AI for Legal Teams: 4 Steps to Transform Intake into Outcomes

Nearly 1 in 5 hours of a knowledge worker’s week goes to searching or gathering information. For in-house legal, most of that waste hides in intake—emails, Slack pings, and “quick questions” that fracture focus and bury context. The cost isn’t just time. It’s uneven risk decisions, fragmented knowledge, and a queue that grows faster than your team. The fix isn’t another form; it’s treating intake like your operating system.

Below is a four-step, AI-ready approach to turn intake into speed, alignment, and trust—grounded in workflows Sandstone automates every day.

Step 1: Standardize the Front Door (Without Forcing New Behavior)

Start by meeting the business where it already works. Offer one front door for requests—with native touchpoints in email, Slack, and your ticketing tool—to prevent context from scattering. Keep the form short and purposeful: request type, counterparty, business priority, due date, data sensitivity, and attachments. Add dynamic follow-ups only when needed (e.g., “Is there personal data?” triggers privacy prompts).

From there, define routing rules: NDAs and routine vendor contracts auto-triage to AI agents; high-risk marketing claims route to counsel; late-cycle escalations ping a duty lawyer. Send instant acknowledgments with expected SLAs and what happens next. That alone defuses internal anxiety and reduces duplicate pings.

In Sandstone, the “front door” isn’t just a form—it’s a layer that captures context, normalizes metadata, and hands every request to the right workflow with zero extra clicks.

Step 2: Codify Decisions and Playbooks So AI Can Act

AI can’t follow what you haven’t written down. Convert playbooks and positions into structured rules: clause fallbacks by risk tier, when to accept a counterparty paper, required approvals by deal value, data residency defaults, marketing claim guardrails, and regulatory triggers by region.

Model these as layered decision trees instead of static PDFs. Each layer adds precision—business unit, product, region, contract type—so the system can make the same decision your best lawyer would, the first time. Crucially, version and annotate every rule with “why” and links to precedent. That’s how knowledge compounds rather than vanishes when people rotate roles.

Sandstone turns playbooks into a living, AI-powered knowledge layer. Agents use those positions in real time to propose a path, justify it with citations, and log the decision for audit and learning.

Step 3: Automate the First 80% With Agents, Escalate the Rest

Pick a high-volume workflow—say NDAs or low-risk vendor SOWs. An AI agent can:

- Classify the request and enrich it with CRM/vendor data.

- Select the right template based on counterparty region and data flows.

- Apply clause fallbacks according to your positions and risk tier.

- Draft the document, redline counterparty paper, or reply with a policy-based answer.

- Flag exceptions (e.g., unlimited liability, sensitive data processing) with a clear rationale.

- File artifacts to your CLM (Contract Lifecycle Management) and update the ticket automatically.

For example, a marketing review agent can evaluate claims against your approved positions, check for restricted phrases, propose compliant alternatives, and escalate only the edge cases. In Sandstone, this feels natural—agents run inside your intake flow, cite your playbooks, and log every action so counsel has full control and traceability. Routine requests resolve in minutes, not days; lawyers focus on the 20% that actually need judgment.

Step 4: Instrument the Loop—Measure, Learn, and Tighten

You can’t scale what you can’t see. Track:

- Time to Triage: Minutes from submission to acknowledgment and routing.

- Cycle Time by Request Type: From intake to resolution.

- Auto-Resolution Rate: Share of tasks solved without human review.

- Exception Rate and Top Exceptions: What keeps escalating—and why.

- Rework Rate: How often you revisit a request due to missing context or unclear guidance.

- Requester Satisfaction: Quick CSAT after closure to spot friction.

Review these weekly. Where you see clusters of exceptions, refine the underlying position or add a new fallback. Where triage stalls, simplify the form or tighten routing rules. Sandstone closes this loop by tying analytics to the exact layer—form, rule, or template—that needs the tweak.

Actionable Next Step: Run a 30-Day Pilot

- Choose one workflow (NDAs or marketing claims) with high volume and low risk.

- Stand up a single front door in Slack/email with 6–8 essential fields.

- Codify 10–15 positions (fallbacks, red lines, approvals) as rules.

- Launch one AI agent to draft/respond using those positions.

- Baseline metrics on day 1; run weekly reviews to refine.

Most teams see 30–50% faster cycle times within a month—and a clearer map of where human judgment adds the most value.

The Payoff: A Stronger Legal Foundation, Layer by Layer

When intake becomes a living system, every request strengthens your organization. Context gets captured, decisions are consistent, and knowledge compounds—turning legal from a reactive bottleneck into connective tissue for the business. That’s the promise of Sandstone: strength through layers of data and decisions, crafted precision that fits how your team actually works, and natural integration with the tools you already use.

Build the foundation once. Let it scale across every matter. That’s how legal moves in harmony with the business—and how trust, speed, and growth become your default state.