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A few years ago, we asked a question that most lawyers recognized instantly: why was every company drafting its own NDA from scratch? That question led to the creation of oneNDA. It wasn’t the document itself that mattered most, but the shift it represented. Legal teams were not opposed to standardization; they simply lacked a shared, credible framework to achieve it together.
We are now facing a similar moment, only the landscape has changed. AI has transformed the mechanics of drafting and reviewing contracts. It can summarize, extract, compare, and redline language at extraordinary speed. The challenge is no longer whether automation can improve efficiency. It can. The challenge is how to ensure that efficiency does not erode judgment.
The value of contract review
The value of contract review has never been about producing text quickly. It has always been about understanding what that text means for the business. Whether a limitation of liability is commercially reasonable, whether a data clause introduces unacceptable exposure, or whether a seemingly harmless indemnity creates obligations that cannot be met. AI does not inherently understand these distinctions. It can recognize patterns in language, but it does not know what is fair, defensible, or appropriate in a particular commercial context. Without guidance, it can produce results that look polished, but are disconnected from the organization’s actual risk position.
This is where standards play a different role than many people assume. When lawyers talk about standards, the word often conjures ideas of rigidity or bureaucracy. In practice, they serve a much more important function – particularly in a world of AI. Standards create a shared point of reference. They make decisions traceable and explainable. They ensure that a company negotiates on the basis of consistent principles, even when individual reviewers or tools change. They act as the governance layer that gives AI something to align with, rather than leaving it to interpret risk on its own.
The evolution of oneNDA
The evolution from oneNDA to SimpleAI Standards follows this logic. Instead of treating standards as documents to consult, they are being embedded directly into the systems where negotiation happens. When the logic of a playbook sits inside the tooling, it stops being a theoretical reference and starts becoming part of the workflow. Two lawyers in different parts of the world can work from the same agreement and reach conclusions that are consistent, not because they share a document, but because the reasoning is built into the process itself.
The most important element of this shift is the use of principle-based playbooks. A purely prescriptive playbook can tell you exactly how to amend a clause when the language is familiar, but it struggles when reality deviates from the template. A principle-based playbook sets out what the clause is designed to protect and how to evaluate alternatives. It captures the reasoning rather than the exact words. This allows automation to operate with context, not just instructions, and it helps lawyers and business users understand why a change is being made rather than simply applying it mechanically.
Across the industry, legal teams experimenting with AI are realizing that the real question is not what these tools can do, but how to ensure they do it responsibly. There is a noticeable shift between tools that focus primarily on speed, and those that prioritize defensibility and consistency. The organizations taking AI seriously over the long term are the ones investing in standards, governance, and explanation, rather than simply volume of output.
This approach does not replace human judgment. It extends it. Standards translate experience, policy, and commercial tolerance into something that can be applied at scale. They allow automation to operate with boundaries. And they ensure that when AI produces work, it reflects the company’s values and risk appetite, not just generic legal language.
Human governance remains essential. AI can follow rules, but it does not set them. Standards represent the mechanism through which legal and commercial teams define those rules clearly enough for others – humans or machines – to apply them accurately. When standards sit inside the tools themselves, the result is not a loss of control, but a stronger form of it.
The future of contracts
The future of legal work will not be defined by how quickly software can redline a contract. The real progress lies in making sure that decisions are consistent, explainable, and aligned with the business. The tools will continue to improve at a rapid pace. The question for legal teams is whether their principles and standards are evolving alongside them.
That is where this next phase of work is heading: standards not as static documents, but as active systems that shape how drafting and negotiation actually happen. It’s a shift from automation for efficiency to automation anchored in judgment. And it is likely to define which organizations truly benefit from AI in contracting – and which simply generate faster versions of the same inconsistencies they already had.
The strategic importance of Standard + AI
Companies that take this seriously will gain a structural advantage. Standards allow legal teams to scale without adding equal layers of headcount, because the reasoning behind decisions becomes part of the workflow rather than something only a few people understand or remember. It means that a business user, a junior lawyer, and an AI system can all apply the same principles, reach similar conclusions, and explain how they got there. That consistency is what unlocks speed without exposing the business to hidden risk.
There is also a larger implication. If standards become the shared layer across tools, contracting stops being a series of disconnected documents and starts becoming an interoperable system. It becomes possible for companies, counterparties, and even software platforms to communicate using common logic rather than reinventing decisions from scratch. The future of legal tech is not dozens of isolated tools automating in isolation, but a connected ecosystem guided by principles we agree on collectively.
This is the direction the industry is moving, whether slowly or quickly. AI will continue to evolve, and tools will continue to get faster. The differentiator will be the organizations whose systems are anchored in standards that make the work reliable, explainable, and defensible. The technology will handle the mechanics. Standards will determine whether the outcomes are worth relying on.
Want to see how standards and playbooks work inside SimpleAI? Book a demo and explore the integration →
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