AI-Driven Legal Intake: A Guide for In-House Counsel

Your legal inbox isn’t a workflow. Yet for many teams, it’s still where requests go to wait. In most organizations, more than half of inbound legal asks are routine variations on a few patterns—NDAs, vendor DPAs, marketing approvals—yet they clog channels, fragment context, and slow decisions.

What if intake itself did the heavy lifting? With an AI-powered operating model, the “front door” to legal captures context, applies playbooks, and routes work with crafted precision—so lawyers focus on judgment, not scavenger hunts.

Why Intake Is the Hidden Bottleneck

Legal wants to move in step with the business, but unstructured intake creates thrash:

- Missing information triggers back-and-forth.

- Ambiguous ownership leads to triage by whoever replied first.

- Institutional knowledge lives in docs and people, not the workflow.

The downstream effects are predictable: slower cycle times, inconsistent positions, and eroding stakeholder trust. Most “fixes” (shared inboxes, manual spreadsheets, more Slack channels) add layers without adding structure. The solution is not more lanes—it’s a foundation. Intake must become the place where knowledge compounds, not disappears.

The Operating Model: From Email to AI Triage

Shift from reactive routing to a living, AI-powered intake that translates business questions into operational decisions. The core elements:

- Structured front door: A lightweight, context-aware form or portal that adapts to request type (e.g., NDA vs. vendor review) and requester role. No generic text boxes; guided prompts that gather what counsel actually needs.

- Playbooks and positions: Centralized guidance that reflects how your team makes decisions—approval thresholds, risk positions, fallback clauses, escalation criteria.

- Modular workflows: Reusable steps (collect, classify, enrich, route, resolve) mapped to each request type, so you can iterate without rebuilding.

On a platform like Sandstone, these layers tie together: playbooks and positions become executable logic, so the same rules guide triage, drafting, and escalation. The result is natural integration—intake that fits how your team already works while reducing handoffs and rework.

What an AI Agent Actually Does

Think of the agent as legal’s connective tissue at the edge of the business:

- Classifies the request: Detects matter type and urgency from form fields and attachments; flags risk keywords (e.g., data transfer, exclusivity, auto-renew).

- Collects missing context: Asks targeted follow-ups only when needed (counterparty, use case, data categories, deal size), minimizing requester time.

- Enriches from systems: Pulls vendor status from procurement, deal size from CRM, and DPIA history from privacy tools to pre-populate the case.

- Applies your positions: Checks playbooks for standard vs. non-standard terms, identifies approved templates, and suggests fallback language.

- Drafts and routes: Prepares a first-pass NDA or risk summary, opens the right queue, and assigns reviewer based on load and expertise.

- Closes the loop: Summarizes the decision, updates the knowledge base, and shares the outcome back to the requester with next steps.

Because decisions feed back into the system, every intake, triage, and outcome strengthens the foundation. Over time, the agent gets better at suggesting the right position on day one.

Metrics That Matter

Track the shift from reactive to proactive with a few practical KPIs:

- Time to first response: From submission to acknowledgment with clear next steps.

- Cycle time by request type: NDA, vendor DPA, marketing review—measured median, not average.

- First-pass completion rate: Percent of requests resolved without attorney intervention.

- Playbook adherence: How often outcomes align with stated positions; variance triggers review.

- Rework rate: Requests that bounce between legal and business due to missing info.

If you’re starting from email, baseline these numbers for one high-volume workflow. Small wins compound quickly when the front door is structured.

Action to Take This Week

Pilot AI-driven intake for a single workflow—vendor DPAs:

1) Define the intake questions you always ask (data categories, transfers, subprocessors, purpose, contract value). Keep it to 6–8 required fields.

2) Codify your positions: acceptable SCCs, transfer addenda, audit rights thresholds, security questionnaire criteria, and escalation triggers.

3) Map the workflow: classify → collect → enrich (procurement/CRM) → apply playbook → route → close.

4) Let the agent draft: have it generate the initial risk summary and standard redlines against your template.

5) Review and refine: compare outcomes to your positions weekly; tighten prompts and thresholds as patterns emerge.

In Sandstone, you can express these as modular steps with guardrails—so updates to positions automatically inform how intake, drafting, and triage behave.

The Foundation for Trust and Growth

Speed without standards erodes trust; standards without speed become a bottleneck. The path forward is layered: structured intake, executable playbooks, and AI agents that make institutional knowledge actionable at the moment of need. When the front door to legal is clear and intelligent, work moves faster, decisions align, and knowledge compounds rather than leaking through inboxes.

That is the promise of a modern legal ops platform and knowledge layer like Sandstone: strength through layers, crafted precision in every workflow, and natural integration with how your team already operates. Build the foundation now, and legal becomes not just a service line—but the bedrock of clarity and confidence across the business.