The legal AI market is experiencing a gold rush. Every vendor claims their solution will transform a practice, save thousands of hours, and pay for itself within months. Some of these claims are legitimate. Many are not. The gap between a good demo and a good deployment is wider than most firms expect.
This guide provides a structured approach to evaluating legal AI tools. It prioritises a firm's actual needs over a vendor's sales narrative.
Start With Problems, Not Features
This principle sounds obvious. It is violated constantly. The typical pattern: a partner attends a conference, sees an impressive AI demo, brings the vendor in for a meeting, and the firm purchases a tool that solves a problem it does not actually have.
Before taking a single demo
Document the firm's top three workflow pain points. Be specific:
"Contract review for commercial leases takes an average of 4 hours per document and we handle 30 per month"
"We lose an estimated 15% of potential clients because our intake response time exceeds 24 hours"
"Associates spend roughly 40% of their time on research tasks that could be accelerated"
These problem statements become the evaluation criteria. If a vendor cannot clearly articulate how their tool addresses at least one documented pain point, the conversation should end there.
The "so what?" test
For every feature a vendor demonstrates, ask one question: "What specific problem at our firm does this solve, and how would we measure the improvement?" If the answer is vague or general, the feature is a distraction.
Security and Compliance: The Non-Negotiable Foundation
For law firms, data security is not a feature to evaluate. It is a precondition. Any tool that fails to meet security requirements is disqualified, regardless of how impressive its capabilities are.
Minimum security requirements
SOC 2 Type II compliance: This is the baseline. Type I is a snapshot. Type II demonstrates sustained security practices over time.
Data residency: Where is client data stored? For UK firms, data should remain within UK or EU data centres unless there is explicit client consent for broader processing.
Data processing agreement: The vendor must provide a clear DPA specifying how client data is handled, stored, processed, and deleted.
Model training: Does the vendor use client data to train or improve their models? The answer must be no, with contractual guarantees, unless there is explicit client consent.
Encryption: Data should be encrypted at rest and in transit. This is standard but worth confirming.
Access controls: Can the firm control which team members access which features? Can access to sensitive matter data be restricted?
Questions clients will ask
Increasingly, sophisticated clients are asking their law firms about AI usage. When a client asks "Are you using AI on my matters, and if so, how is my data protected?", the firm needs a clear, confident answer. Evaluating security upfront means that answer is ready from day one.
Integration Over Innovation
A brilliant AI tool that requires lawyers to leave their primary working environment, navigate to a separate platform, upload documents, wait for processing, and manually transfer results back into their workflow will not be used. Adoption is a function of friction. Every additional step adds friction.
The integration hierarchy
Embedded: The AI works within the tools the team already uses. Inside the DMS, within Word or Outlook, as part of the practice management system. This is the gold standard.
Connected: The AI lives in a separate interface but integrates via API with existing systems. Documents and data flow automatically. Acceptable for most use cases.
Standalone: The AI is a separate platform with no integration. Users must manually move data in and out. This is where adoption fails.
What to assess
Does the tool integrate with the firm's specific practice management system? Not "we integrate with practice management systems." The firm's specific one.
Does it work with the document management system?
Can it authenticate against the existing identity provider (Active Directory, Entra ID, etc.)?
Is the integration maintained by the vendor, or does it require a third-party connector?
Total Cost of Ownership
The subscription fee on a vendor's pricing page is the beginning of the cost conversation, not the end. Mid-market firms that budget only for licence fees consistently underestimate the true cost of AI adoption by 40-60%.
The full cost picture
Licence fees: The obvious starting point. Per-user, per-matter, or flat-fee pricing models each have implications for how costs scale.
Implementation and integration: Professional services to set up the tool, connect it to existing systems, and configure it for the firm's workflows. Budget £5,000-25,000 depending on complexity.
Training: Time spent training the team is time not spent billing. For a 30-person firm, initial training might represent 100-150 hours of collective non-billable time.
Customisation: Configuring the tool for specific templates, clause libraries, or risk criteria is additional cost, either paid to the vendor or absorbed as internal effort.
Ongoing support and maintenance: Annual renewal costs, version updates, additional training for new joiners, and internal time spent managing the tool.
The productivity dip: For the first 30-60 days, most teams are slower with a new tool than without it. Budget for this transition cost.
The ROI calculation
Require the vendor to build a realistic ROI model. Not "firms like yours typically see..." but a calculation based on the firm's specific volume, current process times, and hourly rates. If they cannot or will not do this, treat that as a signal.
Reference Checks: The Most Underused Evaluation Tool
Vendor-provided case studies are marketing materials. They are selected for impact and framed for maximum persuasion. Reference checks with actual users give an unfiltered picture.
What to ask references
"What was the implementation experience actually like? How did it compare to what was promised?"
"How long before your team was genuinely productive with the tool?"
"What is your actual usage rate? What percentage of the team uses it regularly?"
"If you were starting over, would you choose the same tool?"
"What is the one thing you wish you had known before purchasing?"
Critical matching criteria
Ask for references that match the firm on at least two of these dimensions:
Firm size: A tool that works for a 200-lawyer firm may require infrastructure a 20-lawyer firm does not have.
Practice area: Contract review AI configured for financial services transactions may perform poorly on construction contracts.
Geography: Jurisdiction-specific legal content matters. A tool trained on US law may have gaps in UK or EU coverage.
The 90-Day Success Framework
Before signing a contract, agree on what success looks like at 90 days. This is not about setting traps for the vendor. It is about ensuring both parties share a measurable definition of value.
Define three metrics
Adoption: What percentage of the target user group is using the tool at least weekly?
Efficiency: What measurable time saving has been achieved on the target workflow?
Quality: Has the tool maintained or improved output quality? Measure through QC reviews, error rates, or client feedback.
Build in a review point
Structure the contract to include a formal review at 90 days. This should not be a termination clause (that creates adversarial dynamics). It should be a structured checkpoint where both sides assess progress against agreed metrics and adjust the approach if needed.
The best vendor relationships are partnerships, not purchases. A vendor confident in their product will welcome structured evaluation.
The Decision Framework
After evaluating against all criteria above, score each tool on a simple three-point scale for each dimension:
Security: Pass/Fail (no partial credit)
Problem fit: 1-3 (how directly does it address documented pain points?)
Integration: 1-3 (how seamlessly does it work with existing systems?)
Total cost: 1-3 (how does the full cost picture compare to expected value?)
References: 1-3 (what did comparable firms report?)
Vendor quality: 1-3 (how confident is the firm in the vendor's ability to deliver and support?)
Any tool that fails on security is eliminated. Among the remaining options, the highest total score wins. If scores are close, weight integration and references most heavily. Those are the dimensions that most strongly predict real-world success.