AI Will Reinvent Private Equity Diligence. The Trigger Won't Be Technology.

AI is compressing the work today. The market reset will come from partners choosing AI-native firms over the brands they’ve known.

In October 2024, we launched Flightline alongside a set of leading private equity investors. We spent our first three months meeting with leaders at 40+ firms. There was clear appreciation for how helpful generative AI could be, but pervasive trepidation about using it. Fewer than 10% of the firms we met with allowed their colleagues to load files into applications using third party LLMs. Many had banned them outright.

Less than two years later, every firm we work with offers employees access to leading AI models. Producing a research primer on a new industry now takes less than an hour and costs as little as $20 a month.

And yet, those same firms continue to engage McKinsey, Bain, and BCG (“MBB”) to perform commercial due diligence at $300,000 per week or more.

With the ever increasing intelligence of frontier models, what explains this disconnect?

We’ve spent nearly two years designing AI systems and workflows for private equity diligence. The “AI-native” approach we’ve built is grounded in AI from the start, not retrofitted onto a traditional pyramid. Based on that experience, we’re convinced AI-native consulting will overhaul commercial due diligence in three distinct phases.

Where we’re writing from

The Flightline team brings together private equity experience (GTCR, Motive Partners, Summit Partners, and Centerbridge), strategy consulting (McKinsey & Company), and AI-systems development for some of the world’s leading financial institutions (OnCorps AI).

Over the past nearly two years, our team has:

• Delivered 150+ research primers and due diligence studies to private equity firms evaluating new opportunities
• Built 50+ agents covering distinct components of a commercial diligence deliverable, with dozens of scorecards (i.e., evaluations) that grade the quality of AI-plus-consultant output continuously across Anthropic, Google, and OpenAI’s frontier models
• Conducted hundreds of primary research touchpoints across surveys, human-led expert calls, and AI bot-led calls
• Spoken in depth with leaders at 100+ private equity, private credit, and consulting firms about how they’re approaching AI in diligence

What private equity is actually buying

To understand where AI-native consulting can and cannot disrupt the existing market, it helps to be precise about what a top-tier consultancy is actually selling.

When a PE firm engages Bain, BCG, or McKinsey for commercial diligence, they are not buying a single product. They are buying a bundle of four things:

This bundle has held together for decades because the four components were essentially unobtainable separately. That is changing.

Phase I: Big chunks of the bundle have already collapsed

Phase I is happening now. It is the dramatic compression of the first two components of the bundle.

What AI has already compressed

The first two components of the bundle — (1) Primary Research & Analytics and (2) Synthesis & “the answer” — are not single tasks. They are produced through the day-to-day activities of an engagement: framing, acquiring and processing evidence, analysis, validation, synthesis, and production. AI compresses each of these activities to a different degree, and the consultant leads where judgment and accountability matter most. In our buy-side commercial due diligence work in Q1 2026, we observed approximately 62% labor reduction overall — concentrated, as the table below shows, in evidence processing and the drafting of synthesis, and far lighter in analytics and client engagement, where the consultant retains ownership.

Even without institutional expertise built up over decades, and without a McKinsey, Bain, or BCG logo on the cover, this model can credibly attack markets that incumbents have never served well. Pre-LOI diligence. Lower-middle-market deals. Private credit. Family offices. PE firms running more rigorous analysis on a larger share of their pipeline.

The strategic choice McKinsey, Bain, and BCG face

Meanwhile, the top-tier firms are doing what well-run incumbents should do. They are investing aggressively in AI to deepen what they sell to their best customers, not to chase the underserved segments.

Their strategic choice is constrained by two structural facts:

• Their business model is built around a fundamentally different cost structure. The top three strategy firms have built large, durable, and highly profitable businesses around their existing model. Bain has publicly noted that its PE consulting practice has grown eightfold over the past fifteen years and now represents about one-third of the firm’s global business, supported by a global network of more than 2,000 experienced professionals serving PE clients. That is not a business one rebuilds around a fundamentally different cost structure without enormous internal pain.
• Their core customers are not asking for cheaper. The PE practices of MBB generate the majority of their revenue from the largest PE funds — clients who are not particularly price-sensitive and who value depth and rigor over cost efficiency. There is limited pressure from these clients to deliver cheaper studies. There is, however, always pressure to deliver better insights.

The question is not whether top-tier firms can use AI. They can and will. The question is whether they are willing to use AI to reprice a premium product that clients are still willing to buy at premium prices. The rational answer is no — they will use AI to deliver more for their most important customers rather than less expensively for everyone else.

In Phase I, both sides win. AI-native firms get a real market. Top-tier firms get to deepen their offering for clients who can pay for it. Brand premium fully holds at the top because nothing about Phase I challenges it.

Phase II: AI-native firms develop institutional expertise of their own

Phase II is now forming. It is when AI-native firms use their experience to develop the third component of the bundle: Institutional Expertise.

AI-native firms don’t immediately have the decades of partner experience and benchmark archives that top-tier incumbents have built. But they accumulate institutional expertise differently — and in some respects, more completely — than traditional firms ever have.

Consider how a traditional engagement actually preserves what it learns. An enormous amount of validated analysis is produced in the course of a commercial diligence, and only a fraction of it survives onto the final slides. The rest — the nuance, much of the supporting evidence, the qualitative texture — is carried in the team’s heads, buried in folders no one queries again, and delivered verbally in the readout, then lost the moment the deck is archived.

We measured this in our own work. Our platform produces a diligence through a series of stage gates, where a consultant reviews and approves the analysis at each step before it passes to the next. When we compared the words a consultant validated and approved across all stage gates of an actual commercial diligence against the words that ultimately appeared on the slides, only 9.7% of the validated content made it onto the page. Roughly nine of every ten words of approved, reviewed analysis never reached the artifact.

In a traditional firm, that 90% disperses with the team that produced it. An AI-native firm captures all of it — every validated piece of content, every transcript, every supporting thread — in queryable, reusable form. The institutional expertise is no longer the firm’s accumulated partner memory. It is the firm’s accumulated, structured research base, growing with every engagement.

While AI-native consulting firms are growing in prevalence in this phase, there is a window for nimble boutiques and middle-market consulting firms to adopt the AI-native operating model — if they are willing to make tradeoffs that the MBB firms are unlikely to make.

The result for the MBB firms: pressure in the middle market, where more price-conscious clients increasingly have substantively comparable alternatives. They feel it less at the top, where credibility and brand still dominate the purchase decision. The natural response is to retreat upmarket — even more depth, more bespoke work, more partner attention, for the largest deals at the largest funds. Brand premium holds at the very top. The middle gets contested.

Phase III: AI-native consulting reaches parity and credibility becomes portable

Phase III is the question that defines the next decade: whether brand credibility becomes portable. By the time Phase III arrives, AI-native consulting has demonstrated track records at scale and depth that are defensible for nearly any deal type. The analytical quality is at parity or better. The accumulated institutional expertise is real. The price point remains a fraction of the MBB firms. The only remaining component of the bundle that these incumbents hold exclusively is the fourth one: Brand Credibility.

Brand is hard to disrupt. McKinsey, Bain, and BCG have built their reputations over decades, and the logo on the cover has real value to ICs, lenders, and LPs that does not transfer overnight to a new entrant.

We believe the final catalyst for Phase III will be people — specifically, senior consultants departing the largest firms for AI-native opportunities. There is a certain irony in this. We have spent this entire post arguing that AI is going to overhaul commercial due diligence. And yet the ultimate catalyst that tips the market is not a technology release or a benchmark threshold. It is a human decision by a senior partner to leave one firm and join another. Consulting is, and will continue to be, a people business. AI doesn’t change that. It just changes what the people are doing and where they choose to do it. Partners have left MBB before. What’s different now is that AI-native firms preserve the institutional infrastructure those partners historically had to leave behind.

AI-native alternatives now come in two forms: new entrants and established firms that have adopted the model. When partners leave the largest firms for either, they bring AI-native work product, accumulating institutional data, and an increasingly credible brand. Credibility starts to become portable. The same expert who carried a Bain logo on the cover for years can now stand behind work produced under a different brand. PE clients who have trusted that expert across multiple deals can follow them. The brand-on-the-cover stops being the sole locus of trust.

If this dynamic takes hold, it becomes self-reinforcing. Each partner who moves makes the AI-native alternative more credible. Greater credibility attracts the next partner, who brings his or her own relationships. The compounding can be slow at first but then moves faster than the consensus expects.

We don’t claim to know exactly how Phase III unfolds. The catalysts we are tracking — analytical parity, accumulating institutional data, and senior talent migration — are visible today but moving at different speeds. The firms that win Phase III are the ones who position for it during Phase II, by building delivery quality that senior consultants would be proud to put their names on, and institutional infrastructure that makes those names portable.

What’s next at Flightline?

If you are a private equity investor, a consulting partner, or someone trying to understand where this industry is going, our goal is to be the most credible and useful source you read on it. And we’d love to hear from you.

You can contact the team at flightlinedata.com/contact or follow Flightline for what comes next.