In a merger or acquisition, the target company's contracts are where much of its value and risk actually sits. Supplier agreements decide whether production keeps running after close, customer contracts decide whether revenue stays, and intellectual property terms decide who owns what. Due diligence teams have to read all of it, usually in a data room, usually against a deadline.
AI and contract lifecycle management software change how that work is done. Instead of sampling a few hundred agreements and hoping the rest look similar, teams can read the whole portfolio, sort it by risk and hand people the contracts that need judgment. This guide, refreshed for 2026, covers where the software helps before, during and after a deal, and where a person must still make the call.
Why contracts make or break due diligence
Financial statements show what a company earned. Contracts show what it has promised, to whom, and under which conditions those promises change. A supplier agreement may let the supplier terminate on a change of ownership. A customer contract may carry uncapped liability or a most favored customer clause that will clash with the buyer's own pricing. A license may not transfer at all.
None of these issues show up in the numbers until after close, when they become expensive. That is why buyers ask counsel to review the contract estate, and why the review is often the slowest part of diligence. The documents arrive as scans, Word files and spreadsheets from several systems, with no consistent naming and no shared metadata.
The traditional answer was to review the largest contracts by value and sample the rest. AI makes a different answer possible: read every agreement once, extract the terms that matter, and focus people on the exceptions.
Step one: migrate and extract the target's contracts
The first job is getting the target's contracts into one place. AI extraction reads each document, whatever its format, and pulls out the parties, dates, value, governing law, renewal terms and key clauses. Bulk upload means the team does not have to key in metadata by hand, and an extracted record links back to the source page so reviewers can check the wording.
This is the same work as any legacy contract migration, compressed into weeks. The quality of the extraction matters more than its speed: a reviewer should be able to see where each value came from and correct it, and corrections should be logged.
Step two: find the clauses that change the deal
With the portfolio searchable, the legal team can run the questions that shape the deal. The table below shows the clauses most diligence teams review first and why.
| Clause | What to look for | Why it matters |
|---|---|---|
| Change of control | Consent or termination rights on a change of ownership | Key suppliers or customers may walk or renegotiate |
| Assignment | Whether the contract can transfer to the buyer | Some agreements need consent before they move |
| Exclusivity and non compete | Restrictions on markets, products or customers | They can bind the combined company |
| Limitation of liability | Caps, carve outs and uncapped exposure | Unlimited liability becomes the buyer's problem |
| Intellectual property | Ownership, licenses and background rights | Confirms the buyer gets what it is paying for |
| Term and renewal | Expiry dates, auto renewal and notice periods | Shows which revenue and supply is at risk soon |
AI can sort the whole repository by these terms in hours. It can also compare each clause against the buyer's own playbook and highlight language that deviates from standard positions, along with contracts that may not meet data protection or sector rules such as GDPR or HIPAA. The change of control review use case shows this workflow in detail.
Step three: add contract context to financial diligence
Finance teams review revenue quality, customer concentration and cost commitments. Contract data sharpens each of those questions. A calendar of renewal and termination dates shows how much revenue is up for renewal in the first year after close. Pricing terms show whether price increases are capped. Minimum purchase commitments show costs the buyer cannot easily cut.
Generative AI can summarize each contract's commercial position in plain language, so a deal lead can read a one page view of a supplier relationship instead of a sixty page agreement. The summary is a starting point. Anything that affects valuation should be checked against the source contract by a person.
Step four: keep the repository working after close
Diligence work is often thrown away once the deal signs. That is a waste. The structured repository built for diligence is exactly what the integration team needs next.
- Consolidate duplicate suppliers and decide which agreements to keep, renegotiate or end
- Track consents and notices required by change of control clauses
- Assign owners to renewals and notice deadlines in the first year
- Move obligations such as service levels, reporting duties and audit rights into tracking, for example in Obligationflow
- Align the acquired templates and clauses with the buyer's clause library
When the combined contract portfolio lives in one system, with consistent metadata, the buyer can answer questions about it at any time instead of commissioning another review.
Where people stay in charge
AI reads faster than any team, but due diligence is a legal and commercial judgment. Good practice keeps people in control at three points: they confirm the extraction on high value and high risk contracts, they decide how each risk finding affects the deal, and they approve anything that leaves the review team. In Aavenir, a named person approves every AI action and every change is logged.
Data security matters too. A data room is confidential by definition, so the review platform should carry independent certification and should never use deal documents to train external models.
The bottom line
AI and contract lifecycle management software let deal teams read every contract in the target's portfolio, focus legal time on the clauses that change the deal, and carry a clean repository into integration. The speed is useful. The real gain is that nothing important is left in the unread pile.