16-17 June 2027 – London, InterContinental O2 | Magazine

LegalTech Diaries Volume 16

James Quinn

Co-Founder and CEO
Clarilis

LegalTech Diaries Volume 16

James Quinn

Co-Founder and CEO
Clarilis

Clarilis has taken a deliberately opinionated position on AI: that precedents crafted by experienced knowledge lawyers remain the gold standard, and that generative AI should handle only the remaining 10% that automation cannot reach. In a market where competitors are leading with AI-first messaging, how do you make the case to firm leaders that restraint is the smarter strategy?

 

We’re genuinely bullish on AI – there are real, valuable applications across the drafting workflow, particularly for blank-page problems, and we’re continually building new capability into Clarilis AI Draft.

But enthusiasm has to be grounded in ROI, and when you do the honest accounting on drafting efficiency, the numbers consistently favour starting from a trusted, expertly-crafted precedent paired with a deterministic approach to drafting. The comparison that matters isn’t “AI output vs. a blank page” – it’s “AI output including all downstream review, supervision, and error-correction time” vs. “a logic-driven automation built on solid precedent.” When you run that calculation, the deterministic, precedent-first approach wins. Every time a lawyer has to interrogate an AI-generated clause they didn’t author, you’re paying a tax. That tax compounds across a large practice.
So the question we’d put to firm leaders isn’t “are you pro-AI or anti-AI?” It’s “where in the workflow does AI actually move the needle on ROI, and where does it just move the risk around?”

 

One of Clarilis’s defining features is its managed service: your team of lawyers designs, implements and maintains automations on behalf of the client, rather than handing over a platform and walking away. The industry is littered with failed automation projects that became shelfware. What have you learned about why adoption fails, and what does the managed service model solve that self-serve cannot?

Automation projects most often fail because firms underestimate the operational effort required, both in implementation and after launch – in particular the lawyer time required to produce substantial automation. Document automation isn’t a one-off implementation – it has to evolve with changing precedents and market positions. If an automation becomes outdated, lawyer confidence drops and usage tumbles.
Maintenance is particularly hard in self-serve models because firms often depend on a small group of motivated individuals working on the automation alongside other work. Over time, priorities shift or key people move on; which is how good projects become shelfware.

The managed service model is designed to solve this problem. At Clarilis, maintaining and improving automations is our team’s core job, not something managed around other responsibilities. That gives firms continuity, specialist expertise and confidence that the automation will stay aligned with current practices – which is ultimately what drives long-term adoption.

You’ve written about the shift from “can AI do this?” to “how do we operationalise this safely and at scale?” as the defining question for 2026. For a managing partner reading this who has run a few AI pilots but is struggling to move from experiment to enterprise deployment, what is the single biggest obstacle you see, and how should they think about overcoming it?

To commit meaningfully to enterprise-wide AI deployment, you need a coherent vision for how AI fits the firm’s business model across its different practices. That’s a harder conversation than most pilots are designed to answer. A pilot tells you whether a tool works in a contained environment; it rarely tells you how AI changes your pricing, your staffing model, or your competitive positioning in Corporate versus Employment versus Real Estate. Managing partners are being asked to make enterprise-scale commitments on the basis of evidence that was never designed to support them.

A more pragmatic path is to scale AI capability through existing, proven vendors rather than trying to build that strategic vision top-down before they’ve earned the confidence to do so. This is faster, lower-risk, and lets the evidence accumulate in a way that can actually inform bigger decisions later.

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