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Best of LinkedIn: Strategy & Consulting CW 42/ 43 · Best of LinkedIn: Strategy, M&A & Investments

• 0:00
  Speaker 0: provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts about strategy and consulting in CW, forty-two and forty-three.
• 0:08
  Speaker 0: Frennis specializes in B to B market research for strategy and consulting teams with a focus on tech and ICT.
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  Speaker 1: Yeah, that LinkedIn summary, it really gave us a fascinating snapshot, didn't it?
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  Speaker 1: You see executives grappling with these big external things, margin pressure, trade issues, but then internally there's this huge push.
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  Speaker 1: AI isn't just pilots anymore.
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  Speaker 1: It's moving into, you know, actual operations delivering real ROI.
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  Speaker 1: It feels like a dual disruption happening at once.
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  Speaker 0: Absolutely.
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  Speaker 0: So for you listening, maybe you're an M&A, private equity VC or... Consulting, what we want to do today is really distill the key strategy and consulting insights from the last couple of weeks.
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  Speaker 0: We'll dig into how businesses are handling all this change.
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  Speaker 0: AI integration, building resilience, even fundamental organization redesign.
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  Speaker 1: Okay, so where should we start?
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  Speaker 1: maybe with the consulting sector itself?
• 0:57
  Speaker 1: Because that first big theme was definitely AI judgment and how the consulting value chain is being rethought.
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  Speaker 0: Yeah, that makes sense.
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  Speaker 1: AI is forcing this fundamental question, what does consulting actually sell now?
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  Speaker 1: It's clearly not just information access anymore.
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  Speaker 1: Okay,
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  Speaker 0: let's unpack that.
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  Speaker 0: Consulting always had that pyramid, right?
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  Speaker 0: Juniors analyze, managers synthesize, partners provide the judgment.
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  Speaker 0: But someone like Dr.
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  Speaker 0: CDS Kone put it really well.
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  Speaker 0: AI is basically doing that analyst's job now, running benchmarks, structuring the model.
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  Speaker 0: in minutes.
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  Speaker 1: And the implication for careers there is huge.
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  Speaker 1: The bar to even get into consulting is rising because that old way of learning, watching analysts work, it's kind of gone.
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  Speaker 1: Now it's more about learning by steering the AI.
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  Speaker 0: So new folks can't just crunch data.
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  Speaker 0: They need to know what data to ask the AI for and, crucially, how to interpret the output, right from day one.
• 1:49
  Speaker 0: Exactly.
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  Speaker 1: And David Leon made a similar point, highlighting how AI is a force multiplier for a specialist, but a threat to a generalist.
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  Speaker 1: If your main value is just general analysis, well, that's becoming a commodity.
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  Speaker 1: What's scarce now is that specific context-witch judgment.
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  Speaker 1: New hires basically need the kind of discernment you used to expect from project managers.
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  Speaker 0: And... that need for instant judgment.
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  Speaker 0: It's speeding everything up client side too.
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  Speaker 0: James O'Dowd mentioned how traditional commercial diligence, you know, the six week kind, it's sitting to look embarrassingly outdated.
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  Speaker 0: Yeah.
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  Speaker 0: Clients want conviction fast, like in two days, backed by AI.
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  Speaker 1: And the key thing O'Dowd implies there is the difference between AI being like a thousand interns versus fifty associates.
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  Speaker 1: Interns give you raw data.
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  Speaker 1: Associates give you curated insight based on existing knowledge.
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  Speaker 0: Good distinction.
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  Speaker 1: That curation layer, that's where the human extrovert is still critical, but they have to be much, much faster.
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  Speaker 0: Which leads to huge changes in the delivery model itself.
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  Speaker 0: So has HC observed firms really pivoting towards a service as a software or sauce model?
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  Speaker 0: Especially in CFO advisory.
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  Speaker 1: He predicted what?
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  Speaker 1: Sixty to eighty percent of advisory could become product led?
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  Speaker 0: Yeah, that's a number.
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  Speaker 1: Well, that's a massive shift.
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  Speaker 1: If sixty eighty percent is product and only twenty forty percent is traditional consulting time, that completely changes the partner track revenue models everything.
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  Speaker 0: It sounds totally transformative.
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  Speaker 0: But what are the big hurdles?
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  Speaker 0: I mean, if it's mostly a software license, how do firms keep those close client relationships, manage risk, justify those high partner rates?
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  Speaker 0: Well, it
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  Speaker 1: forces consultants to shift roles, doesn't it?
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  Speaker 1: from data synthesizers to maybe architects of transformation, trusted change managers.
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  Speaker 1: The product handles the routine stuff, the consultant handles the tricky bits, the edge cases, the judgment calls, the cultural integration.
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  Speaker 1: But it definitely means rethinking IP, how you protect proprietary data to keep that edge.
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  Speaker 0: Which makes that whole product-led shift completely dependent on having solid data foundations, right?
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  Speaker 0: Which brings us nicely to our second theme.
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  Speaker 0: data quality, strategy, and implementation pragmatism.
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  Speaker 1: Exactly.
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  Speaker 0: The AI might be flashy, but its success really hangs on the data it gets and the strategy behind it.
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  Speaker 0: Absolutely.
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  Speaker 1: And leaders get this now.
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  Speaker 1: It's a top-down thing.
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  Speaker 1: Lucas Cordo highlighted how CEOs have to prioritize data quality and integration.
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  Speaker 1: It's not just a tech problem for the C.A.R.
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  Speaker 1: anymore.
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  Speaker 1: It's core business strategy.
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  Speaker 0: And once the data is clean, it's about getting the most value out.
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  Speaker 0: Ball's assimer added that Gen AI is the key here for unlocking value and all that organizational data that's just sitting there.
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  Speaker 1: Moving companies up the DIKW pyramid, as he put it.
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  Speaker 0: Right, DIKW.
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  Speaker 0: Maybe a quick refresher.
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  Speaker 0: Data information knowledge wisdom.
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  Speaker 1: Yeah.
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  Speaker 1: Data is just rough acts.
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  Speaker 1: Information is data with context.
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  Speaker 1: Knowledge is applying it.
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  Speaker 1: Wisdom is, well, using judgment for the best action.
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  Speaker 1: Okay.
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  Speaker 1: Gen AI's power is taking masses of internal information and pushing it up towards knowledge, maybe even near wisdom, way faster than people ever could.
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  Speaker 0: So, data is the foundation.
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  Speaker 0: But the ambition needs to match the disruption.
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  Speaker 0: Camille Gabrin and Dr.
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  Speaker 0: Tobias Schmidt had some clear lessons on making AI implementation work.
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  Speaker 0: They stressed that most AI problems are actually business strategy problems first.
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  Speaker 1: Right.
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  Speaker 1: Start with the biggest business challenge, not just the coolest new tech.
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  Speaker 0: And you need to aim high.
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  Speaker 0: Camille Gabrin argued, pretty convincingly, I thought, that projects need to shoot for, like, five X or ten X improvement.
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  Speaker 1: Five or ten times.
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  Speaker 1: That sounds... Huge.
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  Speaker 1: Almost like setting yourself up to fail with scope creep.
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  Speaker 1: Why isn't a solid say, twenty, thirty percent, gain good enough?
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  Speaker 0: Because the small wins often don't justify the sheer effort.
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  Speaker 0: You know, retraining people, changing systems, fundamentally altering how things get done.
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  Speaker 0: Oh, okay.
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  Speaker 0: Like Dr.
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  Speaker 0: Tobias Schmidt pointed out, anything less than that five X or ten X just adds complexity without enough benefit to get real executive buy-in to scale it.
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  Speaker 0: You have to actually change the game.
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  Speaker 0: not just tweak the edges.
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  Speaker 1: Makes sense.
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  Speaker 1: That kind of exponential goal needs more than just good data and ambition, though.
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  Speaker 1: It needs serious governance, especially when you talk about agentic AI systems acting on their own.
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  Speaker 0: Exactly.
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  Speaker 0: Doug Shannon used that great Kobayashi Maru analogy from Star Trek.
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  Speaker 1: Ah, yes, the unwinnable tests.
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  Speaker 0: Right, but Shannon's point was in enterprise AI, you don't just try to survive the test.
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  Speaker 0: you have to rewrite the rules of the simulation itself.
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  Speaker 1: So what does that look like, practically, in a business context?
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  Speaker 0: It means building proprietary scaffolding, governance layers, guardrails around your internal AI, defining how it can use company IP, what tasks it can execute.
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  Speaker 0: To
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  Speaker 1: protect your core knowledge, stop it from becoming instantly commoditized.
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  Speaker 1: Precisely.
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  Speaker 0: That internal resilience, setting your own rules, it's kind of necessary given all the external chaos.
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  Speaker 0: Which takes us to theme three.
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  Speaker 0: Operations, resilience and the cost of uncertainty.
• 6:44
  Speaker 0: Geopolitics, trade friction, inflation.
• 6:48
  Speaker 0: It's not just background noise.
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  Speaker 0: It's driving real strategic shifts.
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  Speaker 1: And the response, especially from manufacturing and supply chain leaders, it's definitely not the old knee-jerk cost cutting.
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  Speaker 1: Thomas Pettichord, referencing some BCG work, noted leaders are prioritizing reinvestment.
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  Speaker 0: Reinvestment where?
• 7:03
  Speaker 1: In tech, like AI and automation, but also reshoring production, building stability into the supply chain rather than just slashing costs.
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  Speaker 1: It's
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  Speaker 0: like building muscle instead of just cutting fat.
• 7:12
  Speaker 1: Exactly.
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  Speaker 1: And all this trade volatility, it creates these hidden knock-on costs.
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  Speaker 1: Christian H. Rodriguez-Chefelle pointed out how tariffs can trigger things like wage increases or suppliers just passing prices straight through.
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  Speaker 0: Costs
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  Speaker 1: that execs often miss in their models.
• 7:27
  Speaker 0: So just switching suppliers quickly to avoid a tariff as a short-term fix.
• 7:32
  Speaker 0: Jacopo Piccolo Brunelli argued that real competitiveness needs strategic cost management.
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  Speaker 0: redesigning operations, building capabilities to handle volatility, not just reacting tactically.
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  Speaker 1: And that strategic view is why you see things like risk and ESG moving beyond just compliance.
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  Speaker 1: They're becoming actual strategic levers.
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  Speaker 1: Shumgarg, citing Deloitte, really emphasized embedding risk thinking into the core strategy.
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  Speaker 1: Viewing risk, not just defensively, but as an enabler for creating long-term value.
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  Speaker 0: I like the distinction Chema Easy Jean made between ESG and sustainability.
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  Speaker 0: ESG, she said, is more about compliance risk.
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  Speaker 0: investor confidence.
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  Speaker 0: basically, how riskier you are.
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  Speaker 0: Whereas sustainability comes from purpose, long-term value.
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  Speaker 0: It's about innovation, resilience for the whole system.
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  Speaker 1: So one's kind of reactive, focused on the quarterly report, the other's proactive, focused on the future.
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  Speaker 1: And tying into that operational resilience, Alexandra Wilkowska stressed how crucial regulatory visibility is now.
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  Speaker 1: across ESG, cyber,
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  Speaker 0: safety.
• 8:32
  Speaker 0: You need proactive monitoring.
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  Speaker 1: Absolutely.
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  Speaker 1: You can't afford blind spots when things are this volatile.
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  Speaker 1: You need to anticipate changes, not just react.
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  Speaker 0: That pulls it together nicely.
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  Speaker 0: So all this change, designing agentic AI, strategic cost control, it obviously demands a new kind of organization, new leadership, which is our final theme.
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  Speaker 0: Leadership and the human agentic workforce.
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  Speaker 1: Yeah, this is about the redesign itself.
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  Speaker 1: Mark Byershoter from Deloitte described this emerging model as the human-agentic workforce.
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  Speaker 0: Okay, what does that mean?
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  Speaker 1: Humans focus on the high value stuff.
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  Speaker 1: Judgment, empathy, navigating complexity, digital agents handle the repeatable, measurable tasks.
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  Speaker 1: It's about scaling smarter.
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  Speaker 0: And the economics of that are pretty stark.
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  Speaker 0: Neha Cabra shared that line from a fintech COO, one strategic hire plus the right AI stack beats a team of ten.
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  Speaker 0: It's not just about cutting heads, it's about redesigning the whole operating model.
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  Speaker 0: where that strategic hire has to be expert enough to actually steer the AI stack effectively.
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  Speaker 1: Right.
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  Speaker 1: Shifting focus from managing teams to leveraging tech.
• 9:34
  Speaker 1: But the human element is still critical, often underestimated.
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  Speaker 1: Naranjan Shridhar's survey data showed tech accelerates change, sure, but failures still often come down to complexity and weak leadership.
• 9:47
  Speaker 0: So success needs unified leadership, clear vision.
• 9:51
  Speaker 0: business-led collaboration.
• 9:52
  Speaker 0: Exactly.
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  Speaker 1: No matter how smart the AI gets, it's still the people defining success or failure.
• 9:57
  Speaker 0: But why does empathy?
• 9:59
  Speaker 0: that human navigation skill become more important now with all this automation.
• 10:03
  Speaker 1: We'll think about it.
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  Speaker 1: The AI handles the routine low-friction work, what's left for the human leaders, the really hard stuff,
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  Speaker 0: the edge cases,
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  Speaker 1: internal conflicts, external crises, managing change, ethical dilemmas, the highest friction points.
• 10:16
  Speaker 1: Michael Briggle confirmed this.
• 10:17
  Speaker 1: AI changes structure.
• 10:19
  Speaker 1: Departments might shrink, but the best human leaders, they're indispensable for strategy and empathy.
• 10:25
  Speaker 0: And Mark Diorlich highlighted that too, right?
• 10:28
  Speaker 0: Embracing emotions and leadership is key now.
• 10:30
  Speaker 0: That ability to navigate the messy human stuff sets leaders apart.
• 10:34
  Speaker 0: Definitely.
• 10:35
  Speaker 1: The transformation is digital, yes, but it's also deeply human.
• 10:39
  Speaker 0: Which brings us to a final thought for you, the listener.
• 10:41
  Speaker 0: Drawing from these insights, Johannes Lowe had this great tennis analogy.
• 10:44
  Speaker 0: Ah,
• 10:45
  Speaker 1: the visibility one.
• 10:46
  Speaker 1: Yeah.
• 10:46
  Speaker 0: Effort matters, but visibility wins points.
• 10:49
  Speaker 1: Meaning you can run yourself ragged, defending the baseline, playing perfect defense, but you won't actually win unless you step forward, move to the net, and take the shot.
• 10:59
  Speaker 1: Control the point.
• 11:00
  Speaker 0: And in this era, with AI potentially commoditizing your core or geopolitics messing with your costs, you just can't win by defending forever.
• 11:08
  Speaker 0: You have to execute, move into that new model.
• 11:11
  Speaker 0: I think the core takeaway from these last two weeks is clear.
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  Speaker 0: The focus really shifted now, away from just tactical AI wins towards strategic, full-on organizational change.
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  Speaker 1: Which demands those clear data foundations we talked about, proactive risk management.
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  Speaker 0: And human leadership centered on judgment, courage, and building trust.
• 11:29
  Speaker 1: It really is time to move to
• 11:36
  Speaker 0: the net.