AI Discovery Review Strategy: Beyond eDiscovery Platforms

Discovery review is more than the eDiscovery platform you choose. Strategy decisions drive the cost, timeline, and defensibility: which custodians, which search terms, which TAR protocols, how much sampling. AI is changing each of these decisions.

Here's the operator playbook.

The discovery review strategy framework

Five strategic decisions:

• Custodian selection — Who's in scope
• Search and culling — How to narrow the universe
• Review methodology — Linear, TAR, or hybrid
• Quality control — Sampling and validation
• Production approach — Format, redactions, privilege log

AI affects each.

Custodian selection

Traditional approach:

• Identify likely document custodians manually
• Negotiate scope with opposing counsel
• Add custodians as case develops

AI-enabled approach:

• AI analyzes communication patterns to identify relevant custodians
• Pattern detection across organization
• Suggests additional custodians based on document content
• More defensible scope discussions

For complex matters, AI custodian analysis can identify key players that manual identification misses. Particularly valuable in matters involving large organizations.

Search term and culling strategy

Traditional approach:

• Negotiate search terms with opposing counsel
• Apply terms across document universe
• Manual review of results

AI-enabled approach:

• AI suggests search terms based on case facts and initial documents
• Pattern detection finds terms that distinguish relevant from irrelevant
• Iterative refinement of search strategy
• Better-defended search term lists

Search terms are increasingly suggested by AI analysis rather than pure attorney judgment. The defensibility improves.

Review methodology

Three options, with AI playing different roles in each:

Linear review:

• Every document reviewed by attorney
• AI assists individual reviewers with categorization suggestions
• Use for smaller matters or sensitive content

TAR (Technology-Assisted Review):

• Attorneys train AI on seed set
• AI categorizes full population
• Attorneys review AI-categorized batches
• Standard for matters above 100k documents

Hybrid:

• Linear review for high-risk categories (privilege, key witnesses)
• TAR for routine categories
• Most common approach at AmLaw firms in 2026

The choice depends on document volume, sensitivity, and case complexity.

Quality control

Sampling protocols:

• Statistical sampling of categorized documents
• Validation of AI categorization accuracy
• Privilege check sampling
• Production sampling

Documentation:

• Process documentation throughout review
• Sample results
• Categorization decisions
• Final production validation

Defensibility under Da Silva Moore and subsequent cases requires structured QC.

What changes when AI is well-deployed

Cost reduction:

• 50-70% lower review hours and cost
• Matters that were economically unviable become viable
• Smaller firms can handle bigger matters

Timeline compression:

• 40-60% faster review timelines
• Faster case progression
• More flexible response to motion practice

Quality improvement:

• AI catches subtle issues humans miss
• Consistent application of categorization
• Better-documented process

The verification discipline

For every AI-assisted discovery review:

• Seed set training carefully designed
• Sample validation of AI categorization
• Privilege review with attorney verification
• Production validation before sending
• Documentation of every protocol decision

Skip any of these and defensibility weakens. Mata v. Avianca-style errors in production are catastrophic.

Ethics and Federal Rules

Discovery review AI touches:

• Federal Rule 26 (cooperation obligations)
• Rule 34 (production scope)
• Privilege and work product protection
• Sanctions under Rule 37
• ABA Formal Opinion 512

The attorney is responsible for production. AI accelerates the work; attorney signs the production.

What we deploy

For litigation practices working with us:

• Platform selection (Relativity, DISCO, Reveal)
• Workflow design for AI-assisted review
• TAR protocol development
• Quality control framework
• Compliance and defensibility documentation
• Attorney training

Cost: scales with matter complexity. Per-matter setup typically $20-80k; ongoing per-matter pricing varies.

Bottom line

Discovery review strategy AI in 2026 is essential at any meaningful matter scale. The cost reduction (50-70%) and timeline compression (40-60%) make matters viable that wouldn't be otherwise.

The strategic decisions — custodian selection, search terms, review methodology, quality control — all benefit from AI. The execution requires structured discipline.

Litigators not running AI-augmented discovery in 2026 are operating against AI-equipped competitors who deliver better discovery at lower cost. The competitive gap compounds with every major matter.