AI Intake Triage: A Practical Guide for GCs and Legal Ops Leaders

The Intake Problem You Can See but Can’t Measure

In Sandstone’s customer base, roughly half of new legal requests arrive through unmanaged channels like email and Slack. When intake isn’t structured, cycle times stretch 2–3x: lawyers become human routers, work gets lost, and playbooks are applied inconsistently. The fastest-growing companies feel this most—volume climbs, risk posture tightens, and the “quick question” backlog quietly becomes a drag on revenue.

The good news: intake is one of the highest‑leverage places to apply AI in-house. You don’t need a moonshot. You need a disciplined way to capture context, route work, and apply positions the moment a request appears.

What “AI Triage” Actually Means in Practice

An AI intake triage agent sits where requests originate—email, Slack, portals—and performs four reliable steps before a lawyer touches the work:

1) Normalize the request

- Extracts metadata: requester, business unit, counterparty, document type, deadlines, data flows, region, and regulatory flags.

- Maps it to a matter type (e.g., “NDA – Vendor,” “Marketing Review,” “Security Questionnaire – Renewal”).

2) Apply playbooks and positions

- Pulls the latest playbook for the matter type from your knowledge layer.

- Suggests the default path (approve, self-serve template, route to procurement/privacy, or escalate to counsel) and highlights deviation triggers (e.g., data transfer outside approved regions, non-standard indemnity caps).

3) Generate first responses and artifacts

- Drafts a tailored reply in the channel where the request came in, including next steps and required documents.

- Produces the right asset: clean NDA template with pre-applied positions; intake checklist; or a redline when a standard counterparty paper is detected.

4) Log and learn

- Creates or updates a matter record with all extracted fields, decisions, and outcomes.

- Captures playbook gaps and edge cases so your positions get sharper with each request.

On a platform like Sandstone, these steps are layered—data, playbooks, and decisions compound—so the system gets stronger as volume increases instead of slowing down.

Guardrails, Not Guesswork: Accuracy, Audit, and Alignment

AI should never be a black box. High‑functioning teams wrap triage in clear governance:

- Confidence thresholds and scoped actions: If the agent is ≥95% confident and the risk is low (e.g., mutual NDA), it can proceed with a self-serve template. Anything else routes to review with a rationale attached.

- Structured justifications: Every decision includes “why” tied to specific playbook clauses and risk criteria. This makes audits and stakeholder conversations faster and calmer.

- Human-in-the-loop checkpoints: For medium/high‑risk categories (e.g., data processing addenda, marketing claims), the agent prepares the work but requires legal approval before sending.

- Privacy and security posture: Keep models within your tenant, mask sensitive fields in prompts, and log model interactions as part of your matter record. This is table stakes for enterprise legal.

With these controls, AI triage becomes repeatable and defensible—the difference between a helpful demo and a dependable operating capability.

KPIs That Prove It Works

Pick a small set of metrics that reflect speed, quality, and adoption:

- Cycle time to first response: Target same‑day on low‑risk matters; sub‑1 hour for true quick hits.

- Percent of requests auto‑routed or self‑served: 30–50% within 60 days is achievable for teams with clear playbooks.

- Exception rate: Track how often the agent escalates and why; use this to refine positions.

- Playbook adherence: Measure variance from standard terms across matters; aim for consistent application and explainable deviations.

- SLA attainment by matter type: Make service levels visible to business partners to build trust.

When these metrics move in the right direction, legal’s perceived responsiveness—and credibility—improves fast.

Implementation Blueprint (30–60 Days)

You don’t need to “boil the ocean.” Start with one high‑volume, low‑risk flow and expand.

Weeks 1–2: Map and standardize

- Choose a candidate workflow (e.g., vendor NDAs or marketing reviews).

- Inventory positions and templates; convert them into structured playbook rules.

- Define routing logic and SLAs with stakeholders (procurement, privacy, finance).

Weeks 3–4: Configure and pilot

- Connect intake channels (email aliases, Slack forms, or a lightweight portal).

- Configure the AI agent to extract fields, apply the playbook, and draft responses.

- Run a private pilot with 1–2 business units; compare agent outputs to current practice.

Weeks 5–8: Launch and iterate

- Turn on human‑in‑the‑loop for medium/high‑risk items; allow auto‑action for low‑risk.

- Publish SLAs and “what to expect” guidance to requesters.

- Review exceptions weekly; update playbooks and thresholds. Expand to the next workflow.

On Sandstone, these steps live in one system: intake forms in Slack/email, a centralized knowledge layer for playbooks and positions, and modular workflows that adapt to your contours rather than forcing new behavior.

One Practical Next Step

Pick one workflow (NDAs are ideal) and run a two‑week triage trial: turn your current checklist into a structured playbook, connect a single intake channel, and measure cycle time to first response. If you see a >30% reduction, graduate to auto‑routing and self‑serve templates for low‑risk variants.

The Foundation for Speed, Alignment, and Trust

When intake is structured and triage is consistent, legal stops being a bottleneck and becomes connective tissue. Every request strengthens the system—positions sharpen, routing gets smarter, and decisions compound as institutional knowledge. That’s the promise of a modern legal ops platform and knowledge layer like Sandstone: strength through layers, crafted precision, and natural integration with how your team already works. Build once. Learn continuously. Move the business with clarity and confidence.