How White & Case Can Transform Global M&A and Arbitration Legal Services with Agentic AI

Agentic AI in legal services is moving from experimentation to execution. For global firms handling cross-border M&A and international arbitration, the value isn’t in flashy demos. It’s in compressing cycle times, improving consistency, and producing more defensible work product across document-heavy matters.

White & Case is well positioned for this shift. The firm’s matters already demand disciplined process, deep subject-matter expertise, and rigorous governance across jurisdictions. Agentic AI in legal services can strengthen that operating model by taking on repeatable, multi-step work such as document triage, clause extraction, chronology building, and first-pass drafting, while keeping lawyers in control of judgment calls and final outputs.

This guide lays out a practical blueprint: what agentic AI is, where it fits in global M&A and arbitration workflows, how to govern it safely, and how to implement it in 90 days without betting the practice on a single monolithic system.

What “Agentic AI” Means for a Global Law Firm

Definition (in plain English)

Agentic AI in legal services refers to AI systems that can plan and execute multi-step legal workflows, using tools like document search, extraction, drafting, and verification, while iterating toward a defined output. It’s not a one-shot “chatbot answer.” It’s closer to a digital team member that can follow a playbook.

A useful way to draw the line:

• Chatbot AI answers prompts in isolation, often based on whatever context you paste in.

• Agentic AI in legal services runs a workflow: it breaks the task into steps, pulls the right sources, produces structured deliverables, and flags uncertainty for review.

Just as important, the boundaries are clear. Agents assist. Lawyers decide, escalate, and sign off. In high-stakes M&A and arbitration, that distinction is the difference between productivity and unacceptable risk.

Featured snippet: What is agentic AI in legal services?

Core capabilities that matter in legal work

Most “AI in law” conversations stay abstract. In practice, agentic workflows for law firms come down to a few capabilities that map directly onto day-to-day execution.

1. Task planning and decomposition

An agent can take a broad request like “prepare a first-pass diligence summary” and break it into steps: identify document types, extract defined clauses, detect deviations, score severity, and assemble a memo draft.

1. Tool use across the matter stack

Modern legal work is distributed across systems. Effective agentic AI in legal services needs to interact with tools, not just text. Common tool actions include:

• Retrieval: search within a data room export, DMS, or matter workspace

• Extraction: pull defined fields from contracts, pleadings, or exhibits

• Drafting: generate structured sections aligned to a firm template

• Redlining: propose edits against playbook language

• Verification: cross-check quotes, defined terms, and citations

1. Memory and context management

Legal matters are context-dense. Agents need “matter memory” that stays isolated to the engagement. That includes deal terms, party names, definitions, jurisdictional constraints, and the evolving set of documents that become the record.

1. Collaboration patterns (multi-agent setups)

The most reliable deployments rarely rely on a single agent. A common pattern is a small team:

• Research agent to find and quote sources

• Drafting agent to assemble sections in firm format

• QA agent to validate citations, defined terms, and inconsistencies

• Escalation agent to flag uncertainty and route questions

That structure aligns well with how legal teams already work, and it is a practical way to reduce risk while scaling.

Why now (what changed in 2024–2026)

Three shifts are driving adoption of agentic AI in legal services across global firms.

First, document understanding and retrieval are materially better. Agents can handle larger sets of PDFs, scans, and mixed-quality documents, which is essential for both diligence and arbitration records.

Second, governance patterns are maturing. Enterprises are implementing permissioning, audit logs, sandboxed environments, and tighter controls over data retention and model usage. For legal AI governance and risk, those controls are not “nice-to-haves.” They are prerequisites.

Third, client expectations are changing. Corporate legal departments are increasingly focused on predictability, responsiveness, and cost transparency. The firms that can deliver faster first drafts, cleaner work product, and more consistent outputs will have an advantage, especially in competitive panel reviews.

Where White & Case Wins: High-Value Use Cases in Global M&A

Global M&A is one of the clearest fits for agentic AI in legal services because the workflow is structured, document-heavy, and measurable. The opportunity isn’t to replace judgment. It’s to remove friction from first-pass review and reporting so deal teams can focus on risk posture and negotiation strategy.

Pre-signing: diligence acceleration without quality loss

The highest ROI use case is still the simplest: accelerate the first pass while increasing consistency.

A practical agent workflow looks like this:

1. Ingest the data room export

The agent indexes the document set, identifies file types, and assigns categories (corporate, commercial, IP, privacy, employment, real estate, disputes, etc.).

1. Classify and prioritize

Not all documents deserve equal attention. The agent can triage likely high-impact agreements (revenue-driving contracts, exclusivity, change of control, data processing, sanctions exposure) and flag what needs attorney review first.

1. Extract key clauses and fields

For AI for M&A due diligence, extraction targets should be defined upfront. Examples include:

• Change of control and assignment

• Term, renewal, termination, and notice

• Indemnities, limitations of liability, caps, baskets

• Governing law, forum, arbitration clauses

• Data protection and cross-border transfer language

• Audit rights and security requirements

• Most favored nation, exclusivity, non-competes

1. Flag risks and deviations

The agent compares extracted language to a playbook or standard positions, then identifies deviations. This is where contract review automation becomes more than summarization: it becomes structured variance detection.

1. Produce a diligence memo draft

The output should be a memo draft that is easy to review: issues list by severity, citations to source documents, and a clear explanation of what triggered each flag.

What changes for lawyers is immediate:

• Less time scanning for “where is the clause”

• More time deciding whether the clause matters, and what leverage exists to fix it

• Faster escalation to specialists when the issue is jurisdiction-specific

Deliverables that work well in practice:

• Issues list with severity scoring and reason codes

• Clause library with extracted snippets and references

• Jurisdiction-specific risk notes for cross-border matters

Contract analysis and redlining at scale

Once diligence extraction is stable, the next win is scaling playbook-aligned redlines across high-volume agreements.

In many deals, the pain isn’t one SPA. It’s the surrounding universe: NDAs, MSAs, supplier agreements, licensing, SOWs, and local-country templates that vary widely. Agentic workflows for law firms can handle the repetitive parts:

• Triage documents by type and risk category

• Apply playbook guidance for initial redlines

• Generate a brief explanation for each proposed change, tied to the playbook rationale

• Track version control: which edits were accepted, rejected, or modified, and by whom

This is also where auditability matters. A credible workflow should capture:

• Source document version

• The playbook rule invoked

• The proposed change

• The final attorney decision and reason

That audit trail is part of defensibility, and it helps future matters improve.

Regulatory + cross-border complexity support

Global deals often hinge on regulatory coordination: FDI regimes, sanctions exposure, antitrust filings, sector-specific licensing, and data localization constraints. An agent can’t replace specialists, but it can compress the time it takes to get organized and ask the right questions.

A useful pattern is the “jurisdiction packet”:

• The agent reads the deal overview, parties, geographies, and target activities

• It generates a multi-jurisdiction checklist tailored to the matter

• It flags potential red zones: sanctioned countries, sensitive sectors, government touchpoints, data transfer constraints

• It routes focused questions to the right internal specialists

Examples of red flags an agent can reliably surface for review:

• Change-of-control clauses that trigger consent requirements

• Licensing terms tied to territorial restrictions

• Data transfer language that conflicts with cross-border operational needs

• Anti-assignment clauses in revenue-critical contracts

• Sanctions-related representations that are incomplete or inconsistent

This kind of support improves speed and consistency without asking anyone to outsource judgment to a model.

Post-signing: integration, obligations, and compliance tracking

The deal doesn’t end at signing. For many clients, the highest ongoing pain is obligations management.

Agentic AI in legal services can build an obligations register directly from deal documents:

• Identify obligations, deadlines, and conditions precedent/subsequent

• Assign owners and dependencies

• Generate reminders and status prompts

• Produce a client-facing view of what is due and what is risky

Done well, this becomes a service differentiator: clients feel the value in the weeks after signing, not only during the closing sprint.

Where White & Case Wins: Agentic AI in International Arbitration

International arbitration is a natural match for agentic AI in legal services because the work is record-intensive, time-sensitive, and shaped by themes, chronologies, and evidentiary discipline. The best deployments focus on organizing and validating the record so counsel can spend more time on strategy.

Early case assessment and strategy formation

The opening weeks of an arbitration matter often involve absorbing pleadings, key exhibits, and background documentation, then forming a coherent theory of the case.

A strong agent workflow:

1. Intake pleadings and core exhibits

The agent indexes claims, defenses, definitions, and referenced evidence.

1. Build a timeline

It extracts dates, events, and participants, with citations to the record.

1. Create an issues map

The agent identifies the legal and factual issues, then links each to supporting and opposing evidence.

1. Produce a strengths/weaknesses matrix

This is not a final strategy memo. It’s a structured first pass that helps counsel see where evidence is strong, thin, or contradictory.

1. Generate a “questions to investigate” list

The most valuable output is often a list of gaps: missing documents, unclear factual disputes, and suggested witness questions to resolve ambiguity.

This approach supports AI in international arbitration without turning the agent into a decision-maker.

Document-heavy arbitration: search, chronologies, and exhibit prep

For many teams, the daily grind is searching the record and maintaining chronologies as new documents arrive.

Agentic workflows for law firms can automate:

• Chronology updates with citations and excerpted quotes

• Entity and custodian tracking (who said what, when)

• Exhibit bundle suggestions based on relevance to issues

• Conflict detection when two documents describe the same event differently

If the record is multilingual, translation-aware summarization can be helpful, but it should be constrained. A good workflow preserves original-language excerpts, flags where translation was applied, and routes high-impact passages for human review.

This is also where eDiscovery and document analysis AI can be operationalized in a way that arbitration teams actually use: not as a replacement for review platforms, but as a layer that produces case-ready structures.

Witness and hearing preparation

Hearing preparation often requires assembling a coherent witness dossier: prior statements, inconsistent details, key dates, and exhibit anchors.

An agent can:

• Build witness packs tied to the record

• Identify potential inconsistencies across statements and documents

• Draft question outlines that reference specific exhibits

• Generate a hearing day playbook: themes, exhibit sequence, and fallback exhibits

This is the arbitration-adapted version of AI-assisted deposition and witness prep. The key is that the output is structured, reviewable, and anchored in sources.

Drafting submissions with stronger consistency and citation discipline

Drafting is where agentic AI in legal services can provide immediate time savings, especially for sections that are heavy on record citations and internal consistency.

Good targets include:

• Procedural history

• Factual background

• Summaries of witness statements tied to exhibits

• Record-supported chronologies embedded into narrative form

A practical safeguard is the “QA agent” pattern:

• Verify all quotes match the cited excerpt

• Confirm defined terms are used consistently

• Flag internal contradictions (dates, names, amounts)

• Identify missing citations for factual assertions

The goal is not perfect drafting without humans. It’s fewer preventable mistakes and faster iteration toward partner-ready submissions.

Top arbitration tasks suited for agentic AI (with attorney review)

• Building and maintaining chronologies with citations

• Issue mapping tied to pleadings and exhibits

• Witness dossier creation and inconsistency spotting

• First drafts of procedural history and factual background

• Record search and structured summaries for hearing prep

The Operating Model: How Agentic AI Fits into White & Case Workflows

The difference between a pilot that quietly dies and a program that scales is the operating model. Agentic AI in legal services must fit the way matters run: intake, staffing, review gates, and knowledge reuse across offices.

Matter intake + scoping

Start by classifying tasks into two buckets:

Agent-ready tasks (high leverage, lower judgment)

• Document classification and tagging

• Clause extraction and comparison to playbooks

• Chronology building

• First drafts of record-supported sections

• Routing and triage of internal requests

Lawyer-only tasks (judgment-heavy, high risk)

• Legal conclusions and advice

• Final negotiation positions

• Privilege determinations where the facts are unclear

• Final filings and submissions

Then define success metrics before building anything. For legal knowledge management AI and automation to be credible, it must be measurable. Common metrics include:

• Cycle time to first draft

• Percentage of documents triaged correctly

• Citation accuracy rate

• Rework percentage after partner review

• Attorney satisfaction by role (associate, counsel, partner)

• Client responsiveness indicators

Finally, document the matter constraints: jurisdictions, confidentiality requirements, document rules, and any limitations on where data can be processed.

Human-in-the-loop checkpoints (non-negotiable)

In BigLaw, “human-in-the-loop” can’t be a slogan. It has to be a workflow design.

Common review gates:

• Extraction verification: confirm key fields and clauses were correctly captured

• Risk classification review: confirm severity and issue type

• Legal conclusion gate: lawyers decide what the issue means and what to do

• Final drafting approval: lawyers validate accuracy, tone, and strategy

Escalation rules make the workflow safer and faster. Examples:

• If the agent’s confidence is below a threshold, it must ask a clarifying question

• If two sources conflict, it must flag the conflict rather than choose

• If a document appears privileged or sensitive, it must route to designated reviewers

• If the output requires legal advice, the agent must stop at a draft and request approval

Knowledge management upgrades

Agentic AI in legal services becomes dramatically more valuable when paired with better knowledge reuse. That requires a clean separation:

• Matter memory: isolated to the engagement, accessible only to the team

• Firm memory: reusable playbooks, templates, clauses, and argument structures, governed by KM and risk controls

A practical approach is to convert completed work product into reusable assets:

• Clause libraries with approved fallback language

• Diligence issue templates with severity scoring guidance

• Arbitration issue maps and chronology frameworks

• Specialist checklists for common cross-border risks

Close the loop with retrospectives. After a matter closes, review:

• Where the agent was helpful

• Where it produced noise

• Which prompts and workflows need refinement

• Which outputs should be standardized firmwide

That iterative approach reflects how high-performing AI programs scale: narrow workflows, validated sequentially, then expanded.

Collaboration across global offices

White & Case matters often operate across time zones. Agentic workflows for law firms can strengthen follow-the-sun execution by maintaining structured context:

• Standard templates with localized variations

• Consistent tagging and naming conventions

• Hand-off summaries that include: what changed, what’s pending, what needs approval

• A shared matter workspace where the agent’s outputs are traceable and reviewable

The result is fewer missed context transitions, which is a hidden source of cost and risk in global matters.

Governance, Risk, and Ethics (How to Do This Safely)

Legal AI governance and risk is the gating factor for adoption. Firms don’t need theoretical principles. They need concrete controls: confidentiality protections, accuracy safeguards, auditability, and model risk management.

Confidentiality and privilege protection

Confidential data handling in legal AI starts with boundaries:

• Matter-level access control: only the assigned team can access matter data

• Encryption in transit and at rest

• Clear retention rules: what gets stored, for how long, and why

• Controls around model training: sensitive client data should not be used to train shared models

• Approved environments: restrict use to governed tools rather than public consumer interfaces

A “no copy/paste into public tools” policy is a baseline, but policies alone don’t solve the problem. The workflow should make compliance easier than workarounds by offering secure, integrated tools that meet attorney needs.

Accuracy, hallucinations, and defensibility

The biggest practical concern with agentic AI in legal services is not that it will be occasionally wrong. It’s that it can be wrong confidently, and that errors can slip into submissions under time pressure.

The strongest safeguard is retrieval-first design:

• Require every factual assertion to be anchored to a cited source

• Prefer quote-and-cite outputs over paraphrase where possible

• Flag missing sources instead of “best-guess” completion

Add automated verification:

• Quote checks: confirm the excerpt matches the source text

• Cross-document consistency checks: names, dates, defined terms, amounts

• Duplicate detection: ensure the same issue isn’t reported multiple times

Finally, maintain audit logs:

• What the agent produced

• What sources it used

• What edits were made by humans

• Who approved the final version

In high-stakes matters, defensibility is a feature, not overhead.

Regulatory and professional responsibility considerations

Professional responsibility obligations vary by jurisdiction, but several themes are consistent:

• Competence includes understanding the benefits and risks of tools used in practice

• Supervision remains essential: attorneys must supervise work product, even when assisted by automation

• Disclosure norms may apply in certain contexts; policies should be clear on when AI assistance must be disclosed and how

Bias and fairness risks also matter. Summarization and issue spotting can unintentionally skew emphasis. Mitigations include:

• Using structured templates that force balanced reporting

• Requiring evidence links for issue framing

• Having reviewers confirm that countervailing facts were not omitted

Model risk management for legal AI

Model risk management is how agentic AI in legal services becomes sustainable.

Key practices:

• Red teaming: test adversarial documents, privilege traps, and edge cases

• Backtesting: run workflows against closed matters and compare outputs to the final work product

• Monitoring: track error types, near-misses, and where attorneys override the agent

• Incident response: define what happens if an output is wrong, sensitive data is mishandled, or logs reveal misuse

Governance checklist for agentic AI in law firms

• Matter-level isolation and role-based permissions

• Encryption, retention policies, and controlled access paths

• No training on client data without explicit approval and safeguards

• Retrieval-first outputs with source citations and quote checks

• Human review gates for extraction, risk classification, and final drafting

• Audit logs capturing sources, edits, and approvals

• Backtesting on closed matters and ongoing monitoring

• Escalation rules for uncertainty, conflicts, and sensitive content

Implementation Roadmap for White & Case (90 Days to Scale)

A credible plan for agentic AI in legal services must deliver visible outcomes quickly while protecting quality. The most reliable approach is to start narrow, validate against prior matters, and scale with governance.

Phase 1 (Weeks 1–4): Pilot design

Pick 2–3 narrow workflows with clear inputs and outputs. Strong candidates:

• NDA triage and playbook redlines

• Diligence clause extraction with severity scoring

• Arbitration chronology builder with citations

Define the gold standard:

• What does a “good” output look like?

• What must be included every time?

• What is unacceptable?

Create an evaluation rubric. For example:

• Citation accuracy

• Completeness against required fields

• Noise rate (false positives)

• Time saved vs. baseline

• Reviewer effort (minutes to approve)

Then choose the integration approach: DMS, document repositories, redlining tools, matter workspaces, and permissions.

Phase 2 (Weeks 5–8): Build + validate

Build the core agent flows with guardrails:

• Structured prompts and templates aligned to practice requirements

• Tool access limited to approved repositories

• Escalation logic for uncertainty and conflicts

Validate via parallel testing:

• Run the agent on closed matters

• Compare outputs to final memos, chronologies, or redlines

• Measure where it matches, where it misses, and where it invents

This backtesting step is where trust is earned. It also produces the data needed for partners and risk teams to support rollout.

Phase 3 (Weeks 9–12): Rollout + training

Rollout should be role-based:

• Partners: how to review faster, what to trust, what not to delegate

• Associates: how to use outputs as a starting point and how to correct issues

• KM: how to curate playbooks and templates

• Legal ops/IT: how to manage permissions, logs, and support

Adoption tactics that work:

• Standard templates embedded in the workflow

• Office hours and real matter support

• Champion users in each practice group

• Simple dashboards showing time saved and quality metrics

Set up governance structures early: who owns playbooks, who owns risk controls, and how changes are approved.

90-day roadmap to pilot and scale agentic AI

1. Select 2–3 narrow workflows with measurable outputs

2. Define templates, review gates, and evaluation rubrics

3. Build agent flows with permissions, logging, and retrieval-first design

4. Backtest on closed matters and quantify accuracy and time saved

5. Refine prompts, escalation rules, and QA checks based on results

6. Roll out to a limited group with role-based training

7. Track KPIs, run retrospectives, and expand to the next workflows

What to measure (business + quality KPIs)

A balanced scorecard makes agentic AI in legal services real:

Business outcomes

• Time-to-first-draft reduction

• Diligence cycle time reduction

• Faster chronology updates and exhibit prep turnaround

• Reduced manual search time

Quality outcomes

• Citation accuracy rate

• Extraction accuracy (field-level)

• Noise rate in issue spotting

• Rework percentage after partner review

Client-facing outcomes

• Responsiveness

• Predictability of timelines

• Consistency across jurisdictions and teams

• Better transparency into obligations and next steps

Practical Examples (What the Work Product Looks Like)

Agentic AI in legal services succeeds when outputs are not just “helpful text,” but structured deliverables that fit into how teams already review and file work product.

Example deliverables for M&A

Diligence risk memo with severity scoring

A structured memo draft that groups issues by category, assigns severity, and includes citations for each flagged clause. The best versions also include a short “why it matters” explanation and suggested follow-ups.

Clause deviation report (playbook vs. document reality)

A report that shows:

• Extracted clause text

• Playbook position

• Detected deviation

• Suggested redline

• Required reviewer sign-off

Obligations register + tracker

A post-signing deliverable listing obligations, deadlines, and owners, updated as documents change and approvals are recorded.

Example deliverables for arbitration

Chronology with citations

An evolving timeline where each entry links to the record and includes key excerpts.

Issues matrix tied to the record

A map of claims/defenses to supporting and opposing evidence, highlighting gaps and contradictions.

Witness prep pack and hearing outline

A structured witness dossier and question outline anchored to exhibits, with potential inconsistencies flagged for counsel review.

Before vs. after: what changes in daily work

Before agentic AI in legal services:

• Associates spend hours searching, extracting, and formatting

• Chronologies drift and require manual reconciliation

• First drafts are slow, and citations are vulnerable under pressure

• Cross-office handoffs lose context

After agentic AI in legal services:

• First-pass review becomes structured and faster

• Lawyers spend more time on strategy and negotiation posture

• Drafting starts from a record-anchored skeleton, not a blank page

• QA catches preventable inconsistencies earlier

• Matter context is easier to hand off across teams and time zones

Choosing the Right Agentic AI Platform (Evaluation Criteria)

Platform choice matters because it determines whether workflows can be governed, audited, and scaled. For BigLaw, the evaluation criteria should start with risk controls, not shiny features.

Must-haves for BigLaw

Permissions and matter-level isolation

Agentic AI in legal services must respect ethical walls, client confidentiality, and need-to-know constraints.

Citations, provenance, and audit logging

If a system can’t show where an output came from and who approved it, it won’t survive real-world scrutiny.

Enterprise deployment controls

Many firms will require strong hosting options and security controls aligned with client expectations and internal policy.

Integration with the existing stack

The platform must work with document management, redlining tools, and matter workspaces. If it creates parallel processes, adoption will stall.

Build vs. buy decision framework

Building internally can make sense when:

• The firm has strong engineering and legal ops support

• Workflows are highly bespoke

• There is appetite for ongoing maintenance and evaluation

Buying or partnering makes sense when:

• Speed to deployment matters

• Governance and enterprise controls are required immediately

• The firm wants repeatable, configurable workflows without bespoke engineering for every use case

Total cost is not just licensing or development. It includes:

• Evaluation and backtesting time

• Ongoing monitoring and improvements

• Governance and policy work

• Training and support

• Change management across offices

Vendor landscape (keep neutral, stay practical)

The market includes categories such as:

• Workflow orchestration platforms for building agentic workflows for law firms

• Legal research tools with LLM agents for legal research

• Contract review automation tools focused on extraction and redlines

• eDiscovery and document analysis AI tools for large record sets

The right approach is fit-for-purpose testing: pick a workflow, define success, and evaluate tools against the rubric using real (appropriately controlled) matter data.

Conclusion: A Transformation That Clients Will Feel

Agentic AI in legal services is not a branding exercise. For White & Case, it can be a measurable transformation across two of the firm’s most demanding areas: global M&A and international arbitration.

Done right, the impact is tangible:

• Faster, more consistent service delivery across offices and time zones

• Stronger risk spotting and more defensible work product through citations and audit trails

• Better collaboration and knowledge reuse, turning each matter into a foundation for the next

The most pragmatic starting point is also the most effective: start with two pilots that map cleanly to daily work, prove accuracy on closed matters, and scale with governance.

Start with 2 pilots: diligence triage for M&A and an arbitration chronology builder. Validate them, instrument them, and expand from there.

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