The next phase of contract technology is not faster markup. It is whether legal can turn signed agreements into usable knowledge about risk, revenue, obligations and commercial position.
Companies spend enormous time negotiating contracts, then routinely lose track of what those contracts actually say.
That is the operating problem hiding underneath the contract AI boom. The redline gets the attention because lawyers feel that pain every day. It is slow, repetitive and expensive. But the redline is only one visible moment in a much larger lifecycle. The more important question is what the company knows after the agreement is signed.
For a general counsel, that is the higher-value test. Can the legal department explain the company’s commitments, risks, exceptions, obligations and commercial positions at the speed the business needs? Or does it still have to send someone into the repository to search PDFs, compare provisions and reconstruct the answer by hand?
Akshay Verma’s useful point is that contracting should not be reduced to markup. Verma, who joined SpotDraft after legal operations roles at companies including Facebook and Coinbase, sees the contract not as a finished document but as a living business record. It holds pricing decisions, risk positions, compliance obligations, revenue terms, negotiation history and institutional memory.
The business does not experience contracts as documents. It experiences them as margin, obligation, delay, leverage, exposure and cash.
That is where many legal departments still leak value. A signed contract may sit safely in a repository, but “stored” is not the same as “usable.” When the business needs to know what was promised, which customers have nonstandard terms, where pricing exceptions live, what obligations are coming due or how risk was allocated across a portfolio, legal often has to reconstruct the answer manually.
The information exists. It just is not available in a form the business can use.
At Coinbase, Verma says institutional customer agreements contained significant business intelligence: pricing, risk, compliance and commercial terms that mattered well beyond the legal department. But getting answers out of those agreements often required contract managers to dig through documents by hand. That is not just inefficient. It is a management failure hiding in plain sight.
This is the part of the CLM conversation that deserves more attention.
A redlining tool may help lawyers move faster inside one document. A repository may help the company store what it signed. Neither, by itself, answers the harder question: Can legal turn the company’s agreements into operational knowledge?
That question should reshape how legal leaders evaluate contract technology.
Can the system show which agreements contain unusual indemnity language? Can it identify customers with pricing exceptions? Can it explain where the company accepted terms it usually rejects? Can it track obligations against renewal dates? Can it surface provisions that create risk in an acquisition? Can it show which clauses are slowing deals? Can it distinguish a real market position from a legacy habit no one has challenged?
Those are not product-demo questions. They are management questions.
They affect money, time, risk, revenue and compliance. They also determine whether legal is seen as a business partner or as the department that knows the answer exists somewhere but cannot produce it fast enough.
That is the real reader value in Verma’s argument. The current AI conversation rewards speed: faster review, faster drafting, faster answers. But speed without memory is not transformation. If a legal department accelerates contract review but still cannot tell the business what is inside its contracts, it has improved the front end while leaving the operating problem intact.
Legal departments have spent years trying to prove they are more than cost centers and risk blockers. Contract data gives them a practical way to do it. If legal can show the business what has been agreed, where risk is concentrated, how negotiation positions are changing and where commercial terms are creating friction, it becomes more than a reviewer of deals. It becomes a source of enterprise intelligence.
That requires legal to think differently about its own work.
A contract is not done when it is signed. It begins producing value or risk after signature. The language negotiated by legal becomes the operating reality for sales, finance, compliance, procurement and the executive team. If that language cannot be found, compared, measured or explained later, then legal has preserved the document but lost the intelligence.
That is the useful lens on SpotDraft. The company is competing in a crowded CLM market, and it still has to prove whether its platform can deliver on this broader operating promise. But the frame Verma offers is useful regardless of vendor: Legal leaders should stop asking only how quickly a system can review a contract and start asking what the business can learn from the contracts it already has.
The practical test is simple. Do not buy contract AI because the redline looks good. Buy it only if it helps legal answer better questions after the signature.
The next phase of contracting will not be defined by faster markup alone. It will be defined by whether legal can convert agreements into institutional memory.