AI Intake and Triage: A Guide for In-House Legal Teams
AI Intake and Triage: A Guide for In-House Legal Teams
If you’re like most in-house teams, 30–50% of requests are repeatable questions and contracts—and yet attorneys still spend hours every week triaging email. AI-powered intake and triage can reclaim that time while improving consistency, auditability, and business satisfaction.
This guide is for GCs, legal ops leaders, and tech-forward buyers who want a practical path from shared inbox chaos to a scalable, rightsized workflow grounded in policy and playbooks.
What “Good” AI Intake Looks Like
Effective intake is more than a form—it is a living system that captures context, applies policy-backed decisions, and routes with clarity. A mature AI intake and triage layer should:
- Standardize capture: Dynamic forms that adapt to request type (NDA, vendor paper, marketing review, privacy inquiry).
- Classify reliably: Natural language classification to identify matter type, risk level, and required approvals.
- Enforce playbooks: Auto-apply positions (e.g., data processing standards, indemnity thresholds) and flag exceptions.
- Route and notify: Assign to the right queue with service-level targets (SLAs) and clear ownership.
- Respond and deflect: Suggest FAQs or auto-resolve low-risk requests where policy allows.
- Log decisions: Maintain an auditable trail of reasoning, versions, and approvals.
On platforms like Sandstone, layered data, modular workflows, and approvals compound over time: every intake strengthens the knowledge base and improves the next decision.
Where to Start: Narrow Scope, High Value
Start with a bounded, high-volume workflow. Common candidates include:
- NDAs and low-risk vendor agreements
- Marketing/comms review
- Privacy/data requests (e.g., DPIA pre-checks)
- Procurement intake and standard T&Cs
Example: NDA self-service
- Requestor selects counterparty and purpose; AI checks for existing NDA.
- If no prior agreement, AI classifies risk and proposes the standard template.
- For mutual standard NDAs with no redlines, AI issues signature-ready docs and logs the matter.
- Exceptions (e.g., third-party paper, non-standard terms) are routed with a summary and risk notes.
This pattern demonstrates measurable wins without touching complex negotiations.
Evaluation Criteria: Accuracy, Safety, and Fit
When assessing tools and approaches, use criteria that map to in-house realities:
- Reliability: Classification accuracy; false positive/negative rates on routing and risk flags.
- Security: Data residency options, encryption, access controls, and audit logs.
- Privacy: PII handling, data retention, vendor subprocessors, and model training boundaries.
- Policy fidelity: Ability to encode playbooks (fallback positions, escalation thresholds, clauses).
- Integration: Email, Slack/Teams, eSignature, CLM, ticketing (Jira/ServiceNow), CRM (Salesforce).
- Governance: Role-based permissions, legal hold, approval flows, and change history.
- UX: Requestor-friendly intake; lawyer-friendly review and override.
- Cost: Clear pricing for users, automations, and volume; measurable time-to-value.
Governance tip: Establish a “human-in-the-loop” checkpoint for exceptions, and publish a runbook so business users know what is automated and when legal steps in.
Implementation: A 4-Week Pilot Plan
Week 1: Frame the problem
- Baseline metrics: request volume, cycle time, top request types, percent escalations.
- Define scope: 1–2 use cases, approved templates, fallback positions, and SLAs.
Week 2: Configure the knowledge layer
- Load templates, clause positions, approval matrices, and routing rules.
- Tag sensitive data and set redaction/permission policies.
Week 3: Integrate and test
- Connect intake channels (email alias, Slack/Teams bot, portal) and eSignature.
- Run shadow mode: AI classifies and drafts, humans review and adjust.
Week 4: Go live and measure
- Turn on auto-resolution for low-risk paths.
- Track cycle time, deflection rate, and requester satisfaction; iterate weekly.
On Sandstone, an intake agent can classify matters, apply playbooks, generate tasks, draft responses, trigger eSignature, and post summaries back to Slack—while preserving a clean audit trail.
Metrics That Matter
Pick three metrics to prove value early:
- Time to first response: Target minutes, not days.
- Auto-resolution/deflection rate: Portion of requests resolved without attorney time.
- Cycle time by request type: From intake to signature or resolution.
- Escalation ratio: Exceptions vs. standard path; aim to reduce over time as playbooks mature.
- Requestor CSAT: Simple thumbs up/down with optional comment.
Tie these to business outcomes: faster vendor onboarding, quicker campaigns, fewer policy breaches, and improved forecasting.
Risks and How to Mitigate Them
- Hallucinations or overreach: Lock automations to approved templates and positions; require human review on non-standard paper.
- Data leakage: Apply least-privilege access, redact PII in prompts, and avoid training on sensitive content.
- Change management: Communicate the new front door for legal, publish SLAs, and make escalation paths obvious.
- Drift from policy: Version playbooks, require approvals for changes, and audit rule modifications monthly.
Actionable Next Step
Run a two-week NDA intake pilot:
- Publish a single intake link in Slack/Teams and email.
- Enable AI classification and standard-template issuance for mutual NDAs.
- Require attorney review only when counterparty paper or non-standard terms appear.
- Measure time saved and deflection rate; expand to vendor T&Cs if targets are met.
Closing: Build on Layers, Not Heroics
Sustainable speed comes from layered knowledge—not individual heroics. With an AI-powered intake and triage foundation, legal becomes connective tissue for the business: consistent decisions, clear workflows, and a living record of why choices were made. That’s how trust compounds, cycle times shrink, and legal moves from reactive support to a proactive operating system for growth.