Legal AI implementation:
Avoiding the common pitfalls
A practical guide to avoiding the mistakes that derail legal AI adoption, from choosing the right workflows to measuring real value.
Implementation is where most new innovations succeed or fail
Choosing the right legal AI platform matters. But the choice is only the beginning. Most of the firms that are not getting value from legal AI did not make a bad selection decision. They made a good selection decision and then ran the implementation badly.
This guide is for anyone involved in deploying legal AI, whether you are about to start, currently mid-rollout, or sitting with a platform that has not landed as well as it should have. The pitfalls here are drawn from what consistently goes wrong in UK legal teams. Against each one is what to do instead.
One other thing worth saying at the outset. Not every firm is at the same point. Some are still deciding whether to move. Others think they have legal AI because Copilot came bundled with their Microsoft licence. Some have had a platform running for a year. All of those situations are addressed here, and none of them is too late.
"Firms that do not have it are just going to get left behind."
Solicitor
Preston Redman Solicitors
Pitfall 1: Starting without identifying the right workflows
What goes wrong
The platform goes live. A handful of enthusiasts use it for everything. The majority use it for nothing in particular. Three months in, usage data looks thin, someone asks whether it is worth the cost, and the deployment drifts.
The underlying problem is almost always the same: no one agreed in advance which specific tasks the AI was supposed to change, which meant there was no baseline to measure against and no obvious reason for the broader team to change their habits.
What to do instead
Before procurement, before rollout, before anything else: identify two or three tasks that consume significant time, have clear quality standards, and produce output someone can check. High-volume contract review, standard research queries on recurring topics, first-draft correspondence. Those are your starting points.
Look at the last six months of your team's output and find what repeats. The same document types, the same research questions, the same requests from the same parts of the business.
For each candidate task, ask: does this consume enough time to be worth improving, and could someone verify whether the AI output is correct without repeating the underlying work?
Agree those two or three tasks as the defined scope of the initial rollout. Everything else comes later.
"I had to summarise this case, and normally that would take me 20 minutes or half an hour. Whilst I still had to read the case, I got to the main point in five minutes using Lexis."
Pitfall 2: Assuming general AI and legal AI are the same thing
What goes wrong
Microsoft 365 Copilot is a capable productivity tool. It helps with summarising meetings, drafting emails, and navigating large documents. It is not legal AI. It is not grounded in authoritative UK legal sources. It does not draw on Halsbury's Laws, validated case law, or current legislation. It cannot tell you whether the case it just cited has been overruled.
Firms that believe they have legal AI covered because they have Copilot are exposed. Not because Copilot is a bad product, but because it is answering a different question.
What to do instead
Apply a simple test to any platform you are using or considering. Ask it a specific question about a recent statutory change or a recent Court of Appeal decision in your practice area. Check whether the response is grounded in the actual source, whether that source is current, and whether the citation can be verified.
The test that matters
Ask the platform: what are the current notice requirements under the Employment Rights Act 2025 for collective redundancy? A legal AI platform grounded in authoritative UK sources will give you a traceable, current answer. A general AI tool will give you a confident response that may or may not reflect the current law.
Lexis+ with Protégé draws on over 670,000 UK cases, legislation updated within 24 hours of Royal Assent, Halsbury's Laws, and more than 30,000 forms and precedents across UK practice areas. That is not a claim about features. It is a description of what grounded legal AI requires.
LexisNexis has zero-retention arrangements with all foundation model providers.
UK customer data is stored in eu-west-2, London. All European failover remains within Europe. Full security documentation is available on request. These answers exist and can be provided to your IT function before procurement begins.
Pitfall 3: Underestimating the security conversation
What goes wrong
A platform is selected. Someone in IT or risk asks whether it has been through the standard security review. It has not. The review takes three months. By the time it concludes, the original enthusiasm has faded, the people who were going to champion it have moved on to other priorities, and the rollout has lost its momentum.
Alternatively, and this happened with many platforms adopted quickly in 2023 and 2024, the security review was skipped entirely. The platform is in use. Nobody is sure whether client data is being used to train models. The professional conduct implications have not been considered.
What to do instead
Get ahead of the security questions before you start vendor conversations, not after you have made a selection. The questions are predictable:
- Does the vendor have zero-retention arrangements with all foundation model providers? Your prompts and documents must not be used to train any large language model. Get this confirmed in writing.
- Where is UK customer data stored? Data residency within the UK or Europe is a standard professional requirement for UK legal work.
- What SOC 2 audit coverage exists? Ask for the current report.
- Can administrators restrict access, disable features, and manage permissions at an organisational level? This matters for professional conduct compliance.
Pitfall 4: Rolling out without an internal champion
What goes wrong
The platform launches. An email goes out. A short training session runs. Three weeks later, the people who were enthusiastic at the beginning are still using it. Everyone else has reverted to how they worked before. Nobody is actively tracking whether adoption is happening. Nobody is fielding the questions that new users have but do not know who to ask.
What to do instead
Name a specific person as the internal champion before the rollout begins. This is not the IT contact. It is a legal professional, close to the team's actual work, who is given the explicit responsibility of supporting colleagues, gathering feedback, and escalating issues.
That person needs three things: standing within the team (they cannot be a junior person nobody will listen to), time set aside for the role (not just added to an already full schedule), and a clear brief. They are not there to fix technical problems. They are there to help colleagues get value from the tool and to flag what is and is not working.
It is becoming part of their daily operating software. It is becoming an essential tool in their armoury.
AI adoption is high
Yet integration into existing processes is low
17%
of lawyers said AI is integrated in their existing processes.
Pitfall 5: Not measuring anything.
What goes wrong
Nobody captured how long things took before the platform went live. Nobody set targets for how much time should be saved. Nobody agreed what success looks like. Six months in, the platform either renews on inertia or gets cancelled on the same basis. The decision either way is made without evidence.
What to do instead
Set benchmarks before the rollout begins. For each target workflow, record how long it currently takes. Define what success looks like: a specific time saving, a usage rate on eligible work, or a quality threshold. Without a pre-rollout baseline, post-rollout data tells you nothing.
The most useful metric after rollout is not login rates. It is the proportion of eligible work where the tool was actually used. A team that logs in regularly but handles most actual work the same way as before has not adopted the platform. That distinction is invisible if you are only counting logins.
If you already have a platform in place
The evaluation checklist above applies to what you have now, not only to what you might choose next. If you already have legal AI deployed, these additional questions are worth working through honestly.
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Is it being used on eligible work? |
Track the ratio of AI-assisted work to total work in your target areas. Login rates are not a measure of adoption. If the tool is only used occasionally on high-volume, recurring tasks, the deployment is not working. |
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Do users trust the output? |
If lawyers are routinely re-checking AI responses from the beginning rather than reviewing and refining, the accuracy question has not been resolved. This is a content quality issue, not a user behaviour issue. |
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Can you verify what it draws on? |
If you cannot trace a response back to a source, you cannot defend it in a professional context. Ask whether your current platform produces verifiable, citable output. |
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Has the security been properly reviewed? |
Many platforms adopted quickly during 2023 and 2024 were not subject to the security scrutiny that standard procurement would require. Apply the security checklist above retrospectively. |
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Is it keeping pace? |
The legal AI market has moved significantly in the last 18 months. A platform that was market-leading in 2023 may now be behind on workflows, content depth, or integrations. Evaluate against the current market, not against your original decision. |
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What is it actually costing you? |
Include the cost of time spent on workarounds, the cost of work the tool cannot do, and the opportunity cost of not having full-capability AI. The headline licence fee is rarely the full picture. |
Building a number you can defend
A legal AI platform is a budget decision. At some point you will need to present a number that justifies the investment to someone who is not a lawyer and does not share your instinct that this is the right move.
The strongest cases combine three things: current cost of the workflows AI will change, a conservative estimate of efficiency improvement, and a clear translation into financial terms. Not just hours saved. Hours saved converted into recoverable capacity, reduced external counsel spend, or avoided recruitment.
What UK legal teams have reported
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Pinsent Masons |
Approximately 30 minutes saved per user per week across a 30-person pilot. |
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South Tyneside Council |
1.5 to 5 hours saved per user per week on target tasks. |
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Gordons LLP |
Statutory clause check: 10 to 15 minutes reduced to under one minute. |
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Preston Redman Solicitors |
Equivalent resource value: approximately £50,000 to £60,000 per year across the team. |
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Brethertons Solicitors |
Platform usage rose four times within month one and five times within month two. |
Model your own numbers
The Lexis+ AI Competitive Advantage Calculator lets you input your current weekly hours on research, drafting, and summarising, then model the value of different levels of efficiency improvement. Use a conservative assumption. A number you can defend is more useful than an optimistic one that does not survive a finance conversation.
Pitfall 6: Ignoring the people who are resistant
What goes wrong
The enthusiasts adopt quickly. Their use cases are well-documented. Their feedback is positive. The rollout looks like it is working. Meanwhile, a significant portion of the team has quietly decided this is not for them and is carrying on as before. Nobody is actively addressing their concerns because the headline numbers look fine.
What to do instead
Include people who are resistant in your initial pilot group, not just people who are already interested. Resistant users are more likely to surface the friction points that will affect the majority of the team in a wider rollout. Their objections are useful information.
The most common reasons for resistance in legal teams are worth understanding:
- Accuracy concerns: lawyers who have seen a poorly performing AI output once will assume the platform is unreliable. Address this by demonstrating the difference between general AI and legal AI grounded in authoritative sources. Let them test it on a real query in their own practice area.
- Professional conduct concerns: lawyers are right to ask about the implications of using AI on client matters. Make the security and data answers available clearly and early. Do not assume people will find them.
- Workflow disruption: a platform that requires switching between systems or copying text between windows will lose to inertia. The tool has to sit close enough to existing work that using it is easier than not using it.
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I wouldn't want to be without it now. The time saving on the search function is extremely good. If I'm not sure about something, I can just check it. Whereas before, you spent ages trying to find what you were looking for. Rothera Bray Solicitors |
Pitfall 7: Underinvesting in the business case
What goes wrong
The people who want the platform get it through on enthusiasm and goodwill. The business case is never properly built. When the renewal conversation comes around, or when a cost-cutting exercise asks every team to justify its software spend, the case for keeping it has to be made from scratch with no data to support it.
What to do instead
Build the financial case before the platform goes live, using conservative assumptions. The case has three parts: current cost of the workflows AI will change, efficiency improvement on those workflows, and translation into financial value.
For the efficiency assumption, 25 to 35 per cent is a reasonable range for well-suited workflows. Better to exceed a conservative target than to miss an optimistic one. Translate time savings into headcount equivalency: 300 hours saved per lawyer per year is roughly 0.2 FTE in recovered capacity per person.
Where work currently goes to external counsel, the insourcing argument is often the strongest part of the case. Work that costs external rates can be absorbed internally at a significantly lower cost once AI makes it faster for your team to handle.
I would probably have to employ two more trainees. That is £50,000 to £60,000 a year across my team.
Solicitor, Preston Redman Solicitors
What good implementation looks like
A legal AI strategy that is working does not look dramatic. It looks like lawyers using the tool as a matter of course on the work it was designed for, with no particular effort or ceremony. It looks like research that takes minutes rather than hours. Drafts that start at 70 per cent rather than zero. Summaries that free up time for the work that actually requires legal judgement.
Getting there requires the things described in this guide: clear workflow choices, resolved security, a named champion, measured adoption, engaged sceptics, regular reviews, and a financial case that can survive scrutiny.
None of those things are complicated. But all of them require someone to be responsible for them. If your current strategy is missing any of them, that is where to focus next.
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30 days |
Check usage on target workflows. Address friction points. Confirm the internal champion is active and visible. |
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60 days |
Review first usage data against baseline. Identify the features that are not being used and find out why. |
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90 days |
Assess whether the original workflow choices are delivering the expected value. Plan the next wave of adoption. |
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Ongoing |
Quarterly reviews against the evaluation checklist. Annual assessment of whether the platform is keeping pace with the market. |
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