10-Point AI Readiness Checklist

Are your critical practice data sources digitized and structured?

Consider:

• Matter management system (DMS) with consistent metadata
• Document repositories with searchable content
• Time recording and billing data
• Precedent libraries and knowledge databases
• Client communication records (email, correspondence)

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: AI-powered contract review, legal research, and document automation require high-quality structured data. Legacy systems with inconsistent filing, poor metadata, or paper-based processes limit AI capabilities.

Quick win: Audit data quality in one practice area (e.g., commercial contracts) to identify gaps in document categorization, metadata consistency, and searchability.

Do you have governance frameworks for AI use in legal practice?

Consider:

• SRA Code of Conduct compliance (competence, confidentiality, supervision)
• GDPR for client and employee data
• UK AI Act obligations for legal services
• Professional indemnity insurance considerations for AI use
• Conflict of interest checks for AI training data

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Legal practice is highly regulated. Using AI without proper governance creates regulatory risk, client confidentiality breaches, and professional liability exposure. The SRA expects firms to understand and supervise AI tools.

Quick win: Review SRA guidance on technology and innovation. Map regulatory requirements to planned AI use cases and flag high-risk scenarios for ethics committee review.

Do you have the technical foundation to deploy AI at scale?

Consider:

• Cloud infrastructure with appropriate security for legal data
• API integration with practice management systems (PMS)
• Single sign-on (SSO) and access control for AI tools
• Data residency requirements (UK/EU data sovereignty)
• Network security for external AI service providers

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Legal AI tools must integrate seamlessly with existing PMS, DMS, and billing systems. Poor integration means manual data transfer, duplicate entry, and limited adoption. Client data security is non-negotiable.

Quick win: Assess API availability and SSO capability of your top 3 systems (PMS, DMS, email) to understand integration readiness.

Does your team understand AI capabilities, limitations, and risks in legal contexts?

Consider:

• Partner/senior lawyer understanding of AI opportunities in legal practice
• Associate/trainee capability to use AI research and drafting tools effectively
• Support staff ability to leverage AI for document processing and admin tasks
• Training programs for ethical AI use and output validation
• Clear protocols for when to escalate AI concerns

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Lawyers must validate AI outputs and understand limitations—blind reliance creates client risk and regulatory issues. AI literacy ensures tools augment expertise rather than replace judgment.

Quick win: Run a workshop for associates on AI-assisted legal research—using real client scenarios to build understanding of AI strengths and weaknesses.

Have you identified which AI use cases deliver the highest value for your practice?

Consider:

• Current operational pain points (research time, document review burden, admin workload)
• Strategic objectives (margin improvement, capacity growth, client service differentiation)
• Practice area-specific opportunities (contract review in M&A, case law research in litigation)
• Quick wins (low-risk, high-value) vs. transformational long-term projects
• Client-facing vs. internal efficiency applications

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Not all AI use cases are equal in legal. Contract review in high-volume commercial work has different risk/value profiles than litigation research. Start with high-value, lower-risk applications.

Quick win: List your top 3 practice bottlenecks and research which AI solutions specifically address them in legal contexts (e.g., due diligence document review, lease abstraction, case law research).

Can you assess legal AI vendors objectively without lock-in?

Consider:

• Understanding of legal AI vendor landscape (specialist vs. general)
• Criteria for evaluating vendor claims and case studies
• Data security, confidentiality, and client privilege considerations
• Vendor training data sources (risk of client data leakage or conflicts)
• Exit clauses, data portability, and alternatives

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Many “AI for legal” vendors overpromise on automation and accuracy. Legal-specific expertise, transparent training data, and UK data residency are essential for professional services.

Quick win: Create a vendor evaluation scorecard that includes legal-specific requirements (SRA compliance, client confidentiality, UK data hosting, professional indemnity coverage).

Have you established processes to protect client confidentiality when using AI?

Consider:

• Data anonymization protocols before AI processing
• Vendor agreements with client confidentiality clauses
• Segregation of client data to prevent conflicts or cross-contamination
• Incident response procedures if AI vendor suffers data breach
• Client consent frameworks for AI use in their matters

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Client confidentiality is fundamental to legal practice. AI vendors that process client data create confidentiality risks—especially if training models on your documents or sharing infrastructure with competitors.

Quick win: Draft a client AI use disclosure template explaining how and when you use AI, what safeguards exist, and how confidentiality is maintained.

Do you have a plan to drive adoption of AI tools across fee earners?

Consider:

• Partner sponsorship for AI initiatives
• Communication strategy addressing lawyer concerns (job security, competence)
• Pilot project approach starting with willing adopters
• Incentives aligned with AI use (efficiency gains recognized in billing/reviews)
• Feedback loops to improve AI tools based on lawyer input

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Lawyers value autonomy and expertise. AI adoption requires demonstrating value and building trust—not mandating use without engagement. Early adopters become champions.

Quick win: Identify 2-3 lawyer champions (associates or partners) who will advocate for AI and run your first pilot in their practice area.

Have you allocated realistic budget for AI adoption in legal practice?

Consider:

• Initial software licensing (per-user or consumption-based pricing)
• Ongoing operational costs (subscriptions, API usage, support)
• Internal resource time (project management, training, validation)
• External expertise (consultants, integration partners, legal tech specialists)
• Productivity loss during transition period (learning curve)

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: Legal AI costs extend beyond licensing—training, integration, and change management require investment. Underfunding leads to failed pilots and frustrated users.

Quick win: Model a 12-month total cost of ownership for one priority use case (e.g., AI contract review for M&A due diligence), including hidden costs like lawyer training time.

Do you have KPIs defined to measure AI impact on legal operations?

Consider:

• Baseline metrics before AI implementation (research hours, document review time, error rates)
• Clear success criteria tied to business outcomes
• Fee earner time tracking for AI-assisted vs. manual work
• Quality metrics (accuracy, client satisfaction, error reduction)
• ROI calculation including margin improvement and capacity gains

Your Rating: ☐ Strong ✓ | ☐ Partial ~ | ☐ Gap ✗

Why it matters: In legal, AI success isn’t just time savings—it’s quality, risk reduction, and capacity growth. Clear KPIs ensure AI delivers measurable improvements in margin and client value.

Quick win: Define 3 KPIs for your first AI pilot that directly tie to financial outcomes (e.g., 30% reduction in due diligence hours, 20% increase in associate billable capacity, 50% faster contract review turnaround).