Blackmere

PE Practitioners on Operational Value Creation

A synthesis of 35 voices on where returns come from now

Community Synthesis · Private Equity · March 2026

Based on a discussion in r/PrivateEquity

I posted a question on Reddit and got 35 answers worth reading.

A few weeks ago I posted a question on Reddit’s r/PrivateEquity: “Buying well and structuring well still matter, but more of the return now has to come from what happens inside the asset after close. Product, pricing, GTM, systems, data, AI, org design, procurement, working capital, integration. Is that overstated? Or is that where the consensus is shifting to for driving returns?”

Thirty-five practitioners responded—GPs, operating partners, portfolio company executives, people who left fund roles for operator roles, and a few sharp critics. What follows is not my thesis. It is theirs, organized by theme, with industry data added where it supports or complicates the claims.

01

Financial Engineering Hit a Wall

The End of Near-Zero Arbitrage

The most consistent theme across the thread was blunt: the financial playbook that powered PE returns for two decades has run its course. Not because it was wrong, but because the conditions that made it work have changed.

Arbitrage and value extraction have been maxed out, especially with the end of near-zero rate lending. There’s also intensifying competition, and increasingly more options for tools, processes, and strategies that can increase operational efficiency. This is a good thing and it’s about time, to be honest.

Others were more direct. One commenter put it in three words: “Financial leveraging is dead.” Another pointed to the secondary effects—the tough exit environments that result when valuations were underwritten to optimistic marks and layered with complex debt instruments, making the path to exit increasingly narrow.

The numbers back this up. Bain’s 2026 Global PE Report shows $3.8 trillion in unrealized value sitting across roughly 32,000 unsold portfolio companies, with average holding periods drifting toward seven years. Entry multiples remain at record highs. When you can’t underwrite to multiple expansion and you’re holding longer, the return has to come from what happens to the business between close and exit.

02

12 Is the New 5

Why the Old Playbook Needs More Levers

Several practitioners offered the quantitative framing for what others described qualitatively. One referenced Bain’s research on value creation levers—a heuristic that captures the shift cleanly.

Bain captured it well with their “12 is the new 5” heuristic.

The math behind the heuristic is straightforward. In 2015, a typical PE buyout could hit a 2.5x MOIC with roughly 5% annual EBITDA growth—50% leverage at 6–7% interest rates, and multiple expansion doing the heavy lifting. In 2025, the same 2.5x return requires 10–12% annual EBITDA growth, because leverage is down to 30–40% at 8–9% rates and multiples are flat. That gap between 5% and 12% is the operational value creation challenge in a single number.

A practitioner with deep GP-level experience laid out why the old formula broke:

Multiple expansion carried a lot of PE returns for a long time, buying at 8x and selling at 12x did a lot of heavy lifting and that tailwind is largely gone. Rates, valuation compression, and a tougher exit environment mean you can’t underwrite to multiple expansion with the same confidence.

As one commenter framed it: “a lot of the upside is going to need to come from improving the business… pricing, ops, GTM, all that stuff.” The direction is consistent across the thread, even if the speed of the shift is debatable.

03

The Capability Gap at the GP Level

Finance People Doing Operations

If the previous sections describe what changed, this one describes why firms are struggling to adapt. The sharpest comments in the thread called out a talent mismatch at the fund level itself.

PE is going to need to get a LOT better at identifying and recruiting operational value creators at the fund level. The traditional playbook of just hiring gray haired former execs who talk a lot but won’t get hands-on with anything, or ex-MBB kids who can build spreadsheets and PPTs but have never actually operated anything isn’t paying off any more.

Another commenter reinforced this from a different angle: “Creating value in a spreadsheet or PPT is easy. Creating value in the real world when the heat is on is a very different story. Most PE hires tend to fit the same mold, and at this point that mold is exhausted.”

One GP building a new fund from first principles was even more direct:

MBB/Finance people are the LAST talent that’s needed on a team that actually augments a business. I can model in my sleep, what do I need someone to do that for? What’s harder is creating a system that empowers ops leaders to both create AND turn the wheel in a way that improves the business… It’s just a completely different way to think and is closer to founder thinking than PE thinking.

My own path fits the pattern these commenters describe. I led Alpine Investors’ largest software roll-up—integrating four companies into a unified platform—as an operator who got pulled into PE, not a finance person who studied operations. The difference shows up in the first week post-close: the gap between knowing what should change and having the capability to change it is where most value creation plans stall.

04

The Operators Are Leaving Finance

From Modeling to Making

A recurring pattern in the thread: practitioners describing their own career migration from fund-level roles into portfolio operations.

I left PE for portfolio ops once I realized modeling and memo writing were fake business. You’re so far away from the action it’s almost laughable.

That commenter now holds a Chief Transformation Officer role at a portfolio company. They are not alone. The thread surfaced a broader trend: the most capable operators increasingly prefer to be inside the asset, building, rather than sitting at the fund level directing from a distance.

The economics of the operating partner role came up repeatedly. An experienced OP described unprecedented demand:

Demand for real Ops has never been higher. Throw AI fuel on the fire, and it’s a very busy time. Funds and PortCo CEOs are bored with OPs that don’t get their hands dirty. To be successful these days you are expected to deliver value within 90 days… I’ve had multiple cold inbound for OP work at different funds in the last week. I’ve never had cold inbound before and would usually have a few fund calls a year—not a week.

The structural tension is clear. The people who can create the most value often will not give up the optionality to work with multiple firms. A practitioner focused on labor efficiency and sales noted that some functions require full-time authority—“sitting in the company full time with max authority works best for me”—while others, like supply chain optimization, are better suited to consulting engagements.

I’ve been offered Operating Partner roles and never taken them, for the same reasons these commenters describe. Independent advisory—capped at a few concurrent mandates—is one response to that tension. It trades scale for depth. It doesn’t work for firms that need twenty portfolio companies covered. But the demand pattern the commenters describe matches what I see: more inbound, more urgency, and less patience for advisors who stay at the board level.

05

AI as the New Frontier—and the Enshittification Debate

Where Value Creation Meets Its Critics

AI surfaced throughout the thread as the value creation lever practitioners are most focused on right now. But the discussion also produced a sharp counter-narrative that deserves honest engagement.

Several commenters questioned whether “operational value creation” is just a polished way of describing what critics call enshittification—the systematic degradation of products through cost-cutting and extraction. One commenter was hopeful: “One can dream that enshittification is over.” Another was blunt: “Operational value creation is a nice way of saying enshittification.” A third predicted: “The enshittification has not peaked. It hasn’t even begun to peak.”

Another practitioner noted the damage is real and has consequences beyond the portfolio:

Using operators and growing value were the start, then in different macro cycles this got lost, and now coming back to this. However, a lot of damage has been done and many founders have heard earned horror stories and are skeptical.

The enshittification critique is worth taking seriously. But it helps to ground the AI discussion in numbers. Citadel Securities’ February 2026 macro analysis pointed out that software engineer job postings are up 11% year-over-year (Indeed data), that AI still represents roughly 2% of U.S. GDP ($650 billion), and that technological adoption follows S-curves with natural plateaus—not exponential hockey sticks. The displacement narrative outpaces the adoption data.

That said, the enshittification critics are not wrong about the incentives. The distinction is whether AI is deployed to improve unit economics by making the product better—higher willingness to pay, lower churn—or by making it worse at lower cost. In one engagement I led, AI-powered document ingestion compressed a 45-day manual process to one business day while improving accuracy. The product got better for the customer. That is different from replacing human capability with inferior automation to save headcount.

For PE firms, Citadel’s compute cost framework is useful here: when the marginal cost of compute exceeds the cost of human labor, substitution stalls. AI value creation has an economic boundary, not just a moral one. The practitioners in this thread who are deploying AI well understand both.

06

What This Means

Three Implications for the Industry

The financial engineering era is not over—leverage and structure still matter—but the thread converges on one point: financial engineering is no longer sufficient. The return increasingly has to come from what you do with the asset after you buy it. Three implications:

  • The GP talent model needs to change.

    Funds need operators who have built and shipped—not just modeled and presented. The traditional playbook of hiring former executives who advise from a distance, or ex-consultants who can build decks but have never run a P&L, is producing diminishing returns. Bain’s report notes that winning firms will “invest in talent and AI, and move from full potential diligence to execution on Day 1.” The next generation of GP-level talent looks more like founders than financiers.

  • Operating partner economics must evolve.

    The best operators will not accept the current structures. When demand for operational talent is at all-time highs and the best practitioners can create outsized value across multiple portfolio companies, the economics need to reflect that. Independent models, meaningful carry, and decision-making authority are the minimum. Firms that treat operating partners as cost centers will lose them to firms—or independent practices—that treat them as alpha generators.

  • AI is not optional—but it requires operational depth.

    AI as a value creation lever works when the person deploying it understands the asset deeply enough to know where AI creates value versus where it creates noise. Some firms do this well through platform advisory teams; others through embedded operators. The model matters less than whether the person deploying it understands the product, the customer, and the economics. The enshittification critique is the market’s way of saying: we can tell the difference.

The practitioners in this thread are describing a shift in where returns come from. Whether it is a permanent change in the asset class or a temporary adjustment to a rate environment remains an open question—one that depends on whether the current exit and rate dynamics persist, and whether firms that invest in operational capability outperform those that don’t. That data doesn’t exist yet in a rigorous form. It should.

Full thread: r/PrivateEquity. Thank you to every practitioner who contributed.

Mo Battah advises PE firms on technical diligence and post-close execution. mo@blackmere.ai