AI is challenging the billable hour. That much is widely agreed upon. But the common prediction — AI makes legal work faster, lawyers bill fewer hours, the billable hour collapses — misses the more consequential story underneath.
Dans une chronique publiée dans Associé gérant d'aujourd'hui, PDG et fondateur d'iDS Dan Regard traces the billable hour back to its origins, connects it directly to the rise of discovery, and argues that AI may do something far more significant than simply reducing hours. It may restore predictability to litigation — and with it, the conditions for a fundamentally different model of legal billing.
Why the Billable Hour Emerged
Regard’s argument starts with history. The billable hour didn’t arise from greed or convenience. It emerged as a rational response to a specific problem: when discovery expanded after the Federal Rules of Civil Procedure were introduced in 1938, the amount of work required to resolve a legal matter became increasingly hard to predict. Fixed fees and retainers — the dominant model before — couldn’t absorb that uncertainty. Hourly billing could. It shifted the risk of an unpredictable process from the lawyer to the client.
As Regard puts it, the client wanted resolution. The system rewarded effort. That misalignment has defined the economics of legal services ever since.
From Evidentiary Scarcity to Evidentiary Abundance
The insight at the heart of Regard’s argument is a historical shift that has unfolded largely without fanfare. For most of legal history, the challenge was finding enough evidence to understand a dispute. Today, the challenge is the opposite — the digitisation of nearly everything has created a world of evidentiary abundance.
In the early years of eDiscovery, that abundance made things worse. Volume drove costs up. But the tide, Regard argues, is turning. AI-enabled tools can now help lawyers and experts analyse large collections of data faster than traditional linear workflows — provided the process is designed, tested, and supervised. The key phrase is that last one. Supervised, purposeful AI use is what distinguishes a genuine efficiency gain from a liability.
The Real Promise
Here is where Regard’s argument arrives at its most compelling point. His prediction is not that AI will diminish legal acumen. It is that AI will improve access to the underlying evidence to which legal judgment is applied.
If lawyers and experts can find, understand, and test evidence earlier in a matter, litigation becomes more predictable. And predictability is precisely what makes value-based billing possible in the first place.
The billable hour became necessary because discovery made litigation unpredictable. If AI reduces factual uncertainty earlier in the process, it creates the conditions for legal work to be priced on value rather than effort — and moves the profession closer to what Rule 1 of the FRCP has always promised: the just, speedy, and inexpensive determination of every action and proceeding.
That is not a modest aspiration. And it is not a distant one.
At iDS, the intersection of AI, evidence, and legal efficiency is where our Découverte électronique et divulgation, Données structurées et analyses, et Médecine légale numérique practices operate every day — helping legal teams find the facts faster, so the law can do its job.
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