Before You Deploy AI Agents, Fix Intake: How Legal Can Scale With Confidence

Before You Deploy AI Agents, Fix Intake: How Legal Can Scale With Confidence

If more than half of your legal requests arrive via email or chat without context, you’re not alone. In conversations with mid-sized to enterprise teams, unstructured intake routinely drives delays, rework, and frustration. The surge of legal AI agents is exciting—but without a sturdy intake layer, automation amplifies chaos. Get the basics right and AI becomes compound interest on your legal knowledge.

This post outlines a practical framework to futureproof legal intake so AI agents can triage, route, and resolve work safely—delivering speed without sacrificing judgment.

What “AI-Ready” Legal Intake Actually Means

Legal intake is the front door for work requests—how business users submit matters (e.g., NDAs, vendor contracts, product reviews) and how Legal triages them. AI-ready intake means requests are structured, contextualized, and linked to playbooks so automation can help:

- Collect the right details the first time

- Auto-triage to the right owner or queue

- Apply the correct playbook or position

- Generate summaries, drafts, and next steps with guardrails

For Legal Ops, this is the foundation of a reliable operating model: fewer back-and-forths, consistent decisions, clear metrics. Platforms like Sandstone turn playbooks, positions, and workflows into a living, AI-powered knowledge layer—so every intake strengthens what the team knows and how it works.

The Stakes: Don’t Let Agent Hype Outrun Process Reality

Skipping intake fundamentals creates real risks:

- Agent sprawl: multiple bots working off inconsistent templates and rules

- Data blind spots: key facts trapped in email threads the agent never sees

- Compliance gaps: PII, export controls, or procurement rules not captured at the start

- Trust erosion: business users get fast wrong answers instead of reliable guidance

Before you add more AI, fix intake. Structured intake reduces risk, increases throughput, and turns “answers” into auditable decisions that compound.

A 5-Step Framework to Make Intake AI-Ready

1) Define Your Intake Taxonomy and SLAs

- List your top 8–12 request types (NDAs, vendor MSAs, DPAs, marketing review, product counsel, equity, litigation hold, etc.)

- Assign owners and backup queues

- Set clear SLAs by type (e.g., NDA same-day, vendor contracts 3–5 business days)

2) Operationalize Playbooks and Positions

- Convert policy and precedent into checklists and decision trees

- Mark safe-to-automate steps vs. attorney-required judgment

- Capture fallback rules and escalations (e.g., “if vendor redlines liability cap, route to commercial counsel”)

3) Design Smart, Dynamic Intake

- Use no-code forms with conditional logic to ask only relevant questions

- Require key attachments (SOW, redlines) up front; pull vendor profile data from procurement

- Auto-assign metadata (business unit, region, data category) for reporting and routing

4) Connect Systems and Guardrails

- Identity: SSO and role-based access for requesters and approvers

- Policy: DLP, PII tagging, jurisdictional rules baked into forms and flows

- Records: sync matter IDs and documents to CLM, ticketing, and DMS

5) Pilot an Intake Triage Agent (Copilot First, Then Autopilot)

- Start in recommendation mode: the agent drafts triage notes, populates fields, and proposes routing

- Limit scope to 1–2 request types; measure accuracy, completeness, and SLA impact

- Move to autopilot for low-risk paths (e.g., auto-approve standard NDA if criteria met)

Sandstone’s approach—strength through layers, crafted precision, natural integration—maps directly here: layered data and modular workflows that fit your contours, not the other way around.

High-Impact Use Cases to Start

- NDAs: Agent checks counterparty status, applies the right template, and auto-approves if within position; escalates if terms deviate.

- Vendor Contracts: Intake pulls vendor profile and data categories from procurement; agent suggests fallback clauses and routes to the right queue.

- Product Counsel: Dynamic questions capture data flows, AI features, and jurisdictions; agent flags regulatory hotspots and attaches the relevant playbook.

- Marketing Review: Intake links to brand and claims policies; agent pre-screens copy, highlights risky phrases, and drafts approval notes.

Metrics That Prove It’s Working

Track a small, consistent set of KPIs:

- Cycle time by request type (target 30–50% reduction post-implementation)

- Request completeness on first submission (aim for 70–85%+)

- Deflection rate to self-serve or auto-approve paths (25–40% as a stretch goal)

- SLA adherence by queue and owner

- Business satisfaction (CSAT) and rework rate

Tie these to quarterly goals so Legal and the business see the value.

A 30–60–90 Day Plan

- Day 0–30: Map top five request types, draft SLAs, and convert one playbook to a decision tree. Build a no-code intake form with conditional logic.

- Day 31–60: Connect identity and document systems; launch a copilot triage agent for one use case (e.g., NDAs). Start measuring completeness and cycle time.

- Day 61–90: Expand to a second use case; enable autopilot for low-risk paths. Publish dashboards and refine positions based on data.

Actionable next step: Pick one request type and run a 30-day copilot pilot. Measure baseline cycle time and request completeness, then compare post-pilot.

The Close: Build on Bedrock

When intake is structured and tied to living playbooks, AI doesn’t just move faster—it learns with you. That’s the promise of a modern legal ops platform and knowledge layer like Sandstone: every intake, triage, and decision strengthens your foundation. The result is scalable, streamlined operations that become a source of trust and growth across the business.

If you’re ready to turn AI from a novelty into an operating advantage, start with intake—and talk to us about layering your playbooks, positions, and workflows into a system that compounds.