AI for Transactional vs Litigation Practices: Different Stacks

Transactional and litigation practices have fundamentally different work patterns, document types, and AI deployment needs. The stack that works for a contract-focused transactional practice differs significantly from a document-discovery-heavy litigation practice.

Here's the operator playbook for each.

Transactional practice AI stack

Core tools:

• Spellbook (contract drafting and review inside Word) — solo to mid-size
• Kira Systems or Luminance — large M&A and due diligence
• CLM platform with AI (Ironclad, DocuSign CLM) — for in-house teams
• Casetext CoCounsel or Harvey — general legal AI for research and drafting

Workflow patterns:

• Heavy contract drafting (NDAs, SaaS agreements, employment, vendor contracts, M&A)
• Due diligence at scale
• Negotiation prep
• Closing checklist management
• Post-closing transition

Time compression:

• Contract review: 50-70% reduction
• Drafting: 40-50% reduction
• Due diligence: 50-60% reduction
• Negotiation prep: 30-40% reduction

Pricing model adaptation:

• Value-based and capped fees for routine work
• Fixed fees for standard agreements
• Hourly for negotiation and strategy

Litigation practice AI stack

Core tools:

• Harvey or Casetext CoCounsel — research, drafting, document review
• Relativity with aiR or DISCO — eDiscovery
• Specialized tools for trial prep (Smokeball Trial, TrialDirector)
• Westlaw Precision or Lexis+ AI — legal research

Workflow patterns:

• eDiscovery and document review
• Motion drafting
• Deposition preparation
• Trial preparation
• Legal research at depth

Time compression:

• eDiscovery: 50-70% reduction
• Motion drafting: 40-50% reduction
• Deposition prep: 50-60% reduction
• Legal research: 50-60% reduction

Pricing model adaptation:

• Hourly remains primary for trial work
• Value-based fees for routine matters
• Contingency model unaffected by AI billing rules
• ABA Formal Opinion 512 applies regardless

The shared foundation

Both practice types benefit from:

• General legal AI (Casetext CoCounsel or Harvey)
• Practice management with AI features (Clio, PracticePanther, NetDocuments)
• Operations AI (intake, conflicts, billing narratives)
• Compliance and ethics infrastructure (AI policy, training, supervision)

Most firms have both practice areas. The shared foundation supports both; the specialized tools differ.

Cost comparison

Transactional-focused firm (solo to small):

• Practice management: $69-99/month
• Spellbook: $100-150/month
• General legal AI (CoCounsel or similar): $200-400/month
• General AI: $25/month
• Total: ~$400-700/attorney/month

Litigation-focused firm (solo to small):

• Practice management: $69-99/month
• General legal AI (CoCounsel or similar): $200-400/month
• eDiscovery (per-matter): variable
• Specialized litigation tools: $100-300/month
• General AI: $25/month
• Total: ~$400-800/attorney/month plus per-matter eDiscovery

Mid-size and larger firms with both practices:

• All of the above plus specialized tools per practice area
• Total: $800-2000/attorney/month all-in

Where the practices diverge

Transactional:

• Document creation and review dominates
• Contract-specific AI is the highest-leverage tool
• Multi-language deals benefit from specialized tools
• Closing logistics and CLM platforms matter for in-house

Litigation:

• Document review (discovery) often the largest hours sink
• eDiscovery platform is essential at scale
• Brief drafting and legal research are major workflows
• Trial prep and witness work require specialized tools

What we deploy by practice type

For transactional-focused firms:

• Spellbook (or Kira if AmLaw scale) as primary contract AI
• CoCounsel or Harvey for research and broader drafting
• Workflow integration with document management
• Pricing model adaptation

For litigation-focused firms:

• Harvey or CoCounsel as primary research/drafting AI
• Relativity or DISCO for eDiscovery (or per-matter)
• Specialized trial prep tools
• Verification protocols for citations

For firms with both practices:

• Both stacks, with shared foundation
• Practice-area-specific training
• Cross-practice learning sessions

Bottom line

Transactional and litigation practices need different AI stacks because the work patterns differ fundamentally. The shared foundation (practice management, general legal AI, operations) supports both. The specialized tools (Spellbook/Kira for transactional, Relativity/DISCO for litigation) differ by practice.

Firms with both practices need both stacks. The total cost is higher but the practice-area-specific gains justify it.

Pick the right specialized tools for each practice. Build the shared foundation. Train attorneys on practice-area patterns. The competitive advantage comes from operator discipline more than tool choice.