AI for Contract Review at Scale: Six Rules for Smarter Investment

The legal industry faces a pressing question around contract work: Where does AI create measurable value? And where does it simply add another tool, another subscription, and another layer of complexity?

Most legal departments already have access to Microsoft Copilot, ChatGPT Enterprise, specialized legal AI platforms, contract lifecycle management (CLM) systems, and an expanding list of point solutions. Yet many still struggle with fragmented workflows, inconsistent adoption, and difficulty demonstrating return on investment.

The challenge is no longer AI adoption. It's AI strategy.

According to the 2026 Thomson Reuters Future of Professionals Report, professionals working at organizations with a defined AI strategy are three times more likely to say AI is meeting or exceeding expectations than those without one. Success depends less on having the newest technology than on making smarter decisions about where—and how—to apply it.

During a recent Platforum9 discussion with founder Patricia Gannon, eBrevia co-founder and CEO Adam Nguyen shared a practical framework for evaluating AI investments based on more than a decade of building contract intelligence technology. Rather than focusing on the latest models or features, he argued that legal leaders should focus on process design, measurable outcomes, and long-term value.

Here are six principles every legal department should consider before investing in AI for contract review, contract negotiation, and contract lifecycle management.

1. Eliminate Before You Automate

At it's core, contract inefficiency is a business problem rather than an operational one. Research from World Commerce & Contracting estimates that poor contracting practices cost organizations an average of 8.6% of annual revenue, making contract workflows one of the highest-value opportunities for operational improvement.

Before introducing AI into any contract workflow, legal teams should first examine the process itself. Does every low-risk agreement require the same level of review? Are multiple approvals truly necessary? Are lawyers reviewing provisions the business has already decided aren't negotiable?

As Adam put it during the discussion: "A bad process does not become a good process because AI performs it faster."

That observation is consistent with broader enterprise AI research. McKinsey has found that organizations realizing the greatest financial returns from AI are significantly more likely to redesign workflows alongside AI implementation rather than simply automate existing processes. AI can accelerate a well-designed workflow, but it rarely fixes a flawed one.

For legal teams managing contract review at scale, eliminating unnecessary work before introducing automation often delivers greater value than deploying another AI tool.

2. Buy Outcomes, Not Features

Legal departments shouldn't buy technology because it can summarize a contract or generate a first draft. They should invest because it measurably improves the way contract work gets done.

Adam offered a simple question to evaluate any AI investment: Would lawyers notice if this tool disappeared tomorrow?

If the answer is no, the technology may not be solving an important business problem.

This distinction becomes especially important for enterprise contract review. Reviewing a single agreement with a general-purpose AI assistant is very different from consistently reviewing thousands of agreements across multiple business units.

Contract review at scale requires structured extraction, repeatable outputs, auditability, governance, security, reporting, and integration with existing workflows. Those operational capabilities—not simply access to a large language model—are what ultimately determine long-term value.

A useful way to think about ROI is to work backward from the outcome. A legal team reviewing 40,000 supplier agreements during an acquisition that reduces review time by just five minutes per contract saves more than 3,300 hours of legal effort before accounting for improvements in consistency, auditability, and downstream negotiations. That's the kind of operational impact legal leaders should be measuring.

3. Measure the Cost of the Work, Not Just the AI

AI pricing conversations often focus on licenses, tokens, or model costs. Those metrics only tell part of the story.

Instead, ask questions that reflect how your legal team actually works.

• What will it cost to review 500 contracts? What about 50,000?

• What does heavy usage look like compared with occasional use?

• Which costs are included, and which are additional?

• How much will implementation, integrations, governance, training, and ongoing support add over time?

• Can lower-cost models handle routine work while more advanced models are reserved for complex negotiations or high-risk agreements?

As Adam noted during the discussion, the real cost of AI isn't measured by the price of a token. It's measured by the cost of completing the work your organization actually performs.

That shift in thinking is becoming increasingly important as AI vendors move toward consumption-based pricing. Understanding the total cost of ownership (and measuring it against business outcomes) is a more reliable indicator of ROI than comparing subscription prices alone.

4. Building AI Is a Long-Term Commitment

Generative AI has made it easier than ever to build internal applications, but maintaining them is another matter.

Many legal departments are exploring whether to build AI tools for contract review or negotiation. In some cases, that makes sense. But software isn't finished once it's deployed. It requires ongoing maintenance, governance, testing, integrations, security updates, and continuous improvement as business requirements evolve.

As Adam noted, building is often easier than maintaining.

The most successful organizations are increasingly taking a hybrid approach: building targeted capabilities where they create competitive advantage while relying on specialized contract intelligence platforms for repeatable, high-volume contract workflows.

5. Modernizing Contract Management Doesn't Have to Mean Starting Over

Contract lifecycle management remains an important part of legal operations. What has changed is organizations' appetite for multi-year transformation projects before realizing value.

Instead of replacing every system at once, many legal departments are modernizing incrementally, starting with contract review, contract analysis, AI-assisted negotiation, or repository intelligence before expanding into broader lifecycle management.

Adam described this approach as "CLM Lite": solving the highest-value problems first while building on the systems organizations already have.

For many enterprises, improving contract workflows doesn't require starting over. It requires identifying where AI can remove friction today while creating a foundation for future modernization.

6. Measure Value Before Promising ROI

Don't measure AI success by licenses purchased, prompts submitted, or documents uploaded. Measure outcomes.

Has contract turnaround time improved? Are negotiations more consistent? Has outside counsel spend changed? Has legal capacity increased? Are lawyers spending less time on repetitive review and more time advising the business?

As Adam summarized: "Usage is not the same thing as value."

That perspective reflects a broader shift across the legal industry. Corporate legal departments are under increasing pressure to demonstrate measurable business value from technology investments. The organizations that establish baseline metrics before implementation and track improvements over time will be in a much stronger position to demonstrate ROI.

Better Questions Lead to Better Investments

Legal departments need better decisions about where AI belongs.

For organizations responsible for contract review at scale, AI-assisted negotiation, due diligence, and contract intelligence, the biggest gains won't come from chasing the newest model or buying the platform with the longest feature list. They'll come from simplifying workflows, choosing technology that solves measurable business problems, and evaluating success by outcomes rather than activity.

That's ultimately where AI delivers lasting value, and where the strongest return on investment begins.

About eBrevia

Established in 2011 and trusted by some of the world’s most prestigious companies, eBrevia is a leader in AI contract analysis, review, negotiation and management with clients in the US, EMEA, and APAC. For more than a decade, eBrevia has served law firms, corporations, audit/consulting companies, and financial institutions, such as Baker McKenzie, Norton Rose Fulbright, Kroll, SAP, Intel, PwC, EY, and MUFG.