Every bidder now has the same AI. The edge belongs to the practitioners who know how to wield it — across diligence, integration, and value delivery.
Everyone Has the Same AI Now
AI has arrived in the deal room, and it has arrived for everyone. Every bidder and every seller can now rent the same models, point them at the same data, and draw on broadly the same horsepower. That means the tool itself is no longer an advantage. It is table stakes — both parties have equal access to the base tools, so neither gains an edge from that alone.
The advantage lies elsewhere. Call it alpha in a fund or economic value added in a corporate — the outperformance appears only when identical tools are placed in non-identical hands: when decades of transaction judgment decide what to ask, recognize which of several hundred or more machine-surfaced findings is the one that matters, and know how a deal behaves when it strays from the norm. At Ankura, the redesign of how a next-generation M&A process runs — from diligence through integration and value delivery — was not led by a technologist learning what M&A needs. It was led by a practitioner with more than 30 years across the full deal cycle and every transaction type — so the real nuances of how deals work, including the edge cases, are engineered into the approach rather than discovered mid-deal. The result is a way of working that is resilient by design: built to anticipate the exceptions and to help a client and its advisors navigate them when they hit.
Alpha for a fund or economic value added for a corporate — it appears only when identical tools are placed in non-identical hands.
Where Deals Are Won or Lost
In most transactions, the decisive risks and the largest sources of value do not sit in the financial model. They sit in operations, contracts, and systems — and most often in the seams between them, where one function’s assumptions quietly depend on another’s. Operational due diligence exists to find them before close; the same discipline then carries through integration and execution to turn what it finds into value actually delivered after it.
The method, though, has carried the same ceiling for 30 years. A team works a data room by hand, applies hard-won judgment about where issues usually hide, and races the clock. On a straightforward deal, experience is enough. On a complex one — a large data room, several prior acquisitions stacked one on top of another, a compressed timeline — no team can read everything. So it samples. And the risks that matter most are precisely the ones a sample is least likely to reach.
The risks that break a deal rarely sit where you are looking. They hide in the seams between operations, contracts, and systems — exactly where a sampled review does not reach.
What Modern Operational Diligence Looks Like
The difference shows up less in speed than in what the work can now reach — and in a deliberate division of labor between machine and practitioner that plays to the strengths of each.
Every rock, not just the likely ones. Traditional diligence mixed the science of knowing which part of the data room to open first with the art of sensing when something was worth a deeper look — and a team could only ever turn over so many rocks before the clock ran out. The approach now inverts that. A specific question is put to the data as a “diligence ticket,” and the machine runs it as a full, longitudinal sweep across the entire dataset, turning over every rock — the science, finally able to be done in a fully comprehensive way that was never possible by hand. The practitioner supplies the art: judging which findings are genuinely material, then directing the machine on where to challenge its own conclusion and where to prove it out. Coverage becomes total; judgment stays human.
Rigor that holds up. Reach is only worth having if it is right. Every finding is tied to its underlying source and confirmed by an experienced practitioner before it reaches the client. Because each source reference is a live link to the actual document in the data room, even the quality control pass runs faster and cleaner than a manual review that depended on remembering and relocating where a finding first arose. Speed never comes at the expense of a conclusion a client could not stand behind at the negotiating table.
Resilient by design. Because the workflows were shaped by someone who has seen where deals go sideways, they anticipate the exceptions rather than break on them. The unusual ownership structure, the contract that behaves differently on a change of control, the entity that does not transfer cleanly, the key employee whose true importance is driven by human dynamics an AI may never fully read — the approach is built to expect the deal that strays from the norm, and to bring the seasoned human judgment those moments demand.
One deal, one team — across every advisor. Large deals run on a crowded bench: separate advisors for financial, legal, tax, and commercial work, each expert in its own lane. The two failure modes are duplicated effort on one side and, on the other, an issue that falls between two advisors and reaches no one. Working from a shared base, the approach manages both — screening for overlap and, more importantly, hunting the cross-advisor seams where a problem in one firm’s workstream becomes visible only against another’s.
Diligence that flows into value capture. Findings do not stop at a report. Because the diligence knowledge is held in one place, it carries directly into integration and separation planning — Day 1 readiness, the operating model, the 100-day plan — so protecting and capturing value begins from what diligence has already learned, not from a blank page weeks later.
Two Sides of the Same Coin
Operational diligence asks a different question depending on which side of the deal it serves — but the discipline is the same. On the buy side, the work is about what a business truly costs to run and where its value can be grown or lost after close: the real condition of operations, the durability of margins, and the exposures a financial model quietly smooths over. On the sell side, and in every carve-out, the same lens turns to a harder problem — separation.
No two divestitures are alike, because no two parents are built the same way. One runs as a highly centralized enterprise — heavy shared services, common systems, a single operating model; another as a decentralized holding company whose business units already stand largely on their own. A business being lifted out of a parent is entangled with it in ways that can never be comprehensively addressed on a diligence checklist: shared systems, shared contracts, shared people, and shared costs that do not divide cleanly. Left unmapped, those entanglements become transition-services agreements that overstay their welcome, stranded costs the buyer quietly inherits, and dis-synergies that erode the deal thesis after the ink is dry. Finding them early — and planning for them before signing — is often the difference between a separation that protects value and one that leaks it.
And just as AI makes a comprehensive read of the data room possible, its ability to weigh a wide range of meta-data — the faint signals that betray an entanglement too subtle to spot by hand — makes that foundational part of separation planning broader, deeper, and faster.
Integrations and separations are two sides of the same coin. A separation simply adds a layer — entanglements, transition services, and stranded costs — that has to be found before it can be managed.
From Findings to Value Potential
A diligence finding is only worth what a client can do with it. The most useful operational diligence does three things at once. It prices risk, so a buyer knows what a problem will cost to fix and can carry that number into the negotiation. It protects the downside, surfacing the exposures that otherwise become post-close surprises. And it seeds the upside, turning a real understanding of the business into the first draft of the value creation plan.
Because the diligence and the integration work are done by one team drawing on one body of knowledge, none of that insight is lost in translation. The people who learned where the costs and risks live are the same ones who build the plan to address them — so the client crosses Day 1 already moving, rather than relearning the business from a report written by a team that has since moved on.
The cost of the alternative is rarely visible until it is too late: the exposure no one priced, the integration that stalls because a dependency was missed, the synergy that never materializes because the plan was built on a sample. This is not about a longer report. It is about making sure the expensive surprises happen before the deal closes, not after.
Diligence done well does not just find problems. It prices them, protects against them, and turns what it learns into the plan for capturing value after close
From Value Potential to Value Delivery
At the close of diligence and negotiation, months of effort have gone into structuring, negotiating, and drafting the agreements that maximize a deal’s value. But that phase ends with a stack of signed documents — and not a scrap of value has actually been created yet. Value is delivered only in what comes next: standing up and running the integration.
Here the same principle applies. Mining every advisor’s report, the full data room, and a working knowledge of the sector, the team builds base task plans customized to the specific deal far beyond what was ever practical by hand — then runs the program from them. Where an integration may have 10 or 15 functional leaders all feeding one plan, what used to be a configuration-control headache becomes a process AI manages quickly and accurately — freeing the deal team to spend its bandwidth on the transaction rather than the administrative heavy lifting.
And just as AI teases out the hardest-to-see entanglements in a separation, the same capability surfaces the hard-to-locate dependencies between functional teams — the places where one workstream’s plan quietly relies on another’s — whether the deal is an integration or a separation.
A signed agreement is not value. Value is delivered in the integration that follows — where a customized, AI-run program turns the plan into results
In Practice
On a recent engagement, Ankura led operational due diligence and integration planning for a private-equity–owned platform in a highly regulated, security-sensitive sector — a business built through several prior acquisitions and carrying the kind of classified and controlled-information footprint that makes diligence unusually demanding.
Working across thousands of documents rather than a sample, the team surfaced risks a siloed review would likely have missed — among them an interaction between a planned restructuring step and a change-of-control review that, left unaddressed, threatened operational continuity after close. Records that mattered but sat filed where no one would think to look were found and analyzed, and specialized systems and cybersecurity questions — critical given the target’s security profile — were pursued with the same reach as the rest of the review. A multidisciplinary team spanning operations, IT and data, cybersecurity, and M&A worked the deal as one, and every finding was verified before it reached the client.
The work did not end at a report. The same team carried the diligence knowledge straight into the integration — building the customized task plans, governance, and Day 1 readiness from everything the review had already surfaced, and coordinating the functional workstreams from a single, AI managed plan — so execution began creating value from the moment the deal signed rather than weeks later.
Beyond Diligence: The Rest of the Deal Cycle
We have focused here on operational diligence through pre- and post-close integration planning, execution, and value delivery, but the same principle — commodity tools, differentiated hands — reshapes the earlier phases of the deal cycle just as powerfully. Consider origination. The traditional target scan-and-screen is a manual, periodic exercise: A team pulls a list, ranks it, and by the time the next review comes around the picture has moved. Working with a corporate acquirer running a proactive M&A program, we rebuilt that process into something continuous — monitoring not only named targets but the shifting dynamics of the sectors they live in, so the trigger to act is caught the moment it appears rather than at the next quarterly look. When a target rises to the top, the same approach compresses the path from a prioritized name to a first CEO-to CEO conversation.
That work — re-engineering the front of the deal with the same practitioner-led discipline — is the subject of a forthcoming spotlight.
How Ankura Creates Value Across the Deal Lifecycle
We help acquirers see the whole target before close — and turn what diligence learns into value captured after it
1. Operational Due Diligence
- Whole-data-room analysis
- Cross-domain risk identification
- Quality of operations, supply chain, and cost
- Management and organizational assessment
2. Integration and Separation Planning
- Day 1 readiness
- Operating model design
- TSA and entanglement mapping
- 100-day and execution planning
3. Value Capture and Synergy
- Synergy identification and validation
- Cost, margin, and working capital
- Benefits realization tracking
- Risk and issue governance
4. Sector-Tailored Delivery
- Playbooks configured to sector and deal
- A connected toolkit from diligence to execution
- Senior-led teams, scaled by method
- Repeatable, executive-quality output
© Copyright 2026. The views expressed herein are those of the author(s) and not necessarily the views of Ankura Consulting Group, LLC, its management, its subsidiaries, its affiliates, or its other professionals. Ankura is not a law firm and cannot provide legal advice.
