AI Maturity in Legal and Why Some Teams Move Faster

AI adoption is no longer the issue for legal teams. Most organizations have already moved past pilots. Tools are in place. Lawyers are using them. Early gains in speed and efficiency are starting to show up.

Some teams are clearly pulling ahead, embedding AI into how work actually gets done. Others remain stuck in experimentation, despite having access to the same tools. That gap was at the center of a recent LegalTechTalk webinar, AI Maturity Across the Legal Ecosystem: Honest Assessment for Smarter Investment, featuring leaders from Kraft Heinz, Ashurst, and Harvey.

What emerged from that discussion was a simple but important idea. AI maturity has very little to do with how many tools a team has deployed. It has far more to do with how intentionally legal work is designed.

What “AI Maturity” Really Means

One of the most useful reframes came from Kraft Heinz’s Global Legal Counsel, Saideh Ahmadloo, who described maturity not as a measure of adoption, but as a measure of design.

In her words, AI maturity reflects the degree to which legal work is structured as a system rather than held together by individuals.

That distinction matters. Many legal teams are still operating in environments where outcomes depend heavily on individual effort, experience, and judgment. AI can accelerate parts of that work, but it does not fundamentally change how the system operates.

More mature teams begin to shift away from that model. Work becomes more structured. Processes are more consistent. Systems start to support decision making. At the highest levels, different systems begin to work together, with AI playing a more active role in orchestrating tasks. This progression is less about adding technology and more about rethinking how work flows through the organization. Firms that chart ahead set up the right conditions for firm-wide reimagination, embedding a governance and knowledge management lens that underpins other aspects of the work.

The Real Barrier Isn’t Technology

This aligns closely with what we see across Harvey customers, and underpins how we are investing, via our Transformation Office and Legal Engineering and Customer Success Management functions.

As outlined in What It Really Takes to Transform a Law Firm With AI, the primary barrier to transformation is not capability. It is the human side of change.

AI tools are already widely available and improving quickly. In many cases, they are ahead of what organizations are ready to absorb. The technology is already proving itself, now the surrounding environment must support meaningful change.

In practice, that comes down to a set of organizational conditions. Leadership needs to do more than endorse AI in principle. Teams need space to experiment and learn. Workflows need to evolve, not simply absorb new tools. Organizations also need the right governance, oversight, and guardrails to build trust and confidence as adoption expands. Communication needs to be clear and consistent so that lawyers understand why this matters and how it connects to their role.

Without those elements, adoption tends to plateau. Tools get used, but the way work is delivered remains largely unchanged.

Why Maturity Often Feels Uneven

One of the more nuanced points from the webinar was that AI maturity does not develop evenly across an organization.

Even within a single legal team, different groups can respond very differently to the same technology. Some adopt quickly and build momentum. Others hesitate or struggle to integrate AI into their day-to-day work.

Saideh described this through three interconnected layers: foundation, control, and capability.

The foundation includes the underlying tools, data, and processes. Control relates to governance and how well systems are aligned with the broader business. Capability focuses on people, skills, and mindset.

While the strength of each layer matters, it’s more crucial to examine how well they move together. When one advances without the others, progress tends to stall. A team might invest heavily in tooling but see limited impact if the broader organization has not aligned around how that tooling should be used. In other cases, individuals may be highly capable and motivated but constrained by a lack of organizational support.

This imbalance helps explain why maturity can feel inconsistent, even when the same platform is available across teams.

Where Most AI Efforts get Stuck

What stood out in the discussion is that very few organizations are actually failing to adopt AI. In most cases, the tools are there and people are using them. The friction shows up in what happens next.

Leadership support is often part of the story, but it does not always translate into day-to-day behavior. AI is positioned as important, yet still feels optional in practice. Without clear expectations or visible examples from senior lawyers, it is easy for teams to treat it as something to explore rather than something to rely on.

There is also a tendency to fit AI into existing ways of working instead of rethinking them. Lawyers might use it to draft or review documents, but the structure of the work itself stays the same. Over time, that limits the impact. Efficiency improves at the margins, but the underlying system does not evolve.

At Ashurst, Director of Digital Experience, Sarah Chambers, pointed to a related challenge. The real work begins after a tool is deployed. Maturity comes from continually adapting workflows, governance, and ways of working as the technology evolves, rather than treating AI as a static capability that can simply be rolled out and maintained.

That tension shows up in more practical ways as well. Incentives do not always reward efficiency. Governance concerns can slow down broader adoption. And many teams are still working without a clear sense of what “good” looks like, which makes it harder to measure progress or prioritize where to invest.

Taken together, these challenges are less about capability and more about coordination. The technology is moving quickly. The harder task is getting the organization to move with it.

What More Mature Teams are Doing Differently

When you look at the teams that are making real progress, the difference is not that they have better tools. It is that they have started to treat AI as part of how the organization operates.

That shift shows up in small but meaningful ways. Leadership is more visible, not just in supporting AI initiatives, but in actually using the technology in their own work. That alone changes how the rest of the organization responds. It signals that this is not a side project or an experiment, but something that is expected to become part of everyday practice.

Learning also looks different. Instead of relying on formal training sessions, teams build capability through repeated use. Lawyers test ideas on real matters, share what works, and refine their approach over time. That process is often informal, but it is what turns initial curiosity into something more durable.

Communication plays a role here as well. In organizations that move faster, there is a clearer narrative around why AI matters and how it connects to the broader direction of the business. That clarity makes it easier for lawyers to understand where to focus and how their own work fits into the bigger picture.

Over time, these changes begin to influence how work itself is structured. AI is no longer something that sits alongside existing workflows. It becomes part of how those workflows are designed, how work is reviewed, and how outcomes are delivered. That is usually the point where the impact becomes more visible, not just in individual productivity, but in the consistency and scalability of the work.

This is the pattern Harvey has seen across firms that are moving from early adoption into something more sustained. Progress tends to come from alignment across leadership, capability, communication, and ways of working, rather than from any single initiative on its own.

A Shift in how Legal Teams Collaborate

One of the more interesting shifts discussed in the webinar was how AI is starting to change the relationship between in-house teams and outside counsel.

For a long time, that relationship has been relatively straightforward. Work is handed over, completed, and returned. The interaction is defined by the exchange of outputs.

That model is beginning to loosen.

As more teams adopt the same platforms, they are increasingly working in shared spaces. Instead of passing work back and forth, they are contributing to it at the same time. The process becomes more visible, and collaboration starts to happen earlier and more continuously.

Saideh Ahmadloo described this as a move away from simply sharing work toward sharing systems and ways of working. That shift may sound subtle, but it has broader implications. It changes how value is created, how expertise is applied, and how both sides think about their role in the relationship.

At Ashurst, Sarah Chambers highlighted another dimension of this change. Working more openly in shared tools requires a different level of trust. It also requires a willingness to be more transparent about how work is done, in addition to what the final output looks like. That kind of collaboration can feel unfamiliar, but it is also what enables more meaningful co-creation over time.

From Adoption to Transformation

Taken together, these changes point to a broader transition that many legal teams are now working through.

The first phase of AI in legal was about access and useage. Getting tools into the hands of lawyers, understanding what they could do, and identifying where they might be useful.

The next phase is less straightforward. It involves rethinking how those tools fit into the structure of the organization and how they shape the way work is delivered.

That transition does not happen on its own. It requires deliberate effort to align people, processes, and technology around a shared direction. It also requires a willingness to revisit long-standing assumptions about how legal work should be done.

This is part of the thinking behind Harvey’s recently announced Transformation Office, which focuses on helping organizations move through that phase. The goal is to help teams not only make the operational changes that allow AI to have a more sustained impact, but to think more strategically about AI.

Because in practice, transformation is not defined by whether a tool is available. It is defined by whether it becomes part of how work actually happens.

What AI Maturity Looks Like in Practice

AI maturity is often discussed as something that can be measured or benchmarked. In reality, it tends to show up in more practical ways.

You see it in how work is structured, in how quickly new approaches take hold, and in how comfortable teams are adapting as the technology continues to evolve.

The organizations that are moving fastest are not necessarily the ones with the most advanced capabilities. They are the ones that have taken the time to rethink their systems and follow through on that thinking in a consistent way.

That is what turns adoption into something more meaningful. To learn more about how Harvey can help guide your transformation efforts, contact our team: