How In-house Legal Can Automate Intake and Triage Without Full-Scale CLM

On many in-house teams, 30–50% of legal time is spent triaging inbox requests, chasing context, and routing work—not actually giving advice. It’s the most fixable bottleneck in legal ops. The fastest path to relief isn’t a monolithic CLM rollout. It’s an AI-first intake front door that captures context, applies playbooks, and routes work precisely—so counsel touches fewer, higher-value matters.

The Intake Reality: High Volume, Low Context

Most legal requests arrive via email, Slack, or ad‑hoc forms—missing key facts like deal value, data categories, or counterparty paper. The result:

- Slow starts and long back-and-forth cycles

- Work routed to the wrong person or left untracked

- Inconsistent application of playbooks and risk positions

- Poor visibility into SLAs and capacity

Legacy fixes (long forms, manual triage, “email to ticket” bots) don’t solve the context gap. Legal still has to interpret, clarify, and decide. That’s why intake is primed for AI agents: they can ask targeted follow-ups, classify matters, and apply playbooks instantly—before a lawyer lifts a finger.

AI Agents, Not Forms, as the Front Door

An AI intake agent embedded where business users already work (Slack, email, your CRM) can:

- Ask dynamic questions based on request type and risk signals

- Classify into matters (NDA, vendor/SaaS, DPA, marketing review, policy Q&A)

- Pull context from connected systems (CRM, procurement, security questionnaires)

- Apply playbooks to draft or redline against safe positions

- Route by expertise and load, with clear SLAs and escalation rules

- Log every decision to your knowledge layer for future reuse

Compared with “big-bang” CLM, an agent-led front door ships fast, respects current workflows, and compounds knowledge. You get speed and standardization without forcing the business (or legal) to relearn how to work.

A Lightweight Blueprint You Can Ship in Weeks

Start with the top 3–5 high-volume requests. For most teams that’s NDAs, vendor reviews, DPAs, marketing copy, and policy questions.

1) Define a lean intake schema per request type

- Must-haves only: contract paper, value/term, data categories, region, use case, deadlines.

- Map to risk triggers (PII, cross-border, HIPAA, minors, sub-processors, marketing claims).

2) Encode your playbooks and approval matrix

- Positions, fallbacks, and “never” lines.

- Auto-approval thresholds by value, data, and contract type.

3) Build routing and SLAs around reality

- Assign by expertise and capacity; include a fast lane for revenue blockers.

- Set clear first-response and cycle-time targets per type.

4) Automate the obvious

- NDA self-serve: generate company paper, or redline third-party to safe positions.

- Vendor review: pre-screen with a security and data checklist; draft standard clauses.

- Marketing review: check claims against a structured policy; flag risky language.

5) Close the loop into your knowledge layer

- Capture outcomes, exceptions, and approved deviations.

- Feed back into the agent so the next similar request is faster and safer.

On Sandstone, this looks like layered playbooks connected to intake, with an AI agent that triages, drafts, and routes based on your risk model. Every interaction strengthens your institutional knowledge—so what you decide once becomes how you decide next time.

What to Measure: KPIs Legal Actually Controls

- First-response time by request type

- Cycle time (start to signed/answered)

- Auto-resolution rate (no human touch or single-touch)

- Escalation rate and reasons

- Deflection to self-serve assets (templates, FAQs, playbooks)

- Requester CSAT and internal NPS

If you’re not measuring these today, start with two: first-response time and auto-resolution rate. They move fastest once an agent is in place.

Why This Beats Waiting for Full-Scale CLM

- Speed to value: Weeks, not quarters. Pilot on NDAs or vendor intake, expand from there.

- Minimal change management: Meet requesters in Slack/email; no new portals required.

- Precision over process bloat: Only the questions that matter, asked when they matter.

- Compounding knowledge: Every intake and outcome updates the playbook and agent prompts.

- Cost control: Scale volume without adding headcount or paying for unused CLM modules.

A modern CLM can still play a role—especially for signature workflows and repository needs. But you don’t need to wait on it to fix intake. An agent-led front door addresses the biggest time sink today and makes any future CLM more effective tomorrow.

Your 30-Day Pilot Plan (Actionable Next Step)

- Week 1: Choose one use case (NDAs). Define must-have fields, risk triggers, and approval thresholds. Import your NDA playbook and fallback positions.

- Week 2: Deploy an AI intake agent in Slack/email. Route to a small reviewer pool with clear SLAs. Turn on automated NDA generation/redlining.

- Week 3: Track KPIs (first-response, auto-resolution). Tune prompts and playbook exceptions. Add requester-friendly guidance for common blockers.

- Week 4: Publish a simple service catalog. Expand to a second type (vendor reviews) and enable auto-approvals under low-risk thresholds.

By day 30, most teams see faster first responses, cleaner data, and fewer manual touches—without asking the business to change channels or learn a new tool.

The Bottom Line

Legal shouldn’t be a bottleneck. With an AI-powered intake front door, your team becomes the connective tissue of the business—fast, consistent, and trusted. Sandstone turns playbooks, positions, and workflows into a living operating system, so every triage and decision strengthens your legal foundation. That’s how in-house legal scales with clarity and confidence—and how knowledge compounds instead of disappearing.