One of the things I’ve noticed over the years is that every major technology shift eventually develops its own recognizable contract.
It doesn’t happen overnight. In the early days, every agreement feels different because vendors are still figuring out how to describe the technology and customers are still learning what questions to ask. The contracts look highly customized, negotiations take longer, and every deal feels like uncharted territory.
Eventually, though, something changes.
The market starts asking the same questions over and over again. Lawyers begin negotiating the same issues in nearly every transaction. What once seemed unique gradually becomes familiar, and before long a recognizable contractual framework begins to emerge.
We saw it happen with cloud computing. We saw it happen with SaaS. We saw it happen with open source software.
I think we’re beginning to see the same thing happen with artificial intelligence.
The Language Is Different, But The Questions Are The Same
At first glance, enterprise AI agreements still appear to be all over the map.
Every major vendor has its own terminology, its own structure, and its own way of describing AI functionality. If you compare contracts side by side, they rarely look identical, which makes it easy to conclude that the market is still too immature for any real standardization.
But after reviewing enough of these agreements, I’ve started to notice something else.
The wording changes from contract to contract, but the underlying questions are remarkably consistent.
Customers want to know whether their data will be used to train future models. They ask who owns prompts, inputs, and AI-generated outputs. They want assurances about confidentiality, security, intellectual property protection, transparency, and human oversight. They ask how vendors address hallucinations, inaccurate outputs, model updates, and regulatory changes. They also want to understand where responsibility shifts from the vendor to the customer when AI is put into production.
Those conversations are happening across the market, regardless of which AI platform is involved.
That tells me something important.
The Market Is Beginning To Build A Framework
I don’t think we’re moving toward a standard form agreement. Technology companies will always differentiate themselves through their contracts, and legal teams will continue negotiating language that reflects their own risk tolerance and business models.
What I do think is emerging is something more subtle.
The architecture is becoming familiar.
Most enterprise AI agreements now revolve around the same core subjects. They address data use, model training restrictions, ownership of AI-generated content, confidentiality, acceptable use, security, transparency, governance, audit rights, intellectual property, and liability. Individual clauses may differ, but the overall structure increasingly feels recognizable.
That is often how markets mature.
Long before contract language becomes standardized, the issues themselves become standardized. Lawyers know where to look because they already know the questions that need to be answered.
Contracts Tell Us Where the Market Is Going
One reason I find this interesting is that contracts often reveal where a market is headed before legislation does.
Every negotiated agreement represents a practical attempt to solve real business problems. Customers are asking for commitments they believe they need. Vendors are deciding which promises they are willing to make. Over time, those negotiations begin shaping expectations across the industry.
That’s exactly what happened with cloud computing.
Eventually, cloud agreements settled into a familiar rhythm. Customers expected uptime commitments, service level agreements, disaster recovery provisions, data security obligations, and clear allocation of responsibility. Those concepts became so common that they now feel like ordinary commercial terms.
AI appears to be following the same path.
The specific language will continue evolving, particularly as regulators introduce new requirements and the technology itself changes. But the overall framework is becoming easier to recognize with every negotiation.
Why This Matters For Lawyers
For legal departments, this changes the nature of AI contract review.
A few years ago, reviewing an AI agreement often felt like exploring completely new territory. Every deal raised unfamiliar questions, and there were very few market norms to rely upon. Today, many of those questions have become recurring themes, even if the answers remain heavily negotiated.
That doesn’t make the lawyer’s job easier.
If anything, it raises the stakes. Legal teams are no longer reviewing isolated clauses. They’re evaluating an interconnected governance framework that affects privacy, security, intellectual property, procurement, compliance, and the day-to-day operation of AI inside the business.
I’ve also noticed that these conversations are becoming much more collaborative. Product teams, security professionals, procurement, privacy counsel, compliance, and legal all have legitimate interests in the same contractual provisions because those provisions increasingly determine how AI can actually be deployed after the agreement is signed.
In many ways, the contract has become the blueprint for enterprise AI governance.
A Sign That The Market Is Growing Up
We often measure the maturity of a technology by looking at the products themselves. Are the models getting better? Are customers adopting them? Are businesses finding meaningful use cases?
Those are important indicators.
But I think there’s another measure that’s easier to overlook.
Markets mature when their contracts become recognizable.
That doesn’t mean every agreement looks the same. It means experienced lawyers begin to recognize the architecture because the same issues appear in deal after deal. The negotiations become more sophisticated, expectations become clearer, and both vendors and customers develop a shared understanding of what responsible contracting looks like.
Artificial intelligence seems to be entering that stage now.
The headlines still focus on the latest model, the latest funding round, or the latest regulatory proposal. Meanwhile, commercial lawyers are quietly building something just as important. Through thousands of negotiations taking place every day, they’re creating the contractual framework that will shape how enterprise AI is bought, sold, and governed for years to come.
That may not generate the same headlines as the latest AI breakthrough.
But it is one of the clearest signs that the market is beginning to grow up.
Olga V. Mack is the CEO of TermScout, where she builds legal systems that make contracts faster to understand, easier to operate, and more trustworthy in real business conditions. Her work focuses on how legal rules allocate power, manage risk, and shape decisions under uncertainty. A serial CEO and former General Counsel, Olga previously led a legal technology company through acquisition by LexisNexis. She teaches at Berkeley Law and is a Fellow at CodeX, the Stanford Center for Legal Informatics. She has authored several books on legal innovation and technology, delivered six TEDx talks, and her insights regularly appear in Forbes, Bloomberg Law, VentureBeat, TechCrunch, and Above the Law. Her work treats law as essential infrastructure, designed for how organizations actually operate.
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