Why Enterprises Are Building Their Own AI And Why It's Just Getting Started

The $500M signal

When Kirkland & Ellis - one of the world’s most profitable law firms - announced it was committing $500 million to build its own proprietary AI system, the internet had opinions. Some called it overreach. Others called it visionary.

But the real reason behind the move is far simpler, and far more instructive for every enterprise leader watching from the sidelines.

The table stakes problem

When every major law firm is using the same AI tools - Harvey, Legora, Anthropic Legal - those tools stop being a competitive advantage. They become the floor, not the ceiling.

You’re no longer ahead of the competition. You’re simply not falling behind.

This is the table stakes problem. When a technology becomes universally adopted across an industry, it ceases to differentiate. It becomes infrastructure. And infrastructure, by definition, is something everyone has.

Kirkland & Ellis recognized this early. With roughly 250 lawyers - including 100 partners - involved in shaping the platform, and a three-to-four year build timeline, this isn’t a reactive decision. It’s a strategic one that has been in the works for years.

The proprietary moat

The deeper motivation is about institutional knowledge - and who gets to own it.

Law firms like Kirkland & Ellis are built on the expertise of their senior partners. Those partners command $2,000 per hour or more because of what they know: how to structure a deal, how to anticipate a regulator’s concern, how to navigate a complex cross-border transaction.

That knowledge is hard-won, deeply contextual, and extraordinarily valuable.

When a law firm uses a third-party AI vendor, every interaction - every query, every document, every workflow - potentially trains that vendor’s model. The firm’s most valuable intellectual capital flows outward, enriching a platform that serves competitors just as readily.

Building proprietary AI means keeping that knowledge in-house. It means building AI that institutionalizes what makes the firm great - and ensuring that no vendor ever learns what makes their senior partners worth what they’re worth.

This isn’t just a legal industry story

The Kirkland & Ellis move is a preview of what’s coming across industries.

In the years ahead, we’re going to see more enterprises reach the same conclusion: relying entirely on shared, third-party AI platforms creates a ceiling on differentiation.

The firms and organizations that invest in proprietary AI - trained on their own data, shaped by their own workflows, and governed by their own policies - will be the ones that build lasting competitive moats.

This is especially true in industries where institutional knowledge is the product: consulting, finance, healthcare, and yes, legal. In these sectors, the data that flows through AI systems isn’t just operational. It’s strategic.

Protecting it isn’t just a privacy concern. It’s a business imperative.

What this means for AI strategy

For enterprise leaders, the Kirkland & Ellis announcement is a signal worth paying attention to. It raises a set of questions that every organization should be asking:

• What data are we feeding into third-party AI systems, and who benefits from it?
• Are the AI tools we use today creating competitive differentiation, or just keeping us at parity?
• What would it look like to build AI that reflects our unique processes, expertise, and institutional knowledge?

The answers won’t be the same for every organization. Not every company has the resources or the need to invest $500 million in a proprietary AI build.

But the underlying logic applies broadly:

AI trained on your data, for your workflows, is more valuable than AI trained on everyone’s data for everyone’s workflows.

The shift is already underway

Kirkland & Ellis isn’t alone. Across industries, forward-thinking organizations are beginning to move beyond off-the-shelf AI and toward purpose-built systems that reflect their specific needs, data, and competitive positioning.

The question isn’t whether this shift will happen. It’s whether your organization will be ahead of it - or catching up.