How to Automate Legal Intake to Resolve 60% of Requests: A Practical Playbook

Most in-house teams still manage legal intake through shared inboxes, Slack DMs, and spreadsheets. Yet 60–70% of requests (NDAs, vendor forms, policy questions) are repeatable. That gap is a tax on your team’s attention—and a drag on the business.

This playbook shows how to convert intake into an AI-powered workflow so the routine resolves itself, the risky gets to counsel fast, and knowledge compounds instead of disappearing.

TL;DR: What You’ll Achieve

- Deflect or resolve 50–60% of routine requests via guided self-service.

- Triage the rest in minutes with clear routing, SLAs (service-level agreements), and context-rich handoffs.

- Capture decisions into a living knowledge layer so answers improve over time.

- Prove impact with cycle-time, deflection, and satisfaction metrics.

Step 1: Map Demand and Define “Good” Outcomes

Start with data, not software. Pull the last 60–90 days of intake from email, ticketing, or your contract tool. Bucket requests into 5–8 categories: e.g., NDAs, vendor onboarding, DPAs, marketing review, policy/HR questions, procurement terms.

For each category, define:

- Resolution path: self-serve, paralegal, or attorney.

- Required inputs: forms, templates, documents, approvals.

- Risk flags: redlines, high contract value, regulated data, unusual jurisdictions.

- “Done” definition: what documents, fields, and approvals confirm completion.

So what? This turns “legal is slow” into a measurable queue. You’ll know what can be automated and what needs judgment.

Step 2: Turn Playbooks Into a Living Knowledge Layer

Your team already has positions—buried in docs or people’s heads. Extract them into modular, atomic rules: “If standard NDA, send self-serve; if counterparty paper, route to contracts counsel; if deal value >$250k, require finance approval.”

House these as structured decision points tied to templates and clauses (not static PDFs). In Sandstone, playbooks become intelligent building blocks that AI agents reference during intake. When guidance changes, update the rule once and every workflow reflects it.

So what? Knowledge stops leaking across emails and 1:1 chats. It becomes searchable, actionable policy that drives consistent outcomes.

Step 3: Automate Triage With AI Agents, Not Magic

Automation isn’t about a black box making legal calls. It’s about agents that gather context, label risk, and apply your rules.

A reliable triage flow looks like this:

1) Intake capture: a single, branded form or Slack command with dynamic questions.

2) Context enrichment: the agent pulls CRM data, prior contracts, and policy references.

3) Risk assessment: apply playbook rules (e.g., data types, clause variances, threshold values).

4) Decision and action:

- Self-serve: generate a standard NDA or FAQ answer with guardrails.

- Route: assign to the right queue with a summary, redline diffs, and missing inputs flagged.

- Escalate: tag complex or high-risk matters for attorney review with rationale.

On Sandstone, agents work on top of your layered data—contracts, positions, SLAs—so the triage is explainable and auditable.

So what? Lawyers spend time where judgment matters; the system handles data gathering and first-pass decisions.

Step 4: Close the Loop With SLAs, Visibility, and Feedback

Publish simple SLAs per category—e.g., self-serve instant, paralegal 1 business day, attorney 3 business days. Display live status to requesters and stakeholders (Sales, Procurement) to end the “any update?” pings.

Add in-line feedback: thumbs up/down for self-service answers; quick surveys on resolved tickets. Feed those signals back into the knowledge layer—if an answer underperforms, refine the playbook or add a clarifying question at intake.

So what? Trust increases when people can see where their request is and when to expect a result.

Step 5: Prove ROI With Three Metrics That Matter

Skip vanity stats. Report:

- Deflection rate: % of requests resolved via self-serve.

- Median cycle time by category: from intake to completion.

- Rework rate: % of matters bounced back for missing info or misrouting.

Example: One mid-market SaaS company moved NDAs and marketing reviews to self-serve and cut median cycle time from 2.3 days to 22 minutes, deflecting 58% of volume. Attorney time shifted to DPAs and complex deals without adding headcount.

So what? With hard numbers, you can justify budget, defend SLAs, and prioritize the next automation wave.

Implementation Checklist (2–4 Weeks)

- Week 1: Pull 90 days of intake; categorize and size top 5 request types.

- Week 2: Document decision rules and “done” definitions; map required inputs.

- Week 3: Stand up a single intake surface (form or Slack) and connect your playbooks.

- Week 4: Enable agents for context gathering and routing; publish SLAs and dashboards.

Actionable next step: Schedule a 45-minute working session with Sales, Procurement, and Security to align on categories, SLAs, and thresholds—then lock those into your playbooks.

Why This Matters for Scalable Legal Ops

When intake, playbooks, and workflows live in one AI-powered layer, every request strengthens the foundation. Answers don’t walk out the door; they compound. The business gets speed and clarity. Legal gets leverage and trust.

This is Sandstone’s design principle: strength through layers, crafted precision, natural integration. We meet you where you work, turn positions into operating logic, and let agents carry the busywork so counsel can do counsel. If you’re ready to see how an AI-driven intake can resolve 60% of requests without sacrificing control, let’s turn your playbooks into an operating system.