AI for Legal Ops Teams: 3 Steps to Build an AI-Ready Intake

Turn chaotic requests into structured, self-service workflows that accelerate the business and strengthen your legal foundation.

Up to 60% of in-house legal requests arrive via email or chat without the context needed to act—creating days of back-and-forth and avoidable risk. AI can help, but only if your front door is structured. The fastest path to value is an AI-ready intake that standardizes data, automates triage, and continuously improves your playbooks.

On Sandstone—the modern legal ops platform and knowledge layer—every intake, triage, and decision compounds. Playbooks, positions, and workflows become a living operating system that turns legal from reactive support into a proactive force for speed, alignment, and trust.

Step 1: Design a Structured Front Door

Create a single, structured intake that captures the data your team and AI agents need the first time.

- Define canonical request types (e.g., NDA, vendor contract, marketing review, product counseling, employment, privacy/security). Map each to an owner and SLA.

- Collect only the essential variables for each type: business goal, counterparty, contract value, data flows, jurisdictions, deadlines, URLs/assets, and approvals.

- Add dynamic logic (show/hide fields) so requesters see a short, relevant form. Offer entry points in Slack/Teams, email, and a web portal.

- Embed your positions and playbook prompts as hints in the form (e.g., “We default to mutual NDAs unless…”). This reduces rework and educates the business.

- Owners and metrics: legal ops owns the schema; practice leads own playbook content. Track completion rate (>90%), time-to-acknowledge (<1 hour), and resubmission rate (<5%).

Why it matters: structured data is the substrate for automation. It’s also crafted precision—tools carved to fit your processes—and it integrates naturally into where your stakeholders already work.

Step 2: Automate Triage with Policy-Backed AI

Use AI agents to classify, summarize, route, and propose next actions—grounded in your playbooks and risk thresholds.

- Build policy-backed routing rules (e.g., deal size, data sensitivity, party risk, marketing channel). Convert rules into risk tiers that dictate auto-approve, standard path, or escalate.

- Let an AI agent pre-classify requests, extract key facts, check for missing inputs, and draft a response. Ground the agent in your knowledge layer: playbooks, positions, clause libraries, and prior decisions.

- Auto-assign to the right queue with directory context (region, business unit, approvers). Open a ticket, set SLA, and notify in Slack/Teams.

- Deflect the easy work: for low-risk NDAs or pre-approved marketing claims, return self-serve guidance or a ready-to-send artifact for requester sign-off.

- Metrics: percent auto-triaged (>60% in 60 days), median time-to-first-response (<30 minutes), deflection rate to self-serve, and QA sample accuracy (>95% on critical fields).

On Sandstone, layered data and modular workflows let agents act with confidence: they don’t guess—they retrieve your positions, apply your rules, and learn from each disposition.

Step 3: Close the Loop and Make Knowledge Compound

Turn outcomes into continuously improving playbooks so decisions build on each other instead of disappearing.

- Capture structured dispositions: outcome (approved/changes/reject), risk accepted vs. mitigated, clause deviations, cycle time, and root cause of delay.

- Feed the signals back: update playbooks and positions when patterns emerge (e.g., standard fallback for a recurring vendor posture). Require owner review and timestamp changes.

- Illuminate bottlenecks: dashboards for top request types, recurring blockers (e.g., DPAs missing DPIAs), teams needing enablement, and the dollar value of delays avoided.

- Productize repeatable paths: auto-execute low-risk NDAs, auto-generate vendor questionnaires, and auto-redline standard clauses using your approved positions.

- Metrics: reduction in median cycle time (target 30–50%), decreased escalations, and a rising share of requests handled at the edge by business users.

This is strength through layers: each request enriches your operating system, tightening alignment between business and law and making the next decision faster and safer.

A 30-Day Pilot You Can Start Now

- Week 1: Pick 2 request types (e.g., NDA and marketing review). Draft the intake schema and minimal playbooks. Define SLAs and risk tiers.

- Week 2: Launch the structured front door in Slack/Teams and your portal. Turn on AI classification and missing-field checks.

- Week 3: Add routing rules and auto-responses for low-risk paths. Stand up a QA process (5–10% sample review).

- Week 4: Instrument dashboards. Hold a feedback session with requesters and update playbooks based on outcomes.

Next step: instrument your current intake (even if it’s just an email alias) to log request type, missing info, and time-to-first-response. Baseline for one week—those numbers will tell you exactly where to automate first.

The Foundation for Speed, Alignment, and Trust

An AI-ready intake isn’t just a workflow upgrade—it’s the bedrock of scalable legal operations. When requests start structured, triage is fast, decisions are consistent, and knowledge compounds. That’s how legal moves from bottleneck to connective tissue for growth.

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