If you’ve read any recent study on AI in contract review, you’ve probably noticed a familiar pattern. Researchers compare AI tools against human lawyers, measuring their ability to identify key clauses, flag risks, and ensure compliance. The results are then framed in terms of precision, recall, and overall accuracy.
But here’s the problem: while accuracy is important, it’s not the only metric that matters. In real-world contracting, speed is just as valuable – arguably even more so.
A contract AI tool that is 92% accurate but completes a review in minutes is far more useful to a business than a human lawyer who is 99% accurate but takes days to finish the same task. Despite this, most research continues to focus on accuracy while completely ignoring AI’s real advantage: efficiency.
Why?
Is This Just a Lawyer Thing?
Lawyers are trained to be risk-averse. The legal profession is built on the idea that precision and caution outweigh speed. In fact, legal training often reinforces the notion that taking more time means being more thorough and that rushing leads to mistakes.
This mindset is reflected in how AI research is conducted. When law firms, legal tech companies, or academics evaluate AI tools, they tend to ask, “Is AI as accurate as a lawyer?” rather than “Does AI actually help businesses move faster?”
But the reality is that businesses don’t just care about whether AI gets every clause perfect. They care about whether AI speeds up their contracting process.
Because when contracts take too long, business suffers.
Why Speed Matters More Than Lawyers Realize
Contracting isn’t just a legal function – it’s a business function. A slow contract review process doesn’t just inconvenience lawyers; it delays revenue, drags down procurement, and slows growth.
Here’s why speed should be a bigger priority when evaluating contract AI:
✅ Faster deals = more revenue
Every day a contract sits in review is a day that revenue is delayed. If AI can cut contract review time from weeks to hours, that means businesses close deals faster, recognize revenue sooner, and improve cash flow.
✅ Sales teams can operate more efficiently
One of the biggest bottlenecks in sales isn’t finding customers – it’s waiting for contracts to get approved. When AI accelerates contract review, it empowers sales teams to move quickly, reducing friction in the sales cycle.
✅ Procurement and vendor onboarding improve
Slow contract review doesn’t just hurt sales; it also slows down vendor relationships. Businesses that rely on a steady flow of suppliers, partners, and service providers can’t afford to have procurement contracts stuck in legal review for weeks. AI can remove this bottleneck.
✅ Lawyers can focus on high-value work
When legal teams aren’t bogged down reviewing standard NDAs, MSAs, or vendor agreements, they can focus on more strategic work – whether that’s negotiating complex deals, mitigating risk, or advising the business on key decisions.
In short: speed isn’t just about working faster for the sake of efficiency. It’s about eliminating unnecessary delays that impact a company’s bottom line.
Accuracy Matters – But Only to a Point
Let’s be clear: accuracy does matter. If AI gets too many things wrong, any time saved by automation is lost when lawyers have to go back and fix mistakes. A system that misses key risks or misclassifies clauses creates more work, not less.
But the obsession with maximizing accuracy misses a crucial point: there is a limit to how accurate AI needs to be in order to meaningfully speed up contracting.
Studies shouldn’t just ask “How accurate is AI?” – they should ask “How accurate does AI need to be to create real efficiency gains?”
For example:
- If an AI tool is 92% accurate and cuts contract review time in half, is that good enough?
- What’s the tipping point where AI’s errors create more work than they save?
- Can AI that’s 88% accurate still be valuable if it speeds up low-risk contract reviews, even if it requires human oversight on complex deals?
Instead of chasing an elusive 100% accuracy benchmark, researchers should focus on identifying the threshold of useful accuracy – the point where AI delivers the most efficiency without creating excessive rework.
Because the truth is, AI doesn’t have to be perfect. It just has to be good enough to make contracting faster.
What Should We Be Measuring Instead?
Right now, studies on AI in contract review tend to focus on:
- How often AI correctly identifies contract clauses
- Whether AI makes fewer mistakes than human reviewers
- How well AI aligns with human decision-making
These are important factors, but they don’t tell the full story. If we really want to measure AI’s value, we should be asking:
- How much time does AI save per contract?
Does it cut contract review from 4 days to 4 hours? From 90 minutes to 9 minutes?
- How much faster can deals close with AI?
If an AI-driven contract review system shaves days off the sales cycle, what does that mean for revenue acceleration?
- How does AI’s speed impact legal workload?
Are lawyers spending less time reviewing low-risk agreements? Are they able to shift their focus to higher-value tasks?
- Does AI reduce contract turnaround time for procurement and vendor agreements?
Are businesses able to onboard suppliers and partners faster, reducing operational delays?
And most importantly:
- What is the optimal level of AI accuracy for maximum efficiency?
At what point does increased accuracy stop adding real value?
It’s Time to Rethink AI’s Value in Contracts
AI isn’t just about matching human accuracy – it’s about doing things faster. Legal teams and researchers need to shift their focus beyond just whether AI gets clauses right and start measuring how much time AI is saving across the entire contracting process.
Because in business, speed isn’t just a nice to have. It’s a competitive advantage.
So why are we still measuring AI like lawyers instead of like business leaders?
What do you think?
If you enjoyed this discussion, Electra shares insights like this every two weeks in her Standards Spotlight newsletter. Sign up here to stay in the loop.
