Best of LinkedIn: Strategy & Consulting CW 42/ 43

00:00:00: provided by Thomas Allgaier and Frennis, based on the most relevant LinkedIn posts about strategy and consulting in CW, forty-two and forty-three.

00:00:08: Frennis specializes in B to B market research for strategy and consulting teams with a focus on tech and ICT.

00:00:16: Yeah, that LinkedIn summary, it really gave us a fascinating snapshot, didn't it?

00:00:20: You see executives grappling with these big external things, margin pressure, trade issues, but then internally there's this huge push.

00:00:29: AI isn't just pilots anymore.

00:00:31: It's moving into, you know, actual operations delivering real ROI.

00:00:35: It feels like a dual disruption happening at once.

00:00:37: Absolutely.

00:00:38: 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.

00:00:47: We'll dig into how businesses are handling all this change.

00:00:50: AI integration, building resilience, even fundamental organization redesign.

00:00:54: Okay, so where should we start?

00:00:55: maybe with the consulting sector itself?

00:00:57: Because that first big theme was definitely AI judgment and how the consulting value chain is being rethought.

00:01:02: Yeah, that makes sense.

00:01:03: AI is forcing this fundamental question, what does consulting actually sell now?

00:01:08: It's clearly not just information access anymore.

00:01:11: Okay,

00:01:12: let's unpack that.

00:01:13: Consulting always had that pyramid, right?

00:01:15: Juniors analyze, managers synthesize, partners provide the judgment.

00:01:19: But someone like Dr.

00:01:20: CDS Kone put it really well.

00:01:22: AI is basically doing that analyst's job now, running benchmarks, structuring the model.

00:01:28: in minutes.

00:01:29: And the implication for careers there is huge.

00:01:31: The bar to even get into consulting is rising because that old way of learning, watching analysts work, it's kind of gone.

00:01:38: Now it's more about learning by steering the AI.

00:01:41: So new folks can't just crunch data.

00:01:43: They need to know what data to ask the AI for and, crucially, how to interpret the output, right from day one.

00:01:49: Exactly.

00:01:50: And David Leon made a similar point, highlighting how AI is a force multiplier for a specialist, but a threat to a generalist.

00:01:56: If your main value is just general analysis, well, that's becoming a commodity.

00:02:00: What's scarce now is that specific context-witch judgment.

00:02:04: New hires basically need the kind of discernment you used to expect from project managers.

00:02:09: And... that need for instant judgment.

00:02:11: It's speeding everything up client side too.

00:02:14: James O'Dowd mentioned how traditional commercial diligence, you know, the six week kind, it's sitting to look embarrassingly outdated.

00:02:21: Yeah.

00:02:21: Clients want conviction fast, like in two days, backed by AI.

00:02:26: And the key thing O'Dowd implies there is the difference between AI being like a thousand interns versus fifty associates.

00:02:35: Interns give you raw data.

00:02:37: Associates give you curated insight based on existing knowledge.

00:02:41: Good distinction.

00:02:41: That curation layer, that's where the human extrovert is still critical, but they have to be much, much faster.

00:02:47: Which leads to huge changes in the delivery model itself.

00:02:51: So has HC observed firms really pivoting towards a service as a software or sauce model?

00:02:56: Especially in CFO advisory.

00:02:58: He predicted what?

00:02:58: Sixty to eighty percent of advisory could become product led?

00:03:02: Yeah, that's a number.

00:03:03: Well, that's a massive shift.

00:03:04: If sixty eighty percent is product and only twenty forty percent is traditional consulting time, that completely changes the partner track revenue models everything.

00:03:12: It sounds totally transformative.

00:03:14: But what are the big hurdles?

00:03:16: I mean, if it's mostly a software license, how do firms keep those close client relationships, manage risk, justify those high partner rates?

00:03:25: Well, it

00:03:25: forces consultants to shift roles, doesn't it?

00:03:28: from data synthesizers to maybe architects of transformation, trusted change managers.

00:03:34: The product handles the routine stuff, the consultant handles the tricky bits, the edge cases, the judgment calls, the cultural integration.

00:03:40: But it definitely means rethinking IP, how you protect proprietary data to keep that edge.

00:03:46: Which makes that whole product-led shift completely dependent on having solid data foundations, right?

00:03:52: Which brings us nicely to our second theme.

00:03:54: data quality, strategy, and implementation pragmatism.

00:03:57: Exactly.

00:03:58: The AI might be flashy, but its success really hangs on the data it gets and the strategy behind it.

00:04:03: Absolutely.

00:04:04: And leaders get this now.

00:04:05: It's a top-down thing.

00:04:06: Lucas Cordo highlighted how CEOs have to prioritize data quality and integration.

00:04:11: It's not just a tech problem for the C.A.R.

00:04:12: anymore.

00:04:13: It's core business strategy.

00:04:15: And once the data is clean, it's about getting the most value out.

00:04:20: Ball's assimer added that Gen AI is the key here for unlocking value and all that organizational data that's just sitting there.

00:04:26: Moving companies up the DIKW pyramid, as he put it.

00:04:29: Right, DIKW.

00:04:30: Maybe a quick refresher.

00:04:31: Data information knowledge wisdom.

00:04:33: Yeah.

00:04:34: Data is just rough acts.

00:04:35: Information is data with context.

00:04:37: Knowledge is applying it.

00:04:38: Wisdom is, well, using judgment for the best action.

00:04:41: Okay.

00:04:42: 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.

00:04:49: So, data is the foundation.

00:04:52: But the ambition needs to match the disruption.

00:04:55: Camille Gabrin and Dr.

00:04:56: Tobias Schmidt had some clear lessons on making AI implementation work.

00:05:00: They stressed that most AI problems are actually business strategy problems first.

00:05:05: Right.

00:05:05: Start with the biggest business challenge, not just the coolest new tech.

00:05:08: And you need to aim high.

00:05:09: Camille Gabrin argued, pretty convincingly, I thought, that projects need to shoot for, like, five X or ten X improvement.

00:05:16: Five or ten times.

00:05:17: That sounds... Huge.

00:05:20: Almost like setting yourself up to fail with scope creep.

00:05:23: Why isn't a solid say, twenty, thirty percent, gain good enough?

00:05:26: Because the small wins often don't justify the sheer effort.

00:05:31: You know, retraining people, changing systems, fundamentally altering how things get done.

00:05:35: Oh, okay.

00:05:36: Like Dr.

00:05:36: 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.

00:05:46: You have to actually change the game.

00:05:48: not just tweak the edges.

00:05:49: Makes sense.

00:05:50: That kind of exponential goal needs more than just good data and ambition, though.

00:05:54: It needs serious governance, especially when you talk about agentic AI systems acting on their own.

00:05:59: Exactly.

00:06:00: Doug Shannon used that great Kobayashi Maru analogy from Star Trek.

00:06:03: Ah, yes, the unwinnable tests.

00:06:05: Right, but Shannon's point was in enterprise AI, you don't just try to survive the test.

00:06:10: you have to rewrite the rules of the simulation itself.

00:06:13: So what does that look like, practically, in a business context?

00:06:16: It means building proprietary scaffolding, governance layers, guardrails around your internal AI, defining how it can use company IP, what tasks it can execute.

00:06:27: To

00:06:27: protect your core knowledge, stop it from becoming instantly commoditized.

00:06:30: Precisely.

00:06:31: That internal resilience, setting your own rules, it's kind of necessary given all the external chaos.

00:06:37: Which takes us to theme three.

00:06:39: Operations, resilience and the cost of uncertainty.

00:06:44: Geopolitics, trade friction, inflation.

00:06:48: It's not just background noise.

00:06:49: It's driving real strategic shifts.

00:06:51: And the response, especially from manufacturing and supply chain leaders, it's definitely not the old knee-jerk cost cutting.

00:06:58: Thomas Pettichord, referencing some BCG work, noted leaders are prioritizing reinvestment.

00:07:02: Reinvestment where?

00:07:03: In tech, like AI and automation, but also reshoring production, building stability into the supply chain rather than just slashing costs.

00:07:10: It's

00:07:10: like building muscle instead of just cutting fat.

00:07:12: Exactly.

00:07:13: And all this trade volatility, it creates these hidden knock-on costs.

00:07:17: Christian H. Rodriguez-Chefelle pointed out how tariffs can trigger things like wage increases or suppliers just passing prices straight through.

00:07:25: Costs

00:07:25: that execs often miss in their models.

00:07:27: So just switching suppliers quickly to avoid a tariff as a short-term fix.

00:07:32: Jacopo Piccolo Brunelli argued that real competitiveness needs strategic cost management.

00:07:38: redesigning operations, building capabilities to handle volatility, not just reacting tactically.

00:07:43: And that strategic view is why you see things like risk and ESG moving beyond just compliance.

00:07:48: They're becoming actual strategic levers.

00:07:51: Shumgarg, citing Deloitte, really emphasized embedding risk thinking into the core strategy.

00:07:56: Viewing risk, not just defensively, but as an enabler for creating long-term value.

00:08:01: I like the distinction Chema Easy Jean made between ESG and sustainability.

00:08:05: ESG, she said, is more about compliance risk.

00:08:08: investor confidence.

00:08:09: basically, how riskier you are.

00:08:11: Whereas sustainability comes from purpose, long-term value.

00:08:13: It's about innovation, resilience for the whole system.

00:08:16: So one's kind of reactive, focused on the quarterly report, the other's proactive, focused on the future.

00:08:22: And tying into that operational resilience, Alexandra Wilkowska stressed how crucial regulatory visibility is now.

00:08:29: across ESG, cyber,

00:08:31: safety.

00:08:32: You need proactive monitoring.

00:08:33: Absolutely.

00:08:34: You can't afford blind spots when things are this volatile.

00:08:36: You need to anticipate changes, not just react.

00:08:39: That pulls it together nicely.

00:08:41: 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.

00:08:51: Leadership and the human agentic workforce.

00:08:53: Yeah, this is about the redesign itself.

00:08:55: Mark Byershoter from Deloitte described this emerging model as the human-agentic workforce.

00:09:00: Okay, what does that mean?

00:09:01: Humans focus on the high value stuff.

00:09:04: Judgment, empathy, navigating complexity, digital agents handle the repeatable, measurable tasks.

00:09:10: It's about scaling smarter.

00:09:12: And the economics of that are pretty stark.

00:09:13: Neha Cabra shared that line from a fintech COO, one strategic hire plus the right AI stack beats a team of ten.

00:09:20: It's not just about cutting heads, it's about redesigning the whole operating model.

00:09:25: where that strategic hire has to be expert enough to actually steer the AI stack effectively.

00:09:31: Right.

00:09:31: Shifting focus from managing teams to leveraging tech.

00:09:34: But the human element is still critical, often underestimated.

00:09:38: Naranjan Shridhar's survey data showed tech accelerates change, sure, but failures still often come down to complexity and weak leadership.

00:09:47: So success needs unified leadership, clear vision.

00:09:51: business-led collaboration.

00:09:52: Exactly.

00:09:53: No matter how smart the AI gets, it's still the people defining success or failure.

00:09:57: But why does empathy?

00:09:59: that human navigation skill become more important now with all this automation.

00:10:03: We'll think about it.

00:10:03: The AI handles the routine low-friction work, what's left for the human leaders, the really hard stuff,

00:10:09: the edge cases,

00:10:10: internal conflicts, external crises, managing change, ethical dilemmas, the highest friction points.

00:10:16: Michael Briggle confirmed this.

00:10:17: AI changes structure.

00:10:19: Departments might shrink, but the best human leaders, they're indispensable for strategy and empathy.

00:10:25: And Mark Diorlich highlighted that too, right?

00:10:28: Embracing emotions and leadership is key now.

00:10:30: That ability to navigate the messy human stuff sets leaders apart.

00:10:34: Definitely.

00:10:35: The transformation is digital, yes, but it's also deeply human.

00:10:39: Which brings us to a final thought for you, the listener.

00:10:41: Drawing from these insights, Johannes Lowe had this great tennis analogy.

00:10:44: Ah,

00:10:45: the visibility one.

00:10:46: Yeah.

00:10:46: Effort matters, but visibility wins points.

00:10:49: 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.

00:10:59: Control the point.

00:11:00: And in this era, with AI potentially commoditizing your core or geopolitics messing with your costs, you just can't win by defending forever.

00:11:08: You have to execute, move into that new model.

00:11:11: I think the core takeaway from these last two weeks is clear.

00:11:14: The focus really shifted now, away from just tactical AI wins towards strategic, full-on organizational change.

00:11:21: Which demands those clear data foundations we talked about, proactive risk management.

00:11:25: And human leadership centered on judgment, courage, and building trust.

00:11:29: It really is time to move to

00:11:36: the net.