In this blog, written by Terry Dohrmann, co-founder and CEO at Lexafide, we explore AI adoption for law firms and what it takes to move beyond isolated pilot programs and into firmwide implementation.

In Part 2 of this four-part series, Terry shares practical insights to help plaintiffs firms identify high-impact opportunities, build the right operational foundation, and scale AI in ways that support growth, operational efficiency, and client service.

What You’ll Learn

• Why successful AI adoption requires an operating strategy, not just new technology
• How to assess your firm’s readiness and identify the next best opportunity for implementation
• Which workflows offer the greatest potential for efficiency, scalability, and business impact
• How to expand AI use across the firm while preserving the human judgment and client experience that matter most

The Growing Gap Between AI Adoption and AI Strategy

Research from Thomson Reuters¹ shows that 41% of law firms are already using generative AI, while only 22% of surveyed organizations report having a visible, defined AI strategy.

The distinction matters because adopting a tool and generating measurable business outcomes are two very different challenges. As AI becomes more accessible, competitive advantage is shifting away from simply having the technology and toward integrating it effectively into firm operations. That gap between adoption and strategy is where firms create a lasting competitive advantage.

The Adoption Gap

Buying a technology and changing an outcome are two different projects, and most firms only budget for the first one. A firm signs up for an AI intake tool, runs it in one department, sees early improvements such as faster response times and fewer missed calls, and stops there. Nothing about the tool failed. What’s missing is everything that was supposed to happen after it worked: a second use case, a broader rollout, and a way to measure whether the gains are compounding or simply improving one team’s monthly report.

A successful pilot proves a tool can solve a problem. Firmwide adoption requires something more. It requires leadership ownership, a plan for expansion, and a framework for measuring results across the organization. Without that foundation, even strong early results often remain isolated to a single team or workflow rather than creating meaningful operational improvements across the firm.

Many firms are not held back by a lack of capital, demand, or available technology. More often, they struggle to move beyond familiar ways of operating. The technology changes, but the processes surrounding it do not.

That is the adoption gap: the space between proving a tool can work and building the strategy needed to scale its impact across the firm.

Know Your Stage Before You Pick Your Next Move

Not every firm reading this is in the same place, and the right next step depends on where you are today.

Some firms have not yet implemented AI in a meaningful way and are still determining where to start. Others have several tools in production and are looking to expand adoption or connect those investments more strategically. Neither position is inherently better, but firms often lose time when they misjudge their stage of development.

A simplified view of the adoption curve looks like this:

• Exploration: Identify a high-volume workflow and launch a focused pilot with clear success metrics.
• Pilot: Establish leadership ownership and select a second use case based on business impact, not novelty.
• Integration: Connect AI tools to your core systems and eliminate data silos that limit visibility and efficiency.
• Optimization: Use shared data and connected workflows to identify trends, anticipate needs, and support more proactive decision-making.

The human side of adoption evolves alongside the technology. Early on, AI-generated work should be reviewed closely, much like the work of a new employee. As adoption expands, firms should define what success looks like for each workflow, including metrics such as response time, accuracy, and escalation rates. Establishing those expectations early creates accountability and makes it easier to scale adoption with confidence.

Regardless of your firm’s stage of AI adoption, the principle remains the same: identify the highest-impact opportunity for your stage and build deliberately rather than accumulating disconnected point solutions.

Start With the Business Problem, Not the Technology

The fix isn’t a better tool. It’s starting from the right question: which two or three workflows, if fixed, would move the numbers leadership actually watches?

For most plaintiffs firms, intake is the obvious first answer. It’s high-volume, rules-based, and easy to put a dollar figure on when it breaks down. But intake shouldn’t be the only answer, and the right second workflow depends on what kind of practice you run.

Single-event dockets, including auto accidents, slip-and-fall cases, and premises liability matters, tend to bottleneck on medical record follow-up, treatment status checks, and claims status updates with adjusters. Mass tort and MDL programs face different challenges: high-volume claimant intake, batch processing of medical records and fact sheets across hundreds or thousands of claimants and administering a settlement matrix consistently across a cohort.

Either way, the rule is the same: Identify what’s actually slowing your firm down before you pick a vendor.

Build the Operating Foundation

A pilot that never leaves its department usually isn’t a training problem. It’s a foundation problem.

Before scaling beyond the first use case, four elements need to be in place:

• Data quality: AI is only as useful as the data it’s working from. Inconsistent intake forms and disconnected spreadsheets will undercut even the best tool.
• System integration: The tool should operate within the systems your firm already relies on, not alongside them. When information lives in multiple places, adoption slows and efficiency gains are harder to sustain. A second system that no one consistently checks can create more problems than it solves.
• Security and ownership: Someone within the firm must be responsible for data handling, client communication standards, and compliance oversight. “The vendor handles that” is not an ownership answer.
• Documented procedure: The firm should clearly define what the tool does, who reviews its output, and when a matter should be escalated to a human. Those expectations should be established before expanding adoption, not after a problem arises.

Preserve the Human Layer

The goal of AI is not to remove people from the process. It’s to remove the administrative friction that keeps them from doing the part of the job that actually requires them.

AI can capture a call, qualify a lead against firm-specific criteria, and log a follow-up. It shouldn’t be making the judgment call on a client relationship, a settlement conversation, or a case that doesn’t fit the standard pattern.

Firms that get this wrong often face resistance from staff who see automation as replacement. Firms that get it right introduce the tool as what it actually is: something that clears the repetitive work off a case manager’s desk so the judgment, empathy, and accountability that only a person can provide has more room to happen.

The most effective AI strategies do not replace the human element. They strengthen it by allowing people to spend less time managing routine processes and more time serving clients and moving cases forward.

Measure What Actually Changed

None of this is worth doing if leadership can’t tell, six months later, whether it worked. The relevant measures aren’t exotic: response time, conversion rate, cycle time per case, staff capacity, error rates, client experience, and return on investment. Define those metrics before the rollout, not after. A result that was never clearly defined isn’t a result. It’s a story.

The payoff for firms that do this well is measurable, not just anecdotal. Clio’s 2025 Legal Trends Report found that firms with broad AI adoption were nearly three times more likely to report revenue growth than firms without it. Improved operations, rather than new business alone, was the leading explanation firms gave for that growth.

The advantage compounds because firms measure the results, learn from them, and build on them. The firms that move beyond the pilot stage treat AI adoption as an operational strategy, not a technology project. That distinction often determines whether an investment delivers lasting value or remains an isolated success.

¹ “AI implementation FAQs: What legal leaders ask most often,” Thomson Reuters Legal Blog –
https://legal.thomsonreuters.com/blog/what-legal-leaders-ask-most/.