Right here’s what we all know: 80% of authorized groups are utilizing generative AI in response to ILTA’s 2025 Know-how Survey. That’s spectacular adoption for a know-how 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 know-how. Profitable adoption requires each well-designed know-how and strong people-centered methods. You may’t know-how your method out of behavior formation challenges, and you may’t adoption-strategy your method out of poorly designed instruments. Most organizations are investing closely in a single whereas underinvesting within the different.
Why Habits-Not Know-how-Decide Adoption
I’ve spent my profession finding out how individuals undertake new methods of working, and I’ve realized that know-how transformations fail once we deal with them as know-how issues. The authorized business is about to make that mistake once more with agentic AI, investing in subtle orchestration platforms whereas ignoring the fundamental psychology of behavior formation. We’re fixing for functionality when the actual bottleneck is adoption, and most AI adoption methods don’t plan for abandonment.
Forming a brand new behavior or method of working takes time and repetition. Behavioral science tells us most people fail when attempting to start out 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 into routine. And when individuals stumble, which they’ll, they want structured help to restart.
Analysis from Prosci exhibits that tasks with wonderful change administration are seven instances extra more likely to succeed—proof that the individuals facet isn’t non-obligatory. However most companies roll out AI instruments with a pilot group, a coaching session, a Slack channel, and one of the best of intentions. Then six months later, they’re puzzled when utilization metrics flatline. The know-how didn’t fail. The adoption design did.
Designing for Adoption: Count on the Dip, Construct the Restart
In the event you’re severe about adoption, right here’s what it’s good to construct into your technique—not after instruments fail, however from day one:
Count on the dip: Utilization usually 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 legal professionals by means of their precise work utilizing the software. Not generic demos, real-time problem-solving with their paperwork, their shoppers, their workflow friction factors.
Showcase wins: Set up an everyday forum-lunch-and-learns, showcase classes, or a win-room channel—the place early adopters share what they’re conducting with the software. Not generic success tales, however particular: “Right here’s how I used it to catch a crucial disclosure error” or “Right here’s the way it saved me 3 hours on this negotiation.” Make seen progress contagious. Folks undertake sooner after they see friends fixing actual issues.
Normalize stopping and beginning: Ship a targeted message three months in: “In the event you aren’t nonetheless utilizing a brand new software to your benefit, right here’s the right way to restart.” Give permission to be inefficient to permit individuals to relearn.
Monitor abandonment as a hit metric: In the event you’re not measuring who stops utilizing instruments and why, you’re not severe about adoption. The restart knowledge is extra invaluable than the preliminary adoption knowledge.
These restart methods are crucial, however they work greatest when embedded in a broader readiness strategy.
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 really means:
Begin with the issue and your skeptics. Earlier than evaluating any software, establish the particular downside you’re fixing, and contain your skeptics in defining it. These are the revered practitioners who received’t undertake till they see actual worth. After they assist establish the issue, they’re invested to find an answer. Adoption fails when it’s performed to individuals somewhat than with them. Your skeptics will ask the onerous questions that stop 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 express: “Sure, this adjustments 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 at all times intuitive. Groups want express permission to be inefficient whereas they study, 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 study. Don’t pilot AI in your most time-pressured course of. And in case your knowledge is inconsistent or your programs don’t discuss 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 features.
Set up governance frameworks with auditability, traceability, and clear human-in-the-loop controls. This isn’t crimson tape; it’s the inspiration that enables groups to experiment safely.
The Path Ahead
The way forward for authorized work received’t be outlined by who adopts AI first, however by who adopts it properly. And knowledge, on this case, means understanding that know-how transformation is essentially a human transformation, one which requires persistence, help, and deliberate restarts when individuals 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 know-how.
Able to see know-how designed with adoption in thoughts? Study extra about Litera One and Lito and Lito or Schedule a demo as we speak.
