Generative AI Changes the Contract Game. Not Using It Is Risky

For years, contract intelligence—turning contract terms into usable insights about risk, obligations, rights, and value —was treated like a vitamin: good for your long-term contract health, easy to defer. But artificial intelligence has changed the economics.

AI shouldn’t be treated as a feature layered onto a system, but as part of the system itself. When AI is designed around how legal work actually happens, it structures the right data going in, applies context and judgment in the middle, and produces outputs that tell you what to do next.

That’s how it moves beyond reporting to real decisions: reducing friction, improving workflows, helping teams operate at the right level.

Contracts are where risk sits, where revenue gets protected or leaked, where obligations quietly accumulate, and where commercial intent either survives execution or gets lost in it.

Generative AI can now read contract portfolios at scale, extract structure, compare clauses, surface deviations, and answer portfolio-level questions fast and affordably—enough to change what used to be a nice-to-have into operating infrastructure.

Contract intelligence’s real cost no longer is the review itself—it’s the missed risk, slow decisions, lost value, additional work, and issues pushed to senior lawyers or outside counsel. It shows up in contracts sitting in inboxes, deals losing momentum, and no one having a confident view of what is in the paper.

It’s also felt when similar deals end up with different terms, concessions get lost after signature, scope expands without a clear contractual basis, and contract lifecycle management migrations drag on because the underlying contract data is incomplete or inconsistent.

Storage Isn’t Intelligence

Most large enterprises already have contract repositories (digital filing cabinets for signed agreements) and many have invested in contract lifecycle management, or CLMs, to manage contract workflows, approvals, and storage. But contract storage shouldn’t be conflated with contract understanding.

Most CLM analytics are transactional. You have X types of contracts and Y types of clauses. These are useful to know, but they don’t tell you how to act on that information or extract contract value. CLM tools were designed to store documents, organize them well, and make them retrievable in a usable format. Then AI arrived.

Basic CLM AI focuses on extraction and classification, but a sophisticated AI system reasons across contracts, connecting clauses, context, and commercial outcomes. This form of advanced reasoning can operate at both ends of the CLM.

On the front end, it can clean and tag contract data before it enters the CLM, so obligations, fallback terms, approvals, and risk positions are captured consistently instead of being entered manually or missed. On the back end, it can extract data from existing repositories and find meaningful insights.

Take liability clauses, for example. A CLM might detect 20 standard liability clauses and list three that are off market. A sophisticated AI layer lets you interrogate why those three were accepted, then cross-reference the rest of the contract structure to determine whether there was a commercial reason or whether an exception simply slipped through.

It’s important to get the right person to do the right work, at the right level, in the right location, with the right technology. Corporate transactions—whether mergers, acquisitions, divestitures, or spin-offs—demand contract intelligence at scale. Traditional approaches rely on mass mobilization of lawyers to review and manage contracts, straining already-stretched legal departments.

Generative AI fundamentally changes that. Where acquirers once made decisions with limited visibility into a target’s contractual obligations, AI-enabled review now allows for quick and thorough analysis and help uncover risks and opportunities that might otherwise remain hidden until post-closing.

On the sell side, companies can analyze their portfolios more easily, and then respond to buyer due diligence requests more quickly and more accurately:

• In one mergers and acquisitions review, a global pharmaceutical company used AI to review individual clauses and identify which contracts created deal risk or needed consents, amendments, or other action. This was achieved in days, not months, under strict data-governance requirements.
• A global gaming company was able to extract key rights data from historical agreements in about one minute per contract, creating a reusable view of licensing terms that traditional review would have taken dramatically longer to produce.
• In another case, a global media company isolated 40,000 agreements relevant to a litigation claim from a fragmented contract estate and extracted key obligations, metadata, and requirements at a pace and cost that would have been prohibitive before generative AI. The result was structured and defensible, and data-backed evidence delivered quickly enough to protect the organization’s position and meet court deadlines.

The business impact: Teams know sooner which contracts need attention, where risk sits, and what has to happen next. That means faster diligence, fewer post-closing surprises, and a smoother path from signing to execution.

Under the Hood

When legal teams face an urgent need for comprehensive contract analysis under pressure, it’s important to have a solid regulatory or crisis response plan.

When a global software company faced a major service crisis, AI-enabled review allowed its legal team to analyze 17,000 contracts in days rather than months, slashing costs and giving the business a speedier view of contractual exposure. Without it, the company would have needed large-scale law firm mobilization or legacy review that would have taken months and still only covered a fraction of the agreements.

Contract work now has more robust outputs: risk, rights, deviation patterns, fallback behavior, and operational opportunities the business can act on. And the benefits are repeatable.

AI finally makes it affordable to know what is in your contracts. Now, choosing not to know becomes the riskiest decision.

This article does not necessarily reflect the opinion of Bloomberg Industry Group, Inc., the publisher of Bloomberg Law, Bloomberg Tax, and Bloomberg Government, or its owners.

Author Information

Nimal Hemelge is global head of practice operations at Factor, working with in-house legal teams to embed AI into contracting and commercial operations.

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