Best of LinkedIn: Strategy & Consulting CW 26/ 27

00:00:00: provided by Thomas Allgaier and Frennus, based on the most relevant LinkedIn posts about strategy in consulting from CWB-CWB.

00:00:08: Frenness is a B to B market research company supporting consultancies with a marketing competition perspective for example in commercial due diligence's CDD CDD engagements.

00:00:19: don't forgive slow starts.

00:00:20: Frennus embeds directly into your consulting team as a white label market and competitive intelligence partner, slide ready fully adapted to your client's design an operational within twenty four hours.

00:00:31: You can find more info in the description.

00:00:32: Yeah,

00:00:33: and if you're operating anywhere They're fundamentally changing.

00:00:52: Right, and that is exactly our mission for this deep dive.

00:00:55: We've curated the absolute most critical strategy in consulting insights That surfaced across LinkedIn In calendar weeks twenty-six and twenty seven.

00:01:03: And Our goal here isn't just to you know.

00:01:05: read You what people are saying.

00:01:07: we want to decode The actual mechanisms behind these shifts because Honestly if your still relying on traditional playbooks of leverage and billable hours Well...the math simply doesn't work anymore.

00:01:20: It really doesn't.

00:01:21: We are officially moving way past the sort of conceptual hype phase of AI, we're into the hard unforgiving economics totally over.

00:01:33: So today we're going to explore how AI is forcing a complete redesign of the consulting operating model, and then look at why geopolitical strategy is transitioning from this theoretical risk into a massive board level driver value creation which is

00:01:48: huge right?

00:01:49: And finally will dissect human elements specifically Why getting super fast agreement in your leadership meetings actually often are really toxic trap.

00:01:57: Okay, let's unpack this.

00:01:59: Starting with the absolute core business model of professional services we really have to look at how firms actually make their money right because AI is actively breaking the traditional billable hour, and those pyramid leverage models that consulting PE and M&A firms have used for decades.

00:02:17: Jason Spencer actually brought up a really interesting observation about Accenture's stock repricing.

00:02:22: Oh

00:02:23: yeah I saw that!

00:02:24: Yeah he is suggesting the market is finally recognizing a structural shift where headcount just no longer equals value creation.

00:02:31: but I kind of want to pressure test this with you.

00:02:34: Sure, because stock prices fluctuate based on macroeconomic factors all the time right?

00:02:40: Why should we view this specific repricing as some fundamental break in the business model?

00:02:46: well it's a fundamental break Because It directly attacks The primary engine Of their growth.

00:02:50: i mean historically professional services firms grew revenue by doing one very Specific thing hiring

00:02:56: more people

00:02:57: exactly Hiring More smart People and then Billing them out at Super High Utilization rates.

00:03:01: Your revenue was directly tethered to your headcount.

00:03:03: But the market is realizing that AI just completely severs that tether.

00:03:08: if you have an autonomous agent That can handle the data synthesis, but You know used to require a team of ten associates working

00:03:15: all weekend

00:03:16: right?

00:03:16: Working on week and then your firm's value Is no longer determined by how many human hours you can sell

00:03:22: which perfectly contextualizes that insight from Hugo Rymakers.

00:03:27: he was highlighting this massive shift happening over at McKinsey where they're reportedly shifting partner pay toward equity.

00:03:33: Yes,

00:03:33: but walk me through the nuts and bolts of that like.

00:03:36: why does a shift away from billable hours automatically necessitate to shift from cash compensation?

00:03:45: Because the entire nature of the firm is basically changing.

00:03:48: They're going from a traditional partnership model to more of a software-enabled operating company.

00:03:54: Okay, make sense

00:03:55: think about the cash flow of a classic partnership.

00:03:57: You perform a service this month you bill for those hours?

00:04:00: You collect the cash and then you just distribute The profits to the partners at the end of year.

00:04:04: right it's very linear cash in cash out

00:04:07: exactly.

00:04:07: But software doesn't work like that at all.

00:04:09: when your building proprietary AI agents you incur these massive upfront development costs capital expenditure, basically to build a system that will generate value over a long timeline.

00:04:22: Oh I see!

00:04:23: Yeah you aren't selling the time it took to build the model.

00:04:26: You're deploying an asset and when you start operating like a tech company building scalable assets rather than renting out human-time... ...you actually have to compensate your leaders Like A Tech Company

00:04:38: Which means equity Right..You

00:04:40: Compensate them with Equity in The Long Term Value of the Systems They are Building.

00:04:44: that completely flips the cost structure of a firm on its head.

00:04:48: And this is actually where Nadine Charlon's breakdown of AI economics becomes so incredibly critical.

00:04:54: Yeah, her post was spot-on.

00:04:55: She pointed out that this transition introduces an entirely new variable into affirms OPEX which is token economics because AI costs scale with usage.

00:05:05: And this is a point that frankly, a lot of leadership teams are still really struggling to grasp.

00:05:09: Oh for sure!

00:05:10: They're so used to traditional SAWS economics you know?

00:05:13: You buy a fixed license for software tool and your team can use it.

00:05:17: twenty-four seven right.

00:05:18: the cost is capped.

00:05:19: exactly The Cost Is Capped.

00:05:21: So let's ground This For A Second.

00:05:24: If I'm a partner at a consulting firm or say a PE fund i am very Used To Paying A junior Analyst Of Fixed Salary Let'S Say A Hundred Grand A Year.

00:05:34: Right.

00:05:35: Whether they work forty hours a week or eighty-hours a week crunching data in the due diligence process, my cost for their labor is fixed.

00:05:42: it's just predictable line item on a spreadsheet.

00:05:44: Yep But with token economics Your

00:05:47: costs are entirely variable.

00:05:49: Every single prompt every automated workflow every data reconciliation It all consumes tokens.

00:05:55: Wow

00:05:56: Yeah If you deploy this, you know, autonomous finance agent that runs every single night to reconcile millions of transactions and prepare management dashboards.

00:06:05: Your costs are tied directly to the frequency and complexity of that exact process execution.

00:06:10: It's

00:06:10: basically like moving from buying an all-you-can-eat buffet pass To paying for every single calorie you chew.

00:06:16: That is exactly what it's like.

00:06:17: Like if You aren't monitoring The computational efficiency these workflows your profit margins could just evaporate overnight without even realizing its happening

00:06:26: which demands an entirely new way of measuring success.

00:06:30: And Andreas Angelimiakis noted this, that we have to shift our thinking from traditional ROI to what he calls return on intelligence.

00:06:40: Wait okay hold on.

00:06:41: how do you mathematically calculate a Return On Intelligence?

00:06:45: Because that sounds like bit like a qualitative buzzword.

00:06:47: How does the CFO actually put it in a spreadsheet?

00:06:50: I know it's not like a buzz word but its very real.

00:06:52: You calculate it by measuring the value of the outcome against the blended cost of your human capital plus your token consumption.

00:06:59: Okay,

00:06:59: so its a blended metric?

00:07:00: Exactly!

00:07:01: you are no longer measuring the return on a single piece of software... ...you're measuring the efficiency of an entire workflow.

00:07:07: Give me example.

00:07:08: So if a hybrid team say three humans and two AI agents can deliver commercial due diligence report in four days instead You calculate the revenue of that engagement and then subtract The human salaries in this specific API costs incurred to generate it.

00:07:23: So, the buyer isn't paying for the hours anymore?

00:07:25: Right they're paying For the verified intelligence.

00:07:27: but This brings us To a massive existential problem doesn't?

00:07:32: yeah?

00:07:33: because if the business model shifts from selling hours to selling intelligence And AI is taking over the actual heavy lifting Of generating That baseline analysis Yeah What on earth happens to the human talent pipeline?

00:07:47: Yeah, that's scary part.

00:07:48: Mark Byer Schoder had a super compelling take on this.

00:07:51: he pointed out That AI is rapidly automating all the entry-level work The first drafts of proposals initial financial models baseline research

00:07:59: associate at work

00:08:00: exactly and He argues that while most firms view This as a massive productivity story it Is actually A really dangerous capability Story.

00:08:09: And he is absolutely right To sound the alarm On this because you know, that entry level grunt work.

00:08:14: It was never just about producing a deliverable for the client.

00:08:18: building a complex financial model from scratch tracing every single cell finding this stupid errors at two in the morning That Was The Training Ground?

00:08:27: That was literally the mechanism through which junior analysts developed intuition to spot flawed arguments and hidden risks later In Their Careers.

00:08:36: Let's actually pause on there because This Is Where The Rubber Meets Road And M&A.

00:08:40: Let's talk about terminal value in a discounted cash flow model.

00:08:43: Okay, yeah

00:08:44: for listeners who aren't you know swimming and DCF models every single day.

00:08:48: The terminal value is essentially the estimated value of a business beyond the forecasted period And it often makes up sixty seventy sometimes even eighty percent Of the total calculated value of the company trying to buy

00:09:00: huge chunk at the valuation

00:09:02: huge.

00:09:03: So if you have never built that model by hand How do you know when the AI is hallucinating a variable that throws the terminal value off by one hundred million dollars?

00:09:12: You don't absolutely.

00:09:14: Don't you lose the process through which theoretical knowledge transforms into operational expertise and That is a massive vulnerability for these firms.

00:09:23: Yeah, but what's fascinating here Is how Arjun Vir Singh from Arthur D little approached this.

00:09:29: he offered A really great perspective on how The role of the human has to evolve.

00:09:32: What did he say?

00:09:33: well He pointed out that as AI completely removes the scarcity of basic analysis.

00:09:40: The value of a consultant or an advisor shifts entirely to judgment, it's just a context and the willingness to actually take a contrarian view against the client assumptions.

00:09:51: But hold on let me push back on that for second.

00:09:53: Let's play this out logically.

00:09:54: Okay

00:09:55: If AI is doing all the baseline data synthesis And its getting exponentially cheaper more capable every single month Why wouldn't a massive private equity firm just bypass the consultants entirely?

00:10:06: Because

00:10:06: of the data.

00:10:07: But wait, A major fund has billions in assets under management.

00:10:11: they can afford to hire The top one percent of AI developers.

00:10:14: They can buy all the compute they need.

00:10:16: why when they just build their own walled garden intelligence and run All the models in house like why pay the consulting premium at all?

00:10:22: It's the logical question to ask for sure but it ignores the reality Of how these AI models actually learn.

00:10:29: Farat Dhudeja addressed this brilliantly by applying the Matthew principle.

00:10:33: The idea that to those who have, more will be given?

00:10:37: Exactly!

00:10:37: If you're an in-house PE firm your AI is only learning from the deals your specific firm evaluates.

00:10:43: Right

00:10:43: so your data pool was totally limited to your own pipeline

00:10:46: exactly.

00:10:47: but think about the proprietary datamode of a top tier global consulting firm.

00:10:52: they have historical data frameworks and outcome analyses across thousands of deals across every sector globally for decades.

00:11:00: Oh wow yeah

00:11:01: when you combine that incredibly vast proprietary training data with a high concentration of elite human talent the advantage just compounds.

00:11:09: AI doesn't democratize strategy.

00:11:11: it supercharges the firms that already have the best data and the best people.

00:11:15: so they just pull further ahead

00:11:16: right?

00:11:17: They will use to solve exponentially harder problems way faster than a walled garden in-house team ever could.

00:11:23: Okay, so if the baseline quantitative analysis is being commoditized by AI what are those harder problems that require this elite human judgment?

00:11:33: Well.

00:11:33: The

00:11:34: real world

00:11:35: right because the quantitative models might be solved But the physical reality of the world isn't.

00:11:41: and This perfectly leads us to our next major theme which is geopolitics.

00:11:45: a huge topic right now Because

00:11:48: While an AI agent can summarize a million page data room in seconds, it cannot tell you how a sudden regulatory shift into the South China Sea is going to break your target company's supply chain.

00:11:58: Exactly!

00:11:59: And geopolitics used to be treated as just like background noise and corporate strategy?

00:12:04: Totally It was something you read about in the Financial Times, or maybe you flagged it as a high-level macroeconomic risk and an annual report.

00:12:11: But it rarely dictated the day to date mechanics of commercial due

00:12:14: diligence.".

00:12:14: But now Dr.

00:12:15: Bernhardt-Gerre and Christian H. Rodriguez-Chefell are arguing that building what they call geopolitical muscle is mandatory.

00:12:22: board level capability?

00:12:23: Yeah...the research was eye opening!

00:12:25: They cited research showing While eighty percent of companies report significant geopolitical exposure, only about fifteen percent have actually systematically embedded geopolitical analysis into their core business decisions.

00:12:39: And that sixty-five percent gap between awareness and operational action—that is where companies will live or die in the next decade?

00:12:46: It's a huge blind spot.

00:12:47: Massive!

00:12:48: We are no longer operating in a world of frictionless globalization.

00:12:52: we're dealing with trade fragmentation aggressive dual use technology control the weaponization of supply chains, it's messy.

00:13:00: Gunn-Tarce Krolls highlighted this perfectly when he was analyzing The Baltics.

00:13:04: He pointed out that geopolitics has evolved from just a policy topic into a core investment factor Securities directly dictating where capital is being allocated.

00:13:13: Just look at the rise of French shorn.

00:13:14: Exactly Companies aren't looking for the cheapest labor anymore.

00:13:17: They are looking for most geopolitically secure jurisdictions because In a highly volatile global environment, predictability becomes the premium asset.

00:13:27: It's your ultimate competitive

00:13:28: advantage.".

00:13:29: But I need you to clarify this for me because it sounds familiar...

00:13:43: That's a really common question.

00:13:44: Are we just rebranding the corporate risk register to make it sound more sophisticated?

00:13:49: No, It is crucial distinction and Asutish Padi tackled this directly.

00:13:54: for most institutions standard ERM is purely defensive about buying insurance hedging currency creating disaster recovery plans but potty points out that geopolitics must now be viewed as top three priority value creation

00:14:09: Value creation.

00:14:11: Wait, how do you create value from geopolitical instability?

00:14:14: By using strategic foresight to outmaneuver competitors who are stuck in that defensive posture.

00:14:19: Oh interesting!

00:14:20: Yeah Rajan Arsetti and Alfonso Natale emphasize this is all about rigorous scenario planning.

00:14:25: You aren't trying predict the future perfectly.

00:14:28: your building operational flexibility So

00:14:30: you can react faster

00:14:31: Better than reacting So that when a disruption hits like a sudden tariff, a block straight and embargo you have already mapped out how to capture market share while your competitors are still scrambling just to find new suppliers.

00:14:43: It is an

00:14:44: active offensive strategy.

00:14:46: Here's where it gets really interesting though because navigating all of this You know the redesign of the AI operating model The token-based OPEX, the Frashering Global Map it ultimately comes down to the humans sitting around the boardroom table.

00:15:00: It

00:15:00: always does!

00:15:01: How

00:15:01: do leadership teams actually find that conviction to execute when the environment is just this chaotic?

00:15:07: Well, THAT is the ultimate bottleneck for any transformation.

00:15:11: I mean you can have the most sophisticated AI architecture in the world and the most rigorous geopolitical scenario plans but if the leadership team isn't truly aligned...this execution will fail.

00:15:21: Which brings us to The False Alignment Trap.

00:15:23: This was explored by Anise Haddad and Kristi R. Elmer, and they pointed out that fast agreement in a leadership meeting is usually a massive red flag.

00:15:31: when everyone nods their heads and smiles it often hides deep unspoken misalignment.

00:15:38: And the result?

00:15:39: Is at the burden of execution just lands right back on the CEO's desk a month later.

00:15:45: Because it's just the illusion of consensus.

00:15:47: People agree quickly because conflict is uncomfortable, but they haven't actually aligned on the painful trade-offs They have an agreed on.

00:15:54: you know what legacy projects are going to be killed to fund?

00:15:57: The new AI initiatives.

00:15:58: It is exactly like when a group of friends instantly agrees on a neighborhood for dinner Just to avoid and argument yes

00:16:05: perfect analogy, right?

00:16:07: Like you all agree to go downtown but nobody actually agreed on which restaurant to eat at or who is paying for whose driving.

00:16:13: So You just end up circling the block For an hour hungry and annoyed your The illusion of motion but zero direction.

00:16:19: And that is the exact mechanism describing what happens in these boardrooms and, In times of extreme change... That kind of fake certainty Is just toxic.

00:16:27: Brian Elliott summarized some great insights from leaders like Katie Burke and Daniel Sutton on this.

00:16:32: The core message was incredibly counter-intuitive.

00:16:35: They said the worst thing a leader can promise team right now is certainty

00:16:39: Because it's a lie.

00:16:41: Nobody knows exactly What the AI landscape will look Like in twenty four months Or how global trade routes Will shift.

00:16:47: Exactly.

00:16:48: When leaders promise that AI won't change headcount, or they guarantee a specific revenue outcome based on some fragile geopolitical assumption and then inevitably have to reverse course...they destroy trust.

00:17:01: And trust is hard to rebuild?

00:17:02: Right!

00:17:02: Trust is the only currency that matters in crisis.

00:17:05: Leaders who dropped the armor, who honestly admit their uncertainty and model had a navigate ambiguity.

00:17:11: Those are the ones that build highly resilient teams

00:17:13: And we actually have hard empirical data to prove this human element drives bottom line performance.

00:17:19: Peter Stavros at KKR which is one of the largest private equity firms in the world He implemented an empathy gym for leaders across portfolio companies.

00:17:29: The results from that initiative are just staggering.

00:17:32: But wait!

00:17:32: An Empathy Gym.

00:17:34: What does it actually look like in practice?

00:17:36: Are highly compensated PE partners out there doing trust falls at a retreat

00:17:39: center?".

00:17:40: No, no.

00:17:41: It's not about trust falls—it is about rigorous operational reps and its role-playing difficult conversations.

00:17:48: It is actively practicing how to listen to an employee's feels about automation for example without immediately dropping the spreadsheet of efficiency metrics on them.

00:17:59: It is treating empathy not as a soft skill, but has a highly trainable operational discipline.

00:18:05: And the ROI on that discipline is massive!

00:18:08: By training leaders to be more empathetic and human-centered KKR saw a median drop in the quit rate of thirty percent across those portfolio companies.

00:18:16: Thirty

00:18:16: percent is huge

00:18:17: In a professional services or portfolio environment where replacing top talent costs multiples of their salary in lost productivity and recruitment fees.

00:18:25: A thirty percent reduction in churn is a massive driver of EBITDA.

00:18:29: It really is.

00:18:30: and you know if we synthesize everything we've discussed today, A very clear picture emerges.

00:18:34: Yeah

00:18:34: what's the big takeaway?

00:18:36: As AI commoditizes quantitative logic data synthesis, and baseline modeling.

00:18:42: The skills that were once considered the absolute core of consulting in finance are just becoming table stakes right?

00:18:48: What remains as the ultimate competitive differentiators?

00:18:51: our human centered leadership, the ability to build trust And the strategic imagination to navigate a fractured geopolitical

00:18:59: map.

00:19:00: it Just completely reframes the profile of a successful leader.

00:19:04: You can no longer just be the smartest person in the room with the most complex Excel model.

00:19:09: Not anymore!

00:19:10: You had to be the architect of alignment, you have to harness both the token-based efficiency of artificial intelligence and the nuanced judgment of human experts.

00:19:19: And you have do all that while maintaining operational flexibility In a global economy That is constantly redrawing its own roles

00:19:26: The ground really shifting beneath our feet.

00:19:29: And that is exactly why we do these deep dives, to help you understand the mechanics of this shift.

00:19:33: so can stay ahead?

00:19:35: Yeah, absolutely.

00:19:43: Thank you so much for joining us and make sure

00:20:00: What does the middle management of tomorrow actually look like?

00:20:04: How do you build a career bridge between?