The ACC and Everlaw released their latest GenAI survey in January, and the numbers stopped me.

Corporate legal AI adoption roughly doubled in a single year, from an estimated 23% to 52%.

But the number that really got me thinking: an estimated 64% of in-house teams now say they expect to depend less on outside counsel because of AI capabilities they're building internally.

That's not an analyst's prediction.

That's the stated intention of the people who send you work.

If those intentions translate into action, the impact on firm revenue could be significant.

Here's one example from the ACC/Everlaw survey.

During an acquisition, an in-house team needed to refresh a 50-state legal analysis.

The previous version, handled by outside counsel, cost roughly $150,000.

This time, the in-house team completed it internally using AI.

Faster, cheaper, and more aligned with the business.

One example doesn't make a trend, but we've seen this pattern play out before.

The translation industry went through a version of this when AI tools became good enough for routine work.

Agencies built on high-volume document translation saw that revenue dry up fast.

The ones that survived repositioned around complex, high-stakes work where accuracy and nuance still required human expertise.

The parallels to legal aren't perfect, but the shape of the disruption is familiar.

Thomson Reuters is forecasting that demand for legal services could potentially dip to negative territory by Q3 2026.

If in-house teams are building the capability to handle routine drafting, research, and compliance themselves, the implications are worth taking seriously.

We built a free tool for exactly this question.

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An estimated 59% of in-house teams don't know if their outside counsel uses AI on their matters, roughly 58% say pricing hasn't changed to reflect AI efficiencies, and nearly 60% report no noticeable savings.

That's a trust problem.

If clients find out you've been using AI to work faster but billing at the same rate, the conversation gets uncomfortable quickly.

Marketing agencies have already gone through a version of this, and the ones that leaned into transparency came out ahead.

A growing number have adopted an AI-first model.

They're openly telling clients that content production, social media, SEO, email campaigns, and design work that used to take weeks now gets done in a fraction of the time.

Some are delivering in roughly 15% of the time it used to take.

Instead of hiding that, they made it their differentiator.

They lowered prices, and the result was more clients, not fewer.

Their market expanded because businesses that previously couldn't afford agency work could now get in the door.

Lower margin per engagement, but significantly higher volume and a broader client base.

Whether or not clients are actively replacing you, this transparency gap is worth closing on its own terms.

As you probably already know, not all legal work is the same.

In-house teams can use AI for routine contracts, standard research, and compliance monitoring.

What they can't easily replicate: complex multi-jurisdictional M&A, high-stakes litigation strategy, novel regulatory interpretation, and cross-border compliance.

These require judgment, relationships, and the kind of pattern recognition that comes from decades of practice.

That's the moat.

The firms that explicitly position their value around this kind of work are the ones I think will keep their seat at the table.

Look, I don't run a law firm.

Never have.

And I know I'm probably overstepping my lane here and will likely annoy many people.

But looking at how other industries have adapted their pricing and transparency, I can't think of strong reasons why law firms won't eventually follow.

The marketing agencies that went transparent didn't see their brand suffer.

Even the premium ones found that openness about AI strengthened client trust.

Whether every flagship firm needs to rethink pricing is a bigger conversation for another edition.

But the point is: a pricing shift driven by AI efficiency doesn't have to be brand-damaging.

So here's the experiment I'd design.

Pick one type of routine work for one client.

Or better yet, pick a new client who came in through the website, not a referral.

State a clear hypothesis: "If we're upfront about using AI and reduce the cost by X%, the client will be equally or more satisfied."

Then measure what matters: does the work get done faster?

Is the client just as happy?

Can you communicate why the firm's involvement was still critical at certain points?

Does the data tell you whether to expand or pull back?

One client. One test. Real data.

I'll be running some of these with my consulting clients in the coming months.

If your biggest client built in-house AI for routine legal work, which area would concern you most?

A) Contract review and drafting
B) Legal research and memo writing
C) Compliance monitoring and reporting
D) We'd welcome it, it frees us for strategic work

[Reply with your letter choice] - I'll share the results in the next edition.

I don't know if 64% of in-house teams will actually reduce outside counsel spend this year.

The data says they intend to.

Intentions and reality don't always match.

But I'd rather be the firm that started testing now than the one that finds out too late.

The shift isn't about AI replacing lawyers.

It's about clients quietly building the capability to handle the work that used to be yours by default.

Hit reply and tell me: am I overreacting to this data, or does it match what you're seeing?

I genuinely want to know.

— Liam Barnes

We help law firms build AI-enabled workflows that strengthen client relationships and protect revenue.

Grab some time to chat

(if you don’t see a suitable time, just shoot me an email [email protected])

"How is your team currently handling AI for contract review?"

General-purpose AI and "still evaluating" lead the pack.

A third of respondents are using general-purpose AI for contract review, which tells you the demand for dedicated tools is there but the commitment isn't, exactly the kind of gap that makes in-house AI builds attractive.

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