Best of LinkedIn: Strategy & Consulting CW 50 - 01

00:00:00: provided by Thomas Allgaier and Freeness, based on the most relevant LinkedIn posts about strategy and consulting from CW-Fifty to One.

00:00:06: Freeness specializes in B-to-B market research for strategy and consulting teams with a focus on tech and ICT.

00:00:12: This edition is brought to you by our partner Dealroom.

00:00:15: Help shape their state of M&A's twenty-twenty-six report by taking their survey.

00:00:19: This is your chance to share how you approach sourcing, diligence and integration, and see how your experience compares to peers.

00:00:25: Final link to the survey in the description.

00:00:27: Welcome to the deep dive.

00:00:29: Our mission today is pretty critical for anyone in M&A, private equity, venture capital, really the whole consulting ecosystem.

00:00:37: We're trying to extract the unified strategy and consulting agenda for twenty twenty six.

00:00:42: And this isn't just, you know, a list of trends.

00:00:44: It's more of a hard headed reality check.

00:00:46: Exactly.

00:00:47: A reality check based on where the top thinkers are actually placing their bets.

00:00:51: We've pulled insights from all over the professional sphere and the message is it's really unified.

00:00:56: It is.

00:00:56: The focus has shifted so decisively away from general strategy, from just experimenting.

00:01:02: It's all about execution discipline and measurable returns now.

00:01:06: The playground phase is over.

00:01:07: It

00:01:07: is.

00:01:08: The whole mission is converting this explosion in AI adoption into tangible bottom line impact.

00:01:15: And you have to do that while managing global volatility and just trying to keep your operating model from breaking.

00:01:22: a small task.

00:01:22: So it's a tiny one.

00:01:23: So let's unpack this.

00:01:25: We've clustered the material into four critical things, and our first one jumps right into the deep end.

00:01:29: Yeah.

00:01:29: It's that AI is shifting from simple experimentation to rigorous value proof.

00:01:35: And that's demanding fundamental changes to delivery models.

00:01:39: You can really feel this tension in the source material.

00:01:42: I mean, investments are surging, but the expectations for actual outcome based value are tightening dramatically, especially from the board level.

00:01:50: Holger Paris noted this friction perfectly.

00:01:53: He said that while AI adoption is new, ninety percent in large companies, which is

00:01:57: huge.

00:01:57: It's huge.

00:01:58: But here's the kicker.

00:01:59: Only about ten percent actually succeed in scaling those initial use cases to deliver measurable sustained value.

00:02:06: So you have this ninety ten split.

00:02:07: That's that's the whole challenge right there.

00:02:09: It tells you the technology isn't the problem.

00:02:11: No, it's the system around the technology.

00:02:13: Yeah.

00:02:14: Alan Duarte from Morgan Stanley really echoed this.

00:02:17: He said, the trade for twenty twenty-six is crystal clear.

00:02:20: It's show me the yearnings.

00:02:21: No more narratives.

00:02:22: No more leveraging AI language to justify a multiple.

00:02:25: You have to demonstrate margin proof.

00:02:28: Real operating leverage.

00:02:29: And this demand for measurable impact, it totally changes how you have to manage failure.

00:02:35: Chuck Whitten introduced this really fascinating concept.

00:02:38: he calls a destruction strategy.

00:02:40: Okay, what's that?

00:02:41: Well, since something like forty-five percent of AI pilots hit roadblocks, I mean, that's nearly half.

00:02:48: If your organization has a zero failure rate, it just means you're optimizing for safety.

00:02:52: Not for impact.

00:02:53: Exactly.

00:02:53: You're not optimizing for genuine high-impact innovation.

00:02:56: So the idea is that killing an underperforming pilot needs to be reframed.

00:03:00: It's a capital allocation victory.

00:03:01: So if a pilot dies, you should celebrate it.

00:03:04: That sounds completely counterintuitive.

00:03:06: It is.

00:03:07: But it's a psychological shift.

00:03:09: You stop the company from wasting resources chasing a dead end.

00:03:13: And that requires a culture of, you know, deep psychological safety, which is way harder to engineer than any algorithm.

00:03:20: And as organizations try to scale, the tools themselves are evolving.

00:03:24: We're seeing the rise of what they're calling agentic AI.

00:03:27: Right.

00:03:28: Vincian Bhoshan reported that thirty-five percent of organizations are already using these systems.

00:03:33: And these aren't static tools, right?

00:03:35: They can autonomously plan and act and even learn.

00:03:38: Which, of course, leads to this immediate and pretty intense debate about what we even call them.

00:03:43: Is it an agent,

00:03:44: a tool, or

00:03:45: a teammate?

00:03:46: Aziz Saudogo suggests agents are blurring the line between machine and teammate.

00:03:51: I can see the appeal of calling it a teammate.

00:03:53: It sounds collaborative, futuristic.

00:03:56: Of course.

00:03:57: But David Ritter pushed back on that.

00:03:59: Hard.

00:03:59: He argued it's dangerous, even repugnant.

00:04:02: Strong words.

00:04:03: Very.

00:04:04: He believes that equating software with human beings makes automation bias much, much worse.

00:04:08: That's the risk where you just defer to the machine, even if your gut tells you something is off.

00:04:12: Exactly.

00:04:13: Especially in high-stays environments like due diligence or trading, you cannot sacrifice human accountability for a convenient metaphor.

00:04:21: That's a massive risk for M&A teams.

00:04:23: You could overlook a critical liability just because the system didn't flag it.

00:04:28: That accountability piece has to stay front and center.

00:04:30: Absolutely.

00:04:31: And this whole structural shift is redefining the value of consulting itself.

00:04:36: Hugo Reimakers pointed out that the marginal cost of knowledge is just collapsing.

00:04:41: We've all felt that.

00:04:42: Research that used to take teams weeks now takes one person and a good prompt a few seconds.

00:04:47: So if knowledge is practically free.

00:04:49: What are consulting firms actually charging for?

00:04:51: Precisely.

00:04:52: The value shifts from human hours to compute, leverage, orchestration.

00:04:56: And this is the key, unflinching human judgment.

00:05:00: And this forces new delivery structures.

00:05:02: Like what?

00:05:03: Well, Scott Weiler mentioned BCG is using forward deployed consultants now.

00:05:07: They're physically embedded with clients, building Taylor tools, solving problems in real time.

00:05:12: It's less about advice and more about enablement.

00:05:14: And that must create openings for new players.

00:05:18: Shizama Gray noted that these AI first boutiques are thriving.

00:05:22: They use speed and small teams to just run circles around the traditional heavier structures.

00:05:26: Right, because you can't capture that AI value if your own organization is too slow or too rigid to handle

00:05:33: it.

00:05:33: Which brings us perfectly to our second theme.

00:05:36: Because for any of this to work, For the boutique to be fast, for the value to be proven, you need perfect inputs.

00:05:42: So our second major theme is data and analytics.

00:05:45: foundations are reasserting themselves as the real AI bottleneck.

00:05:50: This was maybe the most consistent complaint we saw.

00:05:52: Rosario LaFaith was very clear that data governance is almost never treated as a strategic investment.

00:05:58: lives in the basement, as he put it.

00:05:59: Classic.

00:06:00: Everyone wants the shiny AI outcome, but nobody wants to fund the boring plumbing underneath.

00:06:05: And that's a fatal flaw?

00:06:06: Yeah.

00:06:07: Because advanced models don't fix bad data.

00:06:09: They just scale its impact at machine speed.

00:06:12: LaFace argued you have to elevate data governments to the executive agenda.

00:06:16: Define ownership, fund the automation.

00:06:18: You can't build a skyscraper on quicksand.

00:06:20: And speaking of foundations, there's a huge strategic shift happening with ERP systems.

00:06:25: Christian Rausch.

00:06:26: covered the convergence of Sforana and Gen AI.

00:06:29: Right.

00:06:30: ERP is evolving from just being a system of record where data goes to be stored to a system of reasoning, a system of continuous learning.

00:06:38: That's a massive cognitive leap.

00:06:40: A system of record just logs what happened.

00:06:43: A system of reasoning uses those logs plus AI to provide decision intelligence.

00:06:48: To anticipate what's next, it makes the ERP a living part of your strategy.

00:06:54: But even with these new sophisticated tools, the human role in complex data work is still non-negotiable.

00:07:00: Absolutely not.

00:07:01: David Ersing shared a great real-world example during a NetSuite migration.

00:07:05: He said AI tools saved them forty percent of their data cleansing time by flagging anomalies.

00:07:10: Which is a huge efficiency gain.

00:07:11: Forty percent is massive.

00:07:13: But, and this is the cue, human expertise was still required for the context.

00:07:17: What do you mean?

00:07:18: Well, the machine could flag an MYR-AID-FORTY-SEVEN variance, but it couldn't tell you if it was a legitimate expense or an error.

00:07:24: A human had to make that judgment call.

00:07:26: Or it could see two vendors with slightly different names.

00:07:29: And only a person would know they're actually the same entity operating in different subsidiaries.

00:07:33: Exactly.

00:07:34: The machine spots the irregularity.

00:07:36: The human provides the context and the judgment.

00:07:40: which actually connects perfectly to our third theme.

00:07:43: Once the tech is deployed and the data is clean, the process is still run by people.

00:07:47: And this theme is all about the operating model reality checks and the needs it for execution discipline.

00:07:53: This

00:07:53: is the seventy percent problem, right?

00:07:55: Milo Niemler highlighted that classic ten-twenty-seventy model.

00:08:00: Seventy percent of AI value comes from the messy parts people, processes, change management.

00:08:05: Oh, where do we spend all the money and focus?

00:08:07: On the ten percent.

00:08:08: the algorithm, the tech itself.

00:08:10: So we've built this brilliant technology that just runs into the brick wall of our own org chart.

00:08:14: Exactly.

00:08:15: And Mark Byer Shoder drilled down into one specific type of friction, coordination overload.

00:08:21: Work slows down not because it's hard, but because the system demands too many sign-offs, too much reporting, just to move an inch.

00:08:28: And his solution is to restore momentum by removing those dependencies, forcing decisions to happen where the work is actually being done.

00:08:35: empowering the edges of the organization.

00:08:37: It just makes sense if you want to capture the speed benefits of AI.

00:08:41: So when technology becomes the great equalizer, what differentiates one company from another?

00:08:47: Neha Cabra argued that humanizing will be the crucial differentiator in twenty twenty

00:08:52: six.

00:08:52: I like that.

00:08:53: Yeah.

00:08:54: Success has decided not by model accuracy, but by human judgment, by sequencing decisions correctly.

00:09:01: and by being able to read the room when the data is ambiguous.

00:09:05: She really drove that point home saying that execution is strategy.

00:09:09: She argued the failure point isn't a technology issue, it's a decision design issue.

00:09:13: Meaning the outcome is determined by things like system defaults, departmental incentives, handoffs.

00:09:19: not the elegant strategy deck you presented six months ago.

00:09:22: That's such a powerful distinction.

00:09:24: It means the strategy team needs to think more like system architects, designing the decisions right into the operational flow.

00:09:30: And the big takeaway, which Reshmi Ramachandran stated so powerfully, is that you have to fix the fundamentals first.

00:09:37: AI is not a magic wand.

00:09:38: Right.

00:09:39: It will not fix your broken cash discipline or unclear roles or your dinosaur processes.

00:09:44: If your organization is already flawed, AI won't fix it.

00:09:48: It will just

00:09:49: accelerate and amplify those existing problems.

00:09:52: Your failures will just be faster, louder, and way more expensive.

00:09:56: Which sets us up perfectly for our final theme.

00:09:59: We've talked about the tech and the internal organization.

00:10:02: Now we have to look at the external environment.

00:10:04: Right.

00:10:04: Theme four is about global strategy, resilience, and risk.

00:10:08: The big message here is that geopolitics is no longer an optional overlay.

00:10:13: It's not some risk that just lives with the supply chain director.

00:10:17: It's a persistent daily constraint.

00:10:19: Christian Rodriguez-Joufel stressed that tariffs trade friction.

00:10:23: These aren't temporary disruptions anymore.

00:10:25: They demand long-term cross-functional planning.

00:10:28: And the response, according to Nicholas Lang, is to proactively build what he calls geopolitical muscle.

00:10:34: You have to strengthen supply chain resilience now.

00:10:36: And this pressure, combined with the energy transition, is driving industrial strategy in completely new ways, particularly in the US.

00:10:43: Alan Duarte noted that energy is now being treated as a policy-backed growth market, not just a utility cost you try to minimize.

00:10:50: That is a huge strategic shift.

00:10:52: Leaders have to treat power as strategy.

00:10:55: He used nuclear as an example, framing it as dual use.

00:10:58: So it serves domestic mission-critical loads, like massive data centers.

00:11:03: While also acting as export leverage in the global geopolitical game, power security and strategy are now one in the same.

00:11:10: And to manage all this complexity and risk, organizations are turning to some pretty advanced simulation tools.

00:11:17: Sameed Goenka introduced the idea of digital supply chain twins.

00:11:21: Okay, so what is that exactly?

00:11:22: It's essentially a real-time digital replica of your entire network.

00:11:26: It connects your ERP data, your supplier feeds, market intelligence, everything.

00:11:30: The goal is to move from reactive firefighting to proactive, predictive decision making.

00:11:36: So you could simulate the impact of a tariff increase or report closure before it happens.

00:11:40: Exactly.

00:11:41: And optimize the network in real time.

00:11:43: Finally, we saw this crucial maturation happening in sustainability.

00:11:47: We're moving past what you could call the early ESG euphoria.

00:11:50: And Estesia Miliveta highlighted the shift to which he calls essential sustainability.

00:11:54: The kind that delivers hard, measurable business value.

00:11:58: It reduces long-term costs, strengthens energy security, it makes it non-negotiable.

00:12:02: And the regulatory side is hardening.

00:12:05: Fast.

00:12:05: Take Europe's CBAM, the carbon border adjustment mechanism.

00:12:08: Right.

00:12:09: Giselle Witterhoven explained that this isn't some theoretical tax anymore.

00:12:12: It is now a hard trade and data regime.

00:12:15: It's forcing ports and traders to become operational compliance actors overnight.

00:12:19: And the documentation is the choke point.

00:12:21: That's what she stressed.

00:12:22: It requires incredible data discipline for every single shipment.

00:12:26: Missing or inaccurate data will become the silent killer of competitiveness for anyone exporting into Europe.

00:12:33: So if we pull all of these threads together, The central tension for twenty-twenty-six is just inescapable, isn't it?

00:12:39: AI makes insight generation abundant, makes the strategy easier to articulate.

00:12:43: But capturing that value requires relentless human judgment, execution discipline, system redesign, and the capacity to navigate this profound global risk.

00:12:52: All the friction points are organizational and cultural.

00:12:55: Which leads to the ultimate paradox for everyone in M&A and consulting heading into twenty-twenty-six.

00:13:01: If the speed of insight is the new differentiator.

00:13:04: and technology is commoditizing research and analysis making knowledge almost free.

00:13:10: How do firms redefine and, more importantly, price the indispensable human element?

00:13:15: That critical combination of judgment, courage, and context that actually turns speed into outcomes.

00:13:21: That's what executives are going to pay for, not the slide deck.

00:13:23: They'll pay for the ability to execute the courage to kill the forty-five percent of failed pilots and the judgment to know if that variance is an error or just, you know, cross-subsidiary complexity.

00:13:33: If you enjoyed this deep dive, new deep dives drop every two weeks.

00:13:37: Also, check out our other editions on private equity, venture capital, and M&A.

00:13:41: Thank you for joining us for this analysis of the twenty-twenty-sixth strategy and consulting agenda.

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