Before AI Can Act, It Has to Understand: Building AI That Actually Works

Where to Start

In a recent episode of the Law Next podcast, Knowable CEO Nik Reed put it plainly: While AI tools can produce fast, fluent answers, those answers are only as reliable as the data beneath them. And in the legal world where accuracy isn’t a luxury, this becomes the line between a system that legal professionals can rely on and one that loses their trust immediately.

Legal understanding goes beyond surface-level text. Contracts have an operative nature that is essential for action. Determining which agreements govern, how they relate, and which one is active is not something AI can do out-of-the-box. It requires data that is complete, verifiably accurate, and structured.

At Knowable, we’ve spent over a decade turning contract chaos into clarity. Our expertise, combined with a patented family mapping structure, powers contract intelligence and grounds our expert prompt engineering in high-quality data. Here are five lessons from our experience that contracts professionals should keep in mind.

1. You Don’t Have a Legal AI Problem, You Have a Contract Chaos Problem

Before AI can do anything meaningful, you need a contract system of record. That means all executed agreements must be gathered, deduped, and structured into a single, searchable environment.

Too often, contracts are scattered across inboxes, shared drives, and overlapping CLMs, riddled with duplicates and partial versions. Simply dropping generative AI on top of that chaos creates a garbage in, garbage out scenario. At Knowable, we work with legal departments to create a verified, unified foundation layer: every final contract in one place, with human-reviewed metadata that makes querying and reasoning both possible and practical.

What you need isn’t a document repository, it’s an operational base layer that supports every day search, downstream analytics, and AI interaction.

2. Contract Families Are The Structure That Makes Legal Reasoning Possible

One of the most overlooked truths in executed agreement management is that agreements don’t stand alone. MSAs, amendments, addenda, and SOWs are just pieces of contract families, and those relationships fundamentally define what terms are in effect.

Generic AI systems don’t understand this. At Knowable, we’ve spent years designing for it. Our platform is built around the complex logic of contract families that allow AI to focus its answers. When you ask, “What’s the current limitation of liability?”, the system doesn’t guess. It pulls directly from high quality metadata outlining which agreements apply and how they interact.

This depth is the result of years of collaboration between technologists and contract experts who have built, refined and patented our platform design around post-signature realities.

3. The AI Is Trained But It Has Much To Learn

AI models today aren’t easily re-trained. But they can be prompted, structured, and guided to behave intelligently in specific domains. At Knowable, our prompting is shaped by data scientists working hand-in-hand with legal professionals who’ve spent over a decade developing contract data models and clause taxonomies.

This collaborative muscle, developed long before the generative AI boom, is what lets us infuse models with the logic of legal documents. It’s what allows our system to navigate the family mapping laid out before it. AI needs specialized prompting to know which data pathways to follow before reporting back that the “termination for convenience” in Amendment 3 actually overrides what was stated in the base MSA. Voila, an answer you can use.

4. Trust Comes from Verification

One of the most dangerous things about GenAI is its confidence. It can sound right even when it’s not. In legal work, that’s a risk no team can afford.

At Knowable, we don’t ask users to trust AI blindly. Every answer from our system is paired with source-linked visibility. You see the original contract text, the related metadata, and the contract family context in the same interface. You can verify immediately without digging or tab switching.

Even the best AI can be wrong. Safety comes from knowing exactly what it was looking at when it made its call, ease of fact checking, and the knowledge that each data point was reviewed by experts, not scraped from the web.

5. AI Doesn’t Understand That “Agency” Is a Legal Duty But Lawyers Still Have To

In legal terms, agency isn’t just a metaphor. It’s a binding relationship grounded in consent, intent, and fiduciary duty. When a lawyer acts as an agent, they are legally empowered and ethically obligated to act on the principal’s behalf.

So-called “agentic AI” simulates intent but lacks personhood, legal capacity, and duty. These systems are not legal agents, even if they appear helpful or decisive. The danger is not in the technology itself, but in the risk that users misunderstand its role.

That’s why Knowable doesn’t position AI to replace lawyers or fully automate legal workflows. Our tools act alongside humans, within guardrails, and grounded in clean, verifiable data. The lawyer remains the responsible party. Even the most advanced AI must support, never substitute, the true agent.

Final Thought

If you want AI that can take action in a legal workflow, start by asking: does it actually understand the contracts it’s working with? That understanding doesn’t begin with the AI model. It begins with the data infrastructure behind it.

Knowable was built to tackle the contract chaos problem that years of CLM repositories have failed to solve. That means starting from real-world order and organization and building a system that delivers clarity, structure, and trust with the most skeptical user-base imaginable: Lawyers.

Before AI can act, it has to understand. We’ve spent the last ten year preparing to teach it how.

For even more, click to watch another of Nik’s interviews on this subject with Artificial Lawyer!