How to Standardize Contract Intake With AI to Cut Cycle Time

How to Standardize Contract Intake With AI to Cut Cycle Time

Analysts estimate 20–30% of in-house legal work is automatable today. Yet many teams still triage contracts from a shared inbox, losing context and time. Standardized, AI-powered contract intake turns ad hoc requests into a repeatable flow that speeds deals and reduces risk—without adding headcount.

Map Request Types and Risk Tiers

Start by inventorying the work that hits legal. Group the most common requests and define objective risk tiers.

- Common request types: NDA (Non-Disclosure Agreement), MSA (Master Services Agreement), order form, renewal, DPA (Data Processing Addendum), vendor agreement.

- Risk signals: deal value, data sensitivity (e.g., PII—Personally Identifiable Information), jurisdiction, unusual terms (e.g., unlimited liability), new template vs. third-party paper.

- Tiers:

- Tier 1 (low risk): standard NDAs, renewals with no material changes.

- Tier 2 (moderate): order forms on your paper, minor deviations.

- Tier 3 (high): DPAs, security-heavy deals, indemnities, or high-value MSAs.

Document a simple rubric (e.g., a 10-question checklist) that scores requests into Tiers 1–3. This rubric becomes the spine for routing, SLAs (Service Level Agreements), and automation.

Design a Friction-Light Intake Form and Routing Rules

Build a single front door for all contract work. Keep the form concise but structured so AI can act on it.

Include fields for:

- Business owner, department, and Salesforce/CRM link

- Counterparty name and contract type

- Deal value and critical dates

- Data involved (customer, employee, none)

- Required docs (third-party paper upload or choose your template)

- Jurisdiction and renewal/termination terms (if known)

Routing examples:

- If Tier 1, auto-issue approved template; set 4-hour SLA.

- If Tier 2 with third-party paper, assign to contracts pod; set 2-day SLA.

- If Tier 3 or data flagged, require security review and counsel approval; set 5-day SLA.

On Sandstone, this looks like a single intake connected to CRM and procurement tools, with modular rules that push requests to the right queue the moment they’re submitted.

Encode Playbooks Into Machine-Readable Positions

Turn playbooks from PDFs into structured decision logic that AI can apply.

- Positions: for each clause (liability cap, governing law, assignment), define default, fallback, and redline boundaries.

- Triggers: “If deal value > $250k, require liability cap ≥ fees paid” or “If PII processed, attach DPA and require SOC 2 evidence.”

- Approvals: map who can approve exceptions by clause and variance level.

When playbooks live as data, AI can draft, flag deviations, and request approvals automatically—turning institutional knowledge into a living control system.

Automate Triage, Drafting, and Approvals With AI Agents

AI (Artificial Intelligence) agents amplify humans when given clear inputs and guardrails.

Typical agent flow:

1. Classify request and assign risk tier from the intake form and uploaded paper.

2. Suggest the right template and pre-fill parties, dates, and commercial terms from CRM.

3. Compare third-party paper to your clause library; generate an issues list with recommended positions.

4. Request lightweight approvals in Slack/Teams when a position exceeds playbook limits.

5. Track SLAs and nudge stakeholders if a queue ages beyond thresholds.

Example patterns:

- NDA Fast Lane: auto-issue mutual NDA on your paper with e-sign ready; turnaround drops from days to minutes.

- DPA Guardrail: if data flagged, attach standard DPA, require security artifact upload, and route to privacy counsel only if variances exceed preset boundaries.

On Sandstone, layered data and modular workflows let each decision compound—every approved deviation becomes learnable context for the next similar deal.

Measure What Matters and Iterate Weekly

Define a small set of metrics. Review them in a 20-minute weekly standup.

- Median cycle time by tier

- Auto-approval/auto-draft rate (Tier 1 and 2)

- First-pass acceptance rate of your paper

- SLA adherence and queue age

- Percentage of requests resolved with no attorney touch

- Rework rate (number of back-and-forth turns)

Set targets such as: 70% of NDAs fully automated, 40% reduction in median cycle time for Tier 2, >90% SLA adherence. Publish a simple dashboard so sales, procurement, and legal see the same reality.

A 30-Day Pilot Plan

Keep scope tight. Prove value, then expand.

Week 1: Map top three request types; finalize the risk rubric and SLAs.

Week 2: Build the intake form; connect CRM and e-sign; import clause library.

Week 3: Encode playbook positions and approvals; launch NDA fast lane.

Week 4: Add Tier 2 order forms; turn on SLA nudges; ship dashboard; collect feedback.

Actionable next step: Choose one workflow—NDAs or low-risk renewals—and run the pilot above. You’ll clarify data fields, expose bottlenecks, and create a repeatable path to automate the rest.

Why This Matters Now

When intake, playbooks, and approvals are standardized, AI can safely do more of the heavy lifting. Legal shifts from reactive email triage to a proactive operating system where knowledge compounds with every request. That’s Sandstone’s ethos: strength through layers, crafted precision, and natural integration with how your team already works.

Key takeaways:

- Standardize contract intake around a clear rubric and SLAs.

- Encode playbooks as data so AI can draft and route with guardrails.

- Start with an NDA fast lane, measure weekly, and expand in layers.

Ready to operationalize this? Get the checklist: AI Intake Pilot Starter Kit—or talk to an expert about turning your contract intake into the bedrock of trust and growth.