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M&A associates using AI in due diligence, contract review, and closing checklists report saving 15-30 hours per deal. Here's how the workflows actually run.
2026/03/07
The M&A associate's workload hasn't gotten smaller. If anything, the expectation from partners and clients is that deals close faster, diligence is more thorough, and junior lawyers produce more output with fewer errors. AI tools have entered this environment not as a luxury but as an expectation — at least at the firms that have moved early.
This guide is written for working M&A associates, not technology buyers. It answers practical questions: which tasks AI actually handles well in a deal context, where the tools fall short, how to manage privilege when third-party platforms touch client documents, and what the realistic time savings look like across deal stages.
We interviewed fourteen M&A associates across four firms — two Am Law 50, one regional, one boutique — and tested the primary platforms against sample due diligence materials over a six-week period. None of the vendors reviewed here sponsored or reviewed this content.
Due diligence is the highest-volume, most time-sensitive phase of any acquisition — and the area where AI tools have delivered the most documented value. The core workflow hasn't changed: you receive a data room, you need to review hundreds (sometimes thousands) of documents, flag issues, and populate a diligence report. What has changed is how much of that work can be handled at machine speed.
The standard AI-assisted diligence workflow in 2026:
In our testing, Kira Systems performed best on structured document review — particularly for extracting defined terms, key dates, change-of-control provisions, and assignment restrictions from commercial contracts. Its training on legal document types is visible in the precision of its extraction.
Luminance stood out on cross-document pattern recognition. When reviewing a data room with 400+ supplier agreements, Luminance identified three agreements where termination rights were subtly inconsistent with the target's representations — a finding that the lead associate confirmed would likely have been caught by human review, but only after significantly more time.
eBrevia and Kira are the most comparable head-to-head. For a detailed breakdown, see our compare Kira Systems vs eBrevia analysis. The short version: Kira wins on contract extraction accuracy, eBrevia on ease of onboarding for new deal teams.
Documented time savings from associate interviews:
| Deal Stage | Traditional Hours (Associate Estimate) | AI-Assisted Hours | Hours Saved |
|---|---|---|---|
| Data room categorization | 8–12 | 1–2 | 7–10 |
| Commercial contract review (100 docs) | 40–60 | 12–18 | 28–42 |
| Employment agreement review | 15–20 | 5–7 | 10–13 |
| IP assignment review | 10–15 | 3–5 | 7–10 |
| Diligence report drafting | 20–30 | 8–12 | 12–18 |
| Total (mid-market deal) | ~120 | ~45 | ~75 |
These are associate self-reported estimates, not billing records, so treat them as directional. The 75-hour saving figure aligns with what vendors claim, though the actual number depends heavily on data room quality and deal complexity.
Beyond diligence, AI tools are increasingly embedded in the contract review and negotiation workflow — the phase where M&A associates spend significant time on purchase agreements, ancillary documents, and disclosure schedules.
Harvey AI has become the preferred tool at several BigLaw firms for purchase agreement review. Its approach differs from Kira and Luminance: rather than extraction-focused analysis, Harvey operates more like a senior associate who has read thousands of M&A agreements and can identify positions that deviate from market norms.
In our testing, Harvey was given a 120-page stock purchase agreement and asked to flag provisions that were seller-favorable relative to market. It identified 14 provisions worth flagging. A partner-level review confirmed 11 as genuinely worth negotiating — a solid result for a first-pass review that took under six minutes.
The workflow that associates described most frequently:
Step 1: Harvey or comparable tool conducts market-norms review of draft agreement Step 2: Associate reviews flagged provisions, applies deal-specific context, and confirms/discards flags Step 3: Associate drafts negotiation comments; Harvey assists with alternative language suggestions Step 4: Redlined agreement returned to opposing counsel
This workflow compresses what would typically be a 6–8 hour first-pass review into roughly 2–3 hours of focused associate time. For the solutions for contract review use case more broadly, the pattern is similar across practice areas — but M&A agreements benefit most because of their standardization.
Two tasks that are tedious, error-prone, and time-consuming: populating disclosure schedules and maintaining closing checklists. Both are well-suited to AI assistance.
Disclosure schedules require cross-referencing representations in the purchase agreement against actual company records. An AI tool that has ingested the agreement can generate a schedule template pre-populated with the relevant representation text, flag which representations require substantive disclosure, and identify inconsistencies as the client populates the schedule.
None of the five platforms we tested does this perfectly — it remains an associate task — but Harvey and Luminance both offer meaningful workflow support. Harvey's document generation capability is stronger for drafting schedule preambles and boilerplate. Luminance's cross-reference detection is more reliable for flagging potential inconsistencies between the agreement representations and the draft schedules.
Closing checklists are a different problem: maintaining a live document that tracks hundreds of deliverables across parties, with status updates, responsible parties, and due dates. AI tools are not yet handling real-time checklist management autonomously, but they do accelerate checklist creation from scratch. Harvey can generate a first-draft closing checklist from a purchase agreement in under two minutes — a task that traditionally takes 2–3 hours.
For deals requiring HSR filings, CFIUS reviews, or other regulatory approvals, AI tools play a supporting but not primary role. The regulatory substance requires human judgment. But AI meaningfully accelerates the document preparation phase.
Specifically: generating the initial draft of HSR narrative sections, reviewing the target's customer and revenue data for HHI calculations, and cross-checking filing completeness against current FTC/DOJ guidance. Associates at two firms described using Harvey for HSR narrative drafts, with partners then editing for strategic framing.
One compliance-specific note: for CFIUS filings, which require careful attention to controlled technology and foreign ownership disclosures, AI tools should be treated as drafting assistants only. The substance of the filing analysis must remain with attorneys who understand current CFIUS policy, which shifts faster than any AI training data.
For a broader view of solutions for due diligence including regulatory components, the key principle is: use AI where the task is document-processing intensive and human judgment where the task is strategically sensitive.
Every M&A associate using AI tools on live deals is operating in a privilege landscape that is still being defined. The core concern: when client documents are processed by a third-party AI platform, does that transmission implicate the third-party doctrine and risk privilege waiver?
The short answer from current case law and ethics guidance: probably not, if the vendor relationship is properly structured. Most major AI vendors execute NDAs and data processing agreements that include confidentiality protections and attorney-client privilege preservation language. But "probably not" is not "definitely not," and the prudent approach involves several steps.
Privilege protection checklist for M&A AI use:
The question of client disclosure is evolving. No ethics opinion currently mandates disclosure of AI tool use in due diligence in the way that, say, fee-sharing arrangements require disclosure. But some sophisticated clients are beginning to ask. The safest posture: update your standard engagement letter to address AI tool use generally, and flag any instance where particularly sensitive client materials will be processed.
For the privilege analysis in more depth — including recent court decisions — see our companion post on attorney-client privilege and AI in courts.
Which AI tools work best for M&A due diligence specifically?
For high-volume contract extraction and issue flagging, Kira Systems and Luminance are the two most purpose-built platforms. Kira has broader market penetration and more established training data on legal document types. Luminance's AI architecture performs better on cross-document pattern recognition. For purchase agreement review and negotiation support, Harvey AI is increasingly the standard at large firms. Most deals at sophisticated firms use at least two tools across the deal lifecycle.
How accurate is AI in M&A contract review — what is the error rate?
In our six-week test, Kira Systems achieved approximately 94% recall on defined-term extraction and 89% recall on clause flagging across a standardized test set of 200 commercial contracts. Luminance achieved 91% and 86% respectively on the same set. These numbers are strong but not perfect — every AI-generated review requires associate verification. The risk of a false negative (a flagged issue missed by AI) is the primary concern; false positives (unnecessary flags) cost time but are less dangerous.
Do we need to disclose AI use to clients in M&A matters?
Current ethics guidance does not universally require disclosure of AI tool use for due diligence or contract review. However, engagement letters should address AI tool use as a general matter, and some sophisticated institutional clients are beginning to request it. If client documents are processed by a third-party platform, you should confirm the platform's data handling meets your confidentiality obligations under the engagement. Disclosure posture is evolving — check your bar's most recent ethics opinion.
What is the cost of AI tools per deal in M&A?
Cost structures vary widely. Kira and Luminance are typically licensed on an annual seat or matter basis — a full deal team license for a mid-market deal might cost $5,000–$15,000 in platform fees depending on deal size and document volume, offset against meaningful associate hour savings. Harvey AI is typically firm-wide licensed, making per-deal cost attribution dependent on your firm's allocation methodology. In most cases, the economics are strongly positive: the platform cost is a fraction of the associate hours saved at BigLaw billing rates.
Does AI tool use affect privilege for work product in M&A?
This is the most legally uncertain question in the space. Current consensus from ethics opinions and the limited case law is that transmitting client documents to a properly structured AI vendor — one with appropriate NDA, DPA, and confidentiality controls — does not waive privilege under the third-party doctrine. The key is "properly structured." Vendors that use client data for model training, have ambiguous data retention terms, or lack SOC 2 certification create real risk. Treat vendor due diligence as a legal issue, not just a procurement formality.
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