Why AI‑Powered Intake Matters for In‑House Legal (and How to Get It Right)

If your team fields 200 requests a month and even 40% arrive without basics (contract type, counterparty, deadline), that’s 80 back‑and‑forth threads, days of delay, and a pile of avoidable risk. Intake is where legal work starts—and where value often leaks. AI changes that, turning first touch into the moment knowledge compounds, not disappears.

What “AI‑Powered Intake” Actually Means

AI‑powered intake is more than a form. It’s a living front door that:

- Meets requesters where they work (email, Slack/Teams, procurement, CRM) and normalizes inputs into one queue.

- Uses trained agents to gather missing context, classify issues, and route by policy, not guesswork.

- Auto‑generates first responses, drafts, or checklists based on your playbooks and positions.

- Captures decisions and artifacts so the next request is faster and more consistent.

On Sandstone, those agents sit on top of your playbooks, clause libraries, and approvals. They don’t replace judgment—they scale it—so every intake strengthens your legal foundation.

Why It Matters Now

- Speed and signal: Business partners need crisp answers in hours, not days. Intake is your best lever on time‑to‑first‑touch and cycle time.

- Fragmented channels: Sales asks in Slack, Finance emails, Procurement opens tickets. AI consolidates without forcing everyone to change tools.

- Accountability: Without structure, it’s hard to defend priorities, measure SLAs, or forecast capacity. Clean intake creates clean data.

- Risk posture: The same policy applied 10 different ways isn’t a policy. AI‑assisted triage enforces playbooks consistently and documents why.

The payoff shows up as fewer reopenings, clearer scope, and more self‑service resolutions (NDAs, marketing claims, low‑risk vendor reviews) that never need to hit an attorney’s desk.

How to Get It Right (Step by Step)

1) Define the intake object. For each common request, list the minimum fields to do real work (e.g., contract type, counterparty, governing law, value, deadline, data flows). Keep it tight.

2) Start with three high‑volume workflows. Typical picks: NDAs, vendor reviews/privacy, sales order forms. Design for success here before expanding.

3) Train on your reality. Load playbooks (positions, fallback language, escalation rules), approved templates, and routing logic. Agents should cite them, not invent them.

4) Meet users where they are. Offer one link, but also enable email forwarding and Slack/Teams slash commands. AI should normalize, not silo.

5) Automate first touches. Have the agent gather missing details, propose next steps, or assemble a ready‑to‑send NDA with known variables.

6) Keep humans in the loop. Set thresholds: auto‑resolve low‑risk, require review for anything above risk line. Every AI action should be transparent and reversible.

7) Instrument outcomes. Track time‑to‑first‑response, cycle time by request type, percent auto‑resolved, reopen rate, and requester CSAT. Share the dashboard with the business.

8) Close the loop. Every decision updates the knowledge layer (positions, fallback clauses, routing). Next time, the system is smarter.

Common Pitfalls (and Fixes)

- Over‑customization on day one: Boil the ocean with 30 forms, and no one uses them. Fix: Launch with three workflows; expand monthly.

- Shadow channels: People still DM attorneys. Fix: Make the best path the easiest path; enable quick‑add from email/Slack and auto‑acknowledgments.

- Black‑box AI: If outputs aren’t explainable, adoption stalls. Fix: Require cited sources (playbooks, clauses) and show rationale in the ticket.

- No audit trail: Approvals happen in chat and vanish. Fix: Log decisions, escalations, and versioned templates as part of the record.

- Change without champions: Intake fails if managers aren’t modeling it. Fix: Recruit GTM/Procurement leads as co‑owners; report wins weekly.

Quick Wins with AI Agents

- NDA self‑service: Agent collects parties, term, governing law; generates the approved template; routes exceptions automatically.

- Vendor privacy review: Intake captures data categories, processing locations, sub‑processors; agent maps to your DPIA checklist and flags high‑risk flows.

- Marketing claims: Agent extracts claims language and required substantiation; routes to brand counsel with a pre‑filled checklist.

- Sales order bumps: Agent classifies standard vs. non‑standard terms; suggests fallback positions and assembles a redline packet for counsel.

Each win removes friction from a high‑volume edge, proving value without big‑bang change.

Actionable Next Step: A 30‑Day Pilot Blueprint

- Week 1: Pick three workflows, define minimum fields, import templates, and codify routing/SLAs.

- Week 2: Train the agent on playbooks and examples; enable email and Slack intake; turn on auto‑acknowledgments with clear expectations.

- Week 3: Go live to two departments (e.g., Sales and Procurement); office hours daily; track first‑response time and percent of requests needing no follow‑ups.

- Week 4: Review metrics, collect qualitative feedback, refine prompts and fields; publish a before/after dashboard.

If you’re on Sandstone, this looks like dropping an intake agent into Slack and your shared legal inbox, attaching your playbooks, and turning on human‑in‑the‑loop review for anything above the risk line. By day 30, you’ll know what to scale.

The Bottom Line

When intake turns into a knowledge engine, legal stops being a bottleneck and becomes connective tissue—aligning speed with standards. Layer by layer, your playbooks, positions, and workflows compound into a durable operating system. That’s the promise of Sandstone: strength through layers, crafted precision, and natural integration with how your team already works. Build the front door right, and you build trust and growth on bedrock.