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Best of LinkedIn: Strategy & Consulting CW 50 - 01 · Best of LinkedIn: Strategy, M&A & Investments

• 0:00
  Speaker 1: provided by Thomas Allgaier and Freeness, based on the most relevant LinkedIn posts about strategy and consulting from CW-Fifty to One.
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  Speaker 1: Freeness 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: This edition is brought to you by our partner Dealroom.
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  Speaker 1: Help shape their state of M&A's twenty-twenty-six report by taking their survey.
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  Speaker 1: This is your chance to share how you approach sourcing, diligence and integration, and see how your experience compares to peers.
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  Speaker 1: Final link to the survey in the description.
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  Speaker 1: Welcome to the deep dive.
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  Speaker 1: Our mission today is pretty critical for anyone in M&A, private equity, venture capital, really the whole consulting ecosystem.
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  Speaker 1: We're trying to extract the unified strategy and consulting agenda for twenty twenty six.
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  Speaker 1: And this isn't just, you know, a list of trends.
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  Speaker 1: It's more of a hard headed reality check.
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  Speaker 0: Exactly.
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  Speaker 0: A reality check based on where the top thinkers are actually placing their bets.
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  Speaker 0: We've pulled insights from all over the professional sphere and the message is it's really unified.
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  Speaker 0: It is.
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  Speaker 0: The focus has shifted so decisively away from general strategy, from just experimenting.
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  Speaker 0: It's all about execution discipline and measurable returns now.
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  Speaker 0: The playground phase is over.
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  Speaker 0: It
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  Speaker 1: is.
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  Speaker 1: The whole mission is converting this explosion in AI adoption into tangible bottom line impact.
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  Speaker 1: And you have to do that while managing global volatility and just trying to keep your operating model from breaking.
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  Speaker 0: a small task.
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  Speaker 1: So it's a tiny one.
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  Speaker 1: So let's unpack this.
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  Speaker 1: We've clustered the material into four critical things, and our first one jumps right into the deep end.
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  Speaker 1: Yeah.
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  Speaker 1: It's that AI is shifting from simple experimentation to rigorous value proof.
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  Speaker 1: And that's demanding fundamental changes to delivery models.
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  Speaker 0: You can really feel this tension in the source material.
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  Speaker 0: I mean, investments are surging, but the expectations for actual outcome based value are tightening dramatically, especially from the board level.
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  Speaker 0: Holger Paris noted this friction perfectly.
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  Speaker 0: He said that while AI adoption is new, ninety percent in large companies, which is
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  Speaker 1: huge.
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  Speaker 0: It's huge.
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  Speaker 0: But here's the kicker.
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  Speaker 0: Only about ten percent actually succeed in scaling those initial use cases to deliver measurable sustained value.
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  Speaker 1: So you have this ninety ten split.
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  Speaker 1: That's that's the whole challenge right there.
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  Speaker 1: It tells you the technology isn't the problem.
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  Speaker 1: No, it's the system around the technology.
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  Speaker 1: Yeah.
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  Speaker 1: Alan Duarte from Morgan Stanley really echoed this.
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  Speaker 1: He said, the trade for twenty twenty-six is crystal clear.
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  Speaker 1: It's show me the yearnings.
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  Speaker 0: No more narratives.
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  Speaker 1: No more leveraging AI language to justify a multiple.
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  Speaker 1: You have to demonstrate margin proof.
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  Speaker 1: Real operating leverage.
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  Speaker 0: And this demand for measurable impact, it totally changes how you have to manage failure.
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  Speaker 0: Chuck Whitten introduced this really fascinating concept.
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  Speaker 0: he calls a destruction strategy.
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  Speaker 1: Okay, what's that?
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  Speaker 0: Well, since something like forty-five percent of AI pilots hit roadblocks, I mean, that's nearly half.
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  Speaker 0: If your organization has a zero failure rate, it just means you're optimizing for safety.
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  Speaker 1: Not for impact.
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  Speaker 1: Exactly.
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  Speaker 0: You're not optimizing for genuine high-impact innovation.
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  Speaker 0: So the idea is that killing an underperforming pilot needs to be reframed.
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  Speaker 0: It's a capital allocation victory.
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  Speaker 1: So if a pilot dies, you should celebrate it.
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  Speaker 1: That sounds completely counterintuitive.
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  Speaker 0: It is.
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  Speaker 0: But it's a psychological shift.
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  Speaker 0: You stop the company from wasting resources chasing a dead end.
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  Speaker 0: And that requires a culture of, you know, deep psychological safety, which is way harder to engineer than any algorithm.
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  Speaker 1: And as organizations try to scale, the tools themselves are evolving.
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  Speaker 1: We're seeing the rise of what they're calling agentic AI.
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  Speaker 1: Right.
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  Speaker 1: Vincian Bhoshan reported that thirty-five percent of organizations are already using these systems.
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  Speaker 1: And these aren't static tools, right?
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  Speaker 1: They can autonomously plan and act and even learn.
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  Speaker 0: Which, of course, leads to this immediate and pretty intense debate about what we even call them.
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  Speaker 0: Is it an agent,
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  Speaker 1: a tool, or
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  Speaker 0: a teammate?
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  Speaker 0: Aziz Saudogo suggests agents are blurring the line between machine and teammate.
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  Speaker 1: I can see the appeal of calling it a teammate.
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  Speaker 1: It sounds collaborative, futuristic.
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  Speaker 0: Of course.
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  Speaker 0: But David Ritter pushed back on that.
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  Speaker 0: Hard.
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  Speaker 0: He argued it's dangerous, even repugnant.
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  Speaker 1: Strong words.
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  Speaker 0: Very.
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  Speaker 0: He believes that equating software with human beings makes automation bias much, much worse.
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  Speaker 1: That's the risk where you just defer to the machine, even if your gut tells you something is off.
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  Speaker 0: Exactly.
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  Speaker 0: Especially in high-stays environments like due diligence or trading, you cannot sacrifice human accountability for a convenient metaphor.
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  Speaker 1: That's a massive risk for M&A teams.
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  Speaker 1: You could overlook a critical liability just because the system didn't flag it.
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  Speaker 1: That accountability piece has to stay front and center.
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  Speaker 0: Absolutely.
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  Speaker 0: And this whole structural shift is redefining the value of consulting itself.
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  Speaker 0: Hugo Reimakers pointed out that the marginal cost of knowledge is just collapsing.
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  Speaker 1: We've all felt that.
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  Speaker 1: Research that used to take teams weeks now takes one person and a good prompt a few seconds.
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  Speaker 1: So if knowledge is practically free.
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  Speaker 1: What are consulting firms actually charging for?
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  Speaker 0: Precisely.
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  Speaker 0: The value shifts from human hours to compute, leverage, orchestration.
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  Speaker 0: And this is the key, unflinching human judgment.
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  Speaker 0: And this forces new delivery structures.
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  Speaker 0: Like what?
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  Speaker 0: Well, Scott Weiler mentioned BCG is using forward deployed consultants now.
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  Speaker 0: They're physically embedded with clients, building Taylor tools, solving problems in real time.
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  Speaker 0: It's less about advice and more about enablement.
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  Speaker 1: And that must create openings for new players.
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  Speaker 1: Shizama Gray noted that these AI first boutiques are thriving.
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  Speaker 1: They use speed and small teams to just run circles around the traditional heavier structures.
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  Speaker 0: Right, because you can't capture that AI value if your own organization is too slow or too rigid to handle
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  Speaker 1: it.
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  Speaker 1: Which brings us perfectly to our second theme.
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  Speaker 1: Because for any of this to work, For the boutique to be fast, for the value to be proven, you need perfect inputs.
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  Speaker 1: So our second major theme is data and analytics.
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  Speaker 1: foundations are reasserting themselves as the real AI bottleneck.
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  Speaker 0: This was maybe the most consistent complaint we saw.
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  Speaker 0: Rosario LaFaith was very clear that data governance is almost never treated as a strategic investment.
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  Speaker 0: lives in the basement, as he put it.
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  Speaker 0: Classic.
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  Speaker 1: Everyone wants the shiny AI outcome, but nobody wants to fund the boring plumbing underneath.
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  Speaker 0: And that's a fatal flaw?
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  Speaker 0: Yeah.
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  Speaker 0: Because advanced models don't fix bad data.
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  Speaker 0: They just scale its impact at machine speed.
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  Speaker 0: LaFace argued you have to elevate data governments to the executive agenda.
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  Speaker 0: Define ownership, fund the automation.
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  Speaker 0: You can't build a skyscraper on quicksand.
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  Speaker 1: And speaking of foundations, there's a huge strategic shift happening with ERP systems.
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  Speaker 1: Christian Rausch.
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  Speaker 1: covered the convergence of Sforana and Gen AI.
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  Speaker 0: Right.
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  Speaker 1: 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.
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  Speaker 0: That's a massive cognitive leap.
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  Speaker 0: A system of record just logs what happened.
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  Speaker 0: A system of reasoning uses those logs plus AI to provide decision intelligence.
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  Speaker 0: To anticipate what's next, it makes the ERP a living part of your strategy.
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  Speaker 1: But even with these new sophisticated tools, the human role in complex data work is still non-negotiable.
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  Speaker 0: Absolutely not.
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  Speaker 1: David Ersing shared a great real-world example during a NetSuite migration.
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  Speaker 1: He said AI tools saved them forty percent of their data cleansing time by flagging anomalies.
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  Speaker 0: Which is a huge efficiency gain.
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  Speaker 0: Forty percent is massive.
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  Speaker 0: But, and this is the cue, human expertise was still required for the context.
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  Speaker 0: What do you mean?
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  Speaker 0: 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.
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  Speaker 0: A human had to make that judgment call.
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  Speaker 0: Or it could see two vendors with slightly different names.
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  Speaker 1: And only a person would know they're actually the same entity operating in different subsidiaries.
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  Speaker 0: Exactly.
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  Speaker 0: The machine spots the irregularity.
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  Speaker 0: The human provides the context and the judgment.
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  Speaker 0: which actually connects perfectly to our third theme.
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  Speaker 0: Once the tech is deployed and the data is clean, the process is still run by people.
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  Speaker 0: And this theme is all about the operating model reality checks and the needs it for execution discipline.
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  Speaker 0: This
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  Speaker 1: is the seventy percent problem, right?
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  Speaker 1: Milo Niemler highlighted that classic ten-twenty-seventy model.
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  Speaker 1: Seventy percent of AI value comes from the messy parts people, processes, change management.
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  Speaker 0: Oh, where do we spend all the money and focus?
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  Speaker 1: On the ten percent.
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  Speaker 1: the algorithm, the tech itself.
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  Speaker 1: So we've built this brilliant technology that just runs into the brick wall of our own org chart.
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  Speaker 0: Exactly.
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  Speaker 0: And Mark Byer Shoder drilled down into one specific type of friction, coordination overload.
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  Speaker 0: 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.
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  Speaker 1: And his solution is to restore momentum by removing those dependencies, forcing decisions to happen where the work is actually being done.
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  Speaker 0: empowering the edges of the organization.
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  Speaker 0: It just makes sense if you want to capture the speed benefits of AI.
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  Speaker 1: So when technology becomes the great equalizer, what differentiates one company from another?
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  Speaker 1: Neha Cabra argued that humanizing will be the crucial differentiator in twenty twenty
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  Speaker 0: six.
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  Speaker 0: I like that.
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  Speaker 1: Yeah.
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  Speaker 1: Success has decided not by model accuracy, but by human judgment, by sequencing decisions correctly.
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  Speaker 1: and by being able to read the room when the data is ambiguous.
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  Speaker 0: She really drove that point home saying that execution is strategy.
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  Speaker 0: She argued the failure point isn't a technology issue, it's a decision design issue.
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  Speaker 1: Meaning the outcome is determined by things like system defaults, departmental incentives, handoffs.
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  Speaker 1: not the elegant strategy deck you presented six months ago.
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  Speaker 0: That's such a powerful distinction.
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  Speaker 0: It means the strategy team needs to think more like system architects, designing the decisions right into the operational flow.
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  Speaker 1: And the big takeaway, which Reshmi Ramachandran stated so powerfully, is that you have to fix the fundamentals first.
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  Speaker 1: AI is not a magic wand.
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  Speaker 0: Right.
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  Speaker 0: It will not fix your broken cash discipline or unclear roles or your dinosaur processes.
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  Speaker 1: If your organization is already flawed, AI won't fix it.
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  Speaker 1: It will just
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  Speaker 0: accelerate and amplify those existing problems.
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  Speaker 0: Your failures will just be faster, louder, and way more expensive.
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  Speaker 1: Which sets us up perfectly for our final theme.
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  Speaker 1: We've talked about the tech and the internal organization.
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  Speaker 1: Now we have to look at the external environment.
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  Speaker 0: Right.
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  Speaker 0: Theme four is about global strategy, resilience, and risk.
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  Speaker 1: The big message here is that geopolitics is no longer an optional overlay.
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  Speaker 1: It's not some risk that just lives with the supply chain director.
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  Speaker 1: It's a persistent daily constraint.
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  Speaker 0: Christian Rodriguez-Joufel stressed that tariffs trade friction.
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  Speaker 0: These aren't temporary disruptions anymore.
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  Speaker 0: They demand long-term cross-functional planning.
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  Speaker 1: And the response, according to Nicholas Lang, is to proactively build what he calls geopolitical muscle.
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  Speaker 1: You have to strengthen supply chain resilience now.
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  Speaker 0: And this pressure, combined with the energy transition, is driving industrial strategy in completely new ways, particularly in the US.
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  Speaker 1: Alan Duarte noted that energy is now being treated as a policy-backed growth market, not just a utility cost you try to minimize.
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  Speaker 0: That is a huge strategic shift.
• 10:52
  Speaker 0: Leaders have to treat power as strategy.
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  Speaker 0: He used nuclear as an example, framing it as dual use.
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  Speaker 1: So it serves domestic mission-critical loads, like massive data centers.
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  Speaker 0: While also acting as export leverage in the global geopolitical game, power security and strategy are now one in the same.
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  Speaker 1: And to manage all this complexity and risk, organizations are turning to some pretty advanced simulation tools.
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  Speaker 1: Sameed Goenka introduced the idea of digital supply chain twins.
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  Speaker 0: Okay, so what is that exactly?
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  Speaker 1: It's essentially a real-time digital replica of your entire network.
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  Speaker 1: It connects your ERP data, your supplier feeds, market intelligence, everything.
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  Speaker 1: The goal is to move from reactive firefighting to proactive, predictive decision making.
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  Speaker 0: So you could simulate the impact of a tariff increase or report closure before it happens.
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  Speaker 0: Exactly.
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  Speaker 1: And optimize the network in real time.
• 11:43
  Speaker 1: Finally, we saw this crucial maturation happening in sustainability.
• 11:47
  Speaker 1: We're moving past what you could call the early ESG euphoria.
• 11:50
  Speaker 1: And Estesia Miliveta highlighted the shift to which he calls essential sustainability.
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  Speaker 1: The kind that delivers hard, measurable business value.
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  Speaker 1: It reduces long-term costs, strengthens energy security, it makes it non-negotiable.
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  Speaker 0: And the regulatory side is hardening.
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  Speaker 0: Fast.
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  Speaker 0: Take Europe's CBAM, the carbon border adjustment mechanism.
• 12:08
  Speaker 0: Right.
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  Speaker 1: Giselle Witterhoven explained that this isn't some theoretical tax anymore.
• 12:12
  Speaker 1: It is now a hard trade and data regime.
• 12:15
  Speaker 1: It's forcing ports and traders to become operational compliance actors overnight.
• 12:19
  Speaker 0: And the documentation is the choke point.
• 12:21
  Speaker 0: That's what she stressed.
• 12:22
  Speaker 0: It requires incredible data discipline for every single shipment.
• 12:26
  Speaker 0: Missing or inaccurate data will become the silent killer of competitiveness for anyone exporting into Europe.
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  Speaker 1: So if we pull all of these threads together, The central tension for twenty-twenty-six is just inescapable, isn't it?
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  Speaker 1: AI makes insight generation abundant, makes the strategy easier to articulate.
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  Speaker 0: But capturing that value requires relentless human judgment, execution discipline, system redesign, and the capacity to navigate this profound global risk.
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  Speaker 0: All the friction points are organizational and cultural.
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  Speaker 1: Which leads to the ultimate paradox for everyone in M&A and consulting heading into twenty-twenty-six.
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  Speaker 1: If the speed of insight is the new differentiator.
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  Speaker 1: and technology is commoditizing research and analysis making knowledge almost free.
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  Speaker 1: How do firms redefine and, more importantly, price the indispensable human element?
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  Speaker 1: That critical combination of judgment, courage, and context that actually turns speed into outcomes.
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  Speaker 0: That's what executives are going to pay for, not the slide deck.
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  Speaker 0: 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.
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  Speaker 1: If you enjoyed this deep dive, new deep dives drop every two weeks.
• 13:37
  Speaker 1: Also, check out our other editions on private equity, venture capital, and M&A.
• 13:41
  Speaker 0: Thank you for joining us for this analysis of the twenty-twenty-sixth strategy and consulting agenda.
• 13:45
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