
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  Speaker 0: You can't fake growth forever.
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  Speaker 0: And you definitely can't fake innovation, which I think is a perfect segue to our next big theme, the AI reality check.
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  Speaker 1: Oh, yes, the AI ambition narrative is really crashing into a wall.
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  Speaker 0: In twenty twenty four, twenty twenty five, it was all about the cool factor, right?
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  Speaker 0: Look at this cool thing the bot can do.
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  Speaker 0: But now.
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  Speaker 0: The cool factor is dead.
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  Speaker 0: All that matters is the ROI factor.
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  Speaker 1: We saw some brutal stats on this from Mark Byershoder.
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  Speaker 1: He surveyed what, thirty-two hundred executives?
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  Speaker 1: And the numbers are just stark.
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  Speaker 1: Employee access to AI tools is up fifty percent.
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  Speaker 1: So
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  Speaker 0: the tools are out there.
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  Speaker 1: The tools are everywhere.
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  Speaker 1: But only twenty-five percent of companies have actually moved an experiment into production.
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  Speaker 0: That's a massive gap.
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  Speaker 0: So everyone's playing in the sandbox, but nobody is building the castle.
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  Speaker 1: And here's the key insight from Byershoder.
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  Speaker 1: The companies that are winning aren't doing more pilots.
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  Speaker 0: They're doing fewer.
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  Speaker 1: They're killing pilots faster.
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  Speaker 0: I love that.
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  Speaker 0: It's so aggressive.
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  Speaker 0: If it doesn't pass the integration or security tests, just kill it.
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  Speaker 0: Stop celebrating the experiment.
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  Speaker 0: Demand the outcome.
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  Speaker 1: But that creates a ton of tension in the C-suite.
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  Speaker 1: Neha Cabra writes about this battle between the CFO and the CIO.
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  Speaker 0: The classic showdown.
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  Speaker 0: The CFO was saying, slow down.
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  Speaker 0: Show me the value.
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  Speaker 0: And the CIO is yelling, move now or we die.
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  Speaker 1: And the CEO is stuck in the middle.
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  Speaker 1: But Neha Cabra brings up a great point from Bob Sternfels at McKinsey.
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  Speaker 1: He says, it's not a vote.
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  Speaker 1: You don't pick one over the other.
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  Speaker 1: The answer is, org rewiring.
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  Speaker 0: The mortgage journey example, right?
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  Speaker 1: Yes, it's perfect.
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  Speaker 1: Getting a mortgage traditionally passes through like five different departments.
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  Speaker 0: WISC,
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  Speaker 1: legal, credit.
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  Speaker 1: If you just sprinkle some AI on each of those silos, you don't get much.
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  Speaker 0: The process itself is still broken.
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  Speaker 1: Exactly.
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  Speaker 1: Sternfeld's point is you have to break down the walls between those departments.
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  Speaker 1: The tech has to flow end to end.
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  Speaker 1: That's the rewiring.
• 5:31
  Speaker 1: You have to change how you work.
• 5:33
  Speaker 0: And speaking of costs, Jim Rowan introduced a term that I think is going to be huge, tokenomics.
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  Speaker 1: It sounds like crypto, but it's becoming critical for any CIO.
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  Speaker 1: Tokens are the currency of AI.
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  Speaker 1: Every single prompt costs money.
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  Speaker 1: And those costs are volatile.
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  Speaker 0: So CIOs can't just be technologists anymore.
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  Speaker 1: Nope.
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  Speaker 1: They have to think like CFOs.
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  Speaker 1: They need to understand the unit economics of a prompt.
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  Speaker 1: If you're burning tokens on something that doesn't create value, you are literally burning cash.
• 6:02
  Speaker 0: Which perfectly sums up what Vinci and Bo Sheen said.
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  Speaker 0: Her wish for twenty twenty six was less experimentation, more transformation.
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  Speaker 0: The tech isn't the problem anymore.
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  Speaker 1: We are.
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  Speaker 1: And that transformation is leading to a specific kind of AI.
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  Speaker 1: We're moving from just chatbots to agentic AI.
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  Speaker 0: Okay.
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  Speaker 0: Theme three.
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  Speaker 0: agentic AI.
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  Speaker 0: For anyone listening, this isn't AI that just talks to you.
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  Speaker 0: This is AI that does things.
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  Speaker 0: It executes entire workflows.
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  Speaker 1: Yulia D from SAP had a perfect use case for this.
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  Speaker 1: They tested cash and accruals agents in finance.
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  Speaker 0: Sounds incredibly dry, but the concept is huge.
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  Speaker 1: It is.
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  Speaker 1: The shift, as Yulia puts it, is we state the goal, the agent finds the solution.
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  Speaker 1: So
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  Speaker 0: instead of me clicking ten buttons to move cash, I just tell the agent cover this shortfall and it just
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  Speaker 1: It figures it out,
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  Speaker 0: but,
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  Speaker 1: and this is the most critical part, tying back to our theme trust comes from auditability.
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  Speaker 0: It can't be a black box.
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  Speaker 1: Never.
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  Speaker 1: If that agent moves a single dollar, it has to leave a detailed log of every step it took.
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  Speaker 1: It has to be a verifiable workflow.
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  Speaker 1: You have to be able to audit the robot.
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  Speaker 0: That makes perfect sense.
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  Speaker 0: But building these things sounds incredibly expensive, retraining a whole model for your company.
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  Speaker 0: It used
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  Speaker 1: to be, but Pascal B shared a really clever framework.
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  Speaker 1: He calls it large model, small sub agents.
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  Speaker 0: Okay, break that down.
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  Speaker 1: The idea is you don't retrain the massive, expensive foundation model.
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  Speaker 1: You keep that frozen.
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  Speaker 0: The big brain stays the same.
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  Speaker 1: The big brain that understands language and logic stays put.
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  Speaker 1: And you train these small, cheap sub agents around it.
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  Speaker 1: One might be an expert searcher of your internal database.
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  Speaker 1: Another is an expert at planning.
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  Speaker 1: He's
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  Speaker 0: like a brilliant CEO with a team of hyper-specialized interns.
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  Speaker 1: Great analogy.
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  Speaker 1: And Pascal says it's seventy times more data efficient.
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  Speaker 1: It's cheaper, faster, easier to update.
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  Speaker 0: And Leanne Allen and Holger Harris are saying this is going mainstream.
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  Speaker 0: We're moving from AI doing a task to AI delivering an outcome.
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  Speaker 1: Which demands a whole other level of reliability from your infrastructure.
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  Speaker 0: Right.
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  Speaker 0: And that brings us to the backbone of it all, theme four.
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  Speaker 0: Cloud resilience and systems thinking.
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  Speaker 1: Gilvin and Boom had a line that stuck with me.
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  Speaker 1: He said running cloud without guardrails is like driving without brakes.
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  Speaker 1: That's
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  Speaker 0: terrifying.
• 8:16
  Speaker 1: But it's true.
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  Speaker 1: He's saying cloud resilience isn't a nice to have anymore.
• 8:20
  Speaker 1: You have to build in robust risk frameworks from day one.
• 8:23
  Speaker 0: And Patrishmucky takes this idea even higher.
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  Speaker 0: He says we need to treat strategy itself as a complex adaptive system.
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  Speaker 1: This is deep systems thinking.
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  Speaker 1: In a complex system, the risk isn't one thing failing.
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  Speaker 1: The risk is in the connections between things, the network.
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  Speaker 0: Tristan Oblinger connected this to the Cinefin framework, which I know you love.
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  Speaker 1: I do.
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  Speaker 1: And his point is so important for twenty twenty-six.
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  Speaker 1: He says, stop using best practices for complex problems.
• 8:52
  Speaker 0: What's the practical difference there?
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  Speaker 1: A best practice?
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  Speaker 1: works for simple problems where there's a known right answer like beaking a cake.
• 9:01
  Speaker 1: But for a complex problem like navigating a volatile market or integrating AI, there is no manual.
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  Speaker 1: The manual hasn't been written.
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  Speaker 0: So you need a different method.
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  Speaker 1: You have to run experiments.
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  Speaker 1: Probe, sense, respond.
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  Speaker 1: You test things to find what works.
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  Speaker 1: You don't look for the correct answer in a book.
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  Speaker 0: Okay.
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  Speaker 0: So on one hand, we have Oblinger saying experiment.
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  Speaker 0: But then we get this truth bomb from Luke Smyers about consulting.
• 9:24
  Speaker 0: that seems to say the opposite.
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  Speaker 1: This one probably heard a few people to read.
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  Speaker 1: Smyers just says it flat out.
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  Speaker 1: Improvisation is the root cause of profit erosion.
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  Speaker 0: So how do we square that?
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  Speaker 0: Experiment but don't improvise.
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  Speaker 1: It sounds like a contradiction but it's not.
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  Speaker 1: Smires is talking about delivering solutions.
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  Speaker 1: If every single client project is a brand new invention, you're creating massive operational strain.
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  Speaker 1: You're being
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  Speaker 0: an artist when you need to be a manufacturer.
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  Speaker 0: Exactly.
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  Speaker 1: It kills your margins.
• 9:51
  Speaker 1: Smires says firms need repeatable propositions.
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  Speaker 1: You compound your expertise, not your complexity.
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  Speaker 0: So the idea is you experiment in the market to find a winning solution.
• 10:04
  Speaker 0: But once you've found it, You standardize the hell out of the delivery.
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  Speaker 1: That is the operational truth.
• 10:09
  Speaker 1: That's rigor.
• 10:10
  Speaker 0: Let's hit our final theme, which kind of brings it all back to people.
• 10:14
  Speaker 0: Brand strategy in professional services.
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  Speaker 1: Frank Aldors had a very strong take.
• 10:18
  Speaker 1: He says if your brand team reports to marketing.
• 10:20
  Speaker 1: you've already made a strategic
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  Speaker 0: error.
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  Speaker 0: Which is where it sits in ninety-nine percent of companies.
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  Speaker 1: Eldorf's argument is that brand should be the operating system for the whole company.
• 10:30
  Speaker 1: It's not the logo.
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  Speaker 1: It's how you hire, how you make decisions, how you treat your customers.
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  Speaker 0: So it's a boardroom issue, not a marketing issue.
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  Speaker 1: Exactly.
• 10:37
  Speaker 1: And Scott Major connects this right down to the individual.
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  Speaker 1: In professional services, who is the brand?
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  Speaker 1: It's the expert in the room.
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  Speaker 0: The people doing the work are the best influencers.
• 10:46
  Speaker 1: Authenticity crushes corporate messaging every single time.
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  Speaker 1: In a world of ghost jobs and AI hype, people want to hear from the actual human who knows how to solve their problem.
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  Speaker 0: So Scott Major is basically telling everyone listening, you're the brand.
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  Speaker 0: Your expertise is the asset.
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  Speaker 1: That's it.
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  Speaker 0: OK, let's try to wrap this up.
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  Speaker 0: We've gone from ghost jobs.
• 11:08
  Speaker 0: to tokenomics, from auditable AI to the tension between complexity and standardization.
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  Speaker 1: The common thread for me is just that shift to operational truth.
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  Speaker 1: The hype cycle is over.
• 11:19
  Speaker 1: Twenty-twenty-six is about showing your work.
• 11:21
  Speaker 0: Right.
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  Speaker 0: 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.
• 11:31
  Speaker 1: I agree.
• 11:32
  Speaker 1: And maybe the thought to leave with is this.
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  Speaker 1: We talked about agentic AI needing an audit log so we can trust it.
• 11:38
  Speaker 1: Maybe we should hold our own strategies to that same standard.
• 11:40
  Speaker 0: What would that look like?
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  Speaker 1: 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?
• 11:54
  Speaker 0: That is a tough question to ask on a Tuesday morning, but a necessary one.
• 11:58
  Speaker 0: If you can't audit your own strategy, you can't expect the market to trust it.
• 12:03
  Speaker 1: Indeed.
• 12:04
  Speaker 0: If you enjoyed this episode, new episodes drop every two weeks.
• 12:07
  Speaker 0: Also check out our other editions on Private Equity, Venture Capital, and M&A.
• 12:11
  Speaker 1: Thanks for listening.
• 12:12
  Speaker 0: Don't forget to subscribe.
• 12:13
  Speaker 0: We'll catch you on the next Deep Dive.