Today, when it comes to AI, the question firms should ask themselves is: Is our firm underneath the technology ready for AI?
Many organisations are being caught out, risking successful AI adoption. AI will not tidy up poor data, connect systems that have never properly spoken to each other, or turn years of scattered know-how into a reliable service model by itself.
In fact, AI often does the opposite. It shines a light on the operational gaps that were previously hidden by hard work, long hours and informal workarounds. Building AI-ready foundations is therefore not a simple technology exercise. It is a broader programme of operational improvement, data discipline and behavioural change. The firms that make real progress will be the ones that create the conditions in which technology can genuinely improve how work is delivered.
Turn operational data into management insight
Reporting is a useful place to start because it shows the problem so clearly. In many firms, management information is still compiled after the work has happened. Teams pull updates from matter files, inboxes, time records and spreadsheets, often under pressure and at the last minute. By the time the report reaches a board pack or partner meeting, it may already be slightly out of date.
An AI-ready firm takes a different view. It asks what the business actually needs to know, then builds the capture of that information into the work itself. Where are matters slowing down? Which teams are carrying too much work? Which types of matters are profitable? Where are risks beginning to appear? Which clients need more proactive communication?
Once those questions are clear, data stops feeling like an administrative exercise. It becomes part of how people manage work, serve clients and make decisions. The aim is not to ask lawyers and business teams to fill in another spreadsheet. It is to make sure useful information is captured once, at the right point, and then reused for workload planning, client updates, pricing conversations and strategic insights.
Replace disconnected systems with a joined-up workspace
Almost every firm has some version of the same problem: the official system says one thing, the team spreadsheet says another, and the most up-to-date answer is sitting in someone’s inbox. These side-of-desk solutions often begin for sensible reasons. People need to get work done, and they create a tracker or workaround because the existing process is too slow, rigid or disconnected.
The difficulty is that every workaround creates another version of the truth. Over time, data becomes harder to trust, knowledge becomes harder to reuse, and reporting becomes harder to automate. AI cannot fix that fragmentation simply by being added on top. It needs context, and that context must come from systems and processes that are connected enough to support it.
A joined-up workspace does not have to mean a single mammoth platform. It means giving teams a more coherent place to manage matter data, documents, tasks, workflows and knowledge. It also suggests designing the user experience so that the new way of working is easier than the old one. People will not adopt a system because the firm has bought it. They will adopt it when it saves time, reduces duplication and helps them do better work.
Turn expertise into live workflow
Most firms are not short of expertise. They are full of it, but it sits in precedent banks, playbooks, old matter files, pricing notes, client preferences and, in the heads of experienced people who know exactly how a particular issue should be handled. The problem is that knowledge is not always available at the point of need.
This is where workflow matters. Instead of treating expertise as a document to search for, firms can begin to embed it into the steps of the work. A matter can prompt the right questions. A process can surface the relevant guidance. A document can draw on approved language and known data. A team member can be nudged towards the next best action without having to remember where the answer was stored, and so forth.
This does not require a firm-wide transformation on day one. In practice, the best starting point is usually a specific pain point: a repeated question, a high-volume process, a slow reporting cycle, or a document that takes too long to produce. Solve that well, involve the people who do the work, and use the first version as something to learn from. Over time, static expertise becomes live operational capability.
Build stronger data foundations before AI disappoints
There is a simple reason many AI pilots underwhelm: the foundations are not strong enough. The model may be impressive, but if the data is inconsistent, the use case is vague, the governance is unclear or the users are unsure how to work with it, the output will disappoint. Strong AI adoption depends on more than access to powerful tools.
Firms need structure for their data to be trusted, governance for it to be used confidently, and education for people to understand the possibilities and the limits of the information. Matter information, billing data, task progress, risk indicators, document status and client-specific knowledge can all become valuable signals – if they’re captured consistently and can be retrieved in context.
That context will matter even more as AI usage grows. Better data foundations help AI systems retrieve the relevant matter history, workflow stage, document set or knowledge source. That improves quality, reduces review effort and helps control cost. In other words, good data is what makes intelligent tools useful in practice.
Outside expertise can accelerate change
The pace of technology change can make firms feel they need to move immediately, and sometimes they do. New products arrive quickly, existing platforms add new AI features, and competitors appear to be experimenting at speed. However, urgency should not be confused with panic. Buying software is rarely the hard part. Knowing what capability the firm wants to build is the more important element.
Outside expertise can help. The right support can bring perspective on what is working elsewhere, help prioritise use cases, design practical, best-practice workflows, build adoption plans and add capacity where internal teams are already stretched. It can also help firms avoid expensive distractions and focus on changes that will make a measurable difference.
For firms without large innovation or transformation functions, that support can be particularly valuable. External partners provide structure, coaching and momentum, while internal champions keep the work grounded in the reality of the firm. The objective of bringing in outside expertise should be to accelerate change, transfer knowledge and make sure new ways of working are actually adopted. Outside expertise is often misunderstood for outsourcing.
The real foundation is capability
AI-ready foundations are built through a series of practical choices. Capture better data at the point of work, replace weak workarounds with connected systems, turn expertise into workflow, and strengthen governance.
Firms don’t need to boil the ocean all at once. The better approach is to start with the work itself: determine where it slows down, where information is lost, where people duplicate effort and where clients would benefit from clearer insight. Fix those foundations, and AI has something real to build on. Without them, even the best tools will struggle to deliver on their promise.
Robust matter management can be that real foundation. If your firm is exploring AI adoption or in the process of deploying it firmwide, your LexisNexis Enterprise Solutions Account Manager can guide you.
