Right here’s what we all know: 80% of legal teams are using generative AI in accordance with ILTA’s 2025 Expertise Survey. That’s spectacular adoption for a expertise that hardly existed two years in the past. However now, as we enter the period of agentic AI, authorized groups are being requested to rethink all the things once more.
The query isn’t whether or not agentic AI will change authorized work. It’s whether or not companies will change how they undertake expertise. Profitable adoption requires each well-designed expertise and strong people-centered methods. You may’t expertise your manner out of behavior formation challenges, and you may’t adoption-strategy your manner out of poorly designed instruments. Most organizations are investing closely in a single whereas underinvesting within the different.
Why Habits-Not Expertise-Decide Adoption
I’ve spent my profession finding out how folks undertake new methods of working, and I’ve discovered that expertise transformations fail once we deal with them as expertise issues. The authorized trade is about to make that mistake once more with agentic AI, investing in refined orchestration platforms whereas ignoring the fundamental psychology of behavior formation. We’re fixing for functionality when the true bottleneck is adoption, and most AI adoption methods don’t plan for abandonment.
Forming a brand new behavior or manner of working takes time and repetition. Behavioral science tells us most people fail when attempting to begin a brand new behavior, not as a result of they lack functionality or dedication, however as a result of habits require sustained apply earlier than they turn out to be routine. And when folks stumble, which they’ll, they want structured help to restart.
Research from Prosci exhibits that tasks with wonderful change administration are seven instances extra prone to succeed—proof that the folks aspect isn’t optionally available. However most companies roll out AI instruments with a pilot group, a coaching session, a Slack channel, and the most effective of intentions. Then six months later, they’re puzzled when utilization metrics flatline. The expertise didn’t fail. The adoption design did.
Designing for Adoption: Anticipate the Dip, Construct the Restart
If you happen to’re severe about adoption, right here’s what you must construct into your technique—not after instruments fail, however from day one:
Anticipate the dip: Utilization sometimes drops 30-40% after the preliminary pleasure. Construct that into your timeline and talk it upfront so groups don’t interpret the dip as failure.
Create restart rituals: Month-to-month “workplace hours” the place somebody coaches attorneys by way of their precise work utilizing the instrument. Not generic demos, real-time problem-solving with their paperwork, their purchasers, their workflow friction factors.
Showcase wins: Set up a daily forum-lunch-and-learns, showcase periods, or a win-room channel—the place early adopters share what they’re conducting with the instrument. Not generic success tales, however particular: “Right here’s how I used it to catch a essential disclosure error” or “Right here’s the way it saved me 3 hours on this negotiation.” Make seen progress contagious. Folks undertake quicker after they see friends fixing actual issues.
Normalize stopping and beginning: Ship a targeted message three months in: “If you happen to aren’t nonetheless utilizing a brand new instrument to your benefit, right here’s the best way to restart.” Give permission to be inefficient to permit folks to relearn.
Monitor abandonment as successful metric: If you happen to’re not measuring who stops utilizing instruments and why, you’re not severe about adoption. The restart information is extra worthwhile than the preliminary adoption information.
These restart methods are essential, however they work greatest when embedded in a broader readiness method.
Strategic Readiness for Authorized Leaders
To arrange for the agentic period, authorized leaders ought to give attention to readiness, not hype. Right here’s what that truly means:
Begin with the issue and your skeptics. Earlier than evaluating any instrument, establish the precise drawback you’re fixing, and contain your skeptics in defining it. These are the revered practitioners who gained’t undertake till they see actual worth. After they assist establish the issue, they’re invested find an answer. Adoption fails when it’s finished to folks somewhat than with them. Your skeptics will ask the onerous questions that forestall costly failures later.
Title what’s being misplaced, not simply gained. Folks resist change after they can’t articulate what they’re giving up. Be specific: “Sure, this modifications how you’re employed. You’ll spend much less time trying to find precedents and extra time making use of judgment to complicated negotiations. Meaning studying new workflows throughout your busiest quarter. Right here’s how we’re supporting that.”
Create psychological security for the training curve. Agentic AI isn’t all the time intuitive. Groups want specific permission to be inefficient whereas they be taught, or they’ll abandon instruments on the first frustration. Construct “protected apply time” into billable hour expectations for the primary 90 days.
Select the correct workflows and repair damaged processes first. Goal high-impact areas the place complexity meets quantity—however solely the place groups have capability to be taught. Don’t pilot AI in your most time-pressured course of. And in case your information is inconsistent or your programs don’t speak to one another, pause the AI dialog totally. Agentic programs amplify good processes and expose damaged ones, they don’t repair them.
Outline success metrics past time saved. Monitor error discount, negotiation velocity, surfaced dangers, and abandonment/restart charges. The adoption journey issues as a lot because the effectivity beneficial properties.
Set up governance frameworks with auditability, traceability, and clear human-in-the-loop controls. This isn’t crimson tape; it’s the muse that enables groups to experiment safely.
The Path Ahead
The way forward for authorized work gained’t be outlined by who adopts AI first, however by who adopts it correctly. And knowledge, on this case, means understanding that expertise transformation is essentially a human transformation, one which requires endurance, help, and deliberate restarts when folks inevitably stumble.
The query isn’t whether or not agentic AI will change authorized work. It’s whether or not your agency will change the way it adopts expertise.
Able to see expertise designed with adoption in thoughts? Learn more about Litera One and Lito and Lito or Schedule a demo at this time.
