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Function: CRM hygiene

AI Workflow for Stale Opportunity Cleanup

Deployment Brief

Start with a weekly stale-deal report showing last touch, stage age, missing next step, owner, recommended action, and approval status.

Related Field Report

• AI workflow readiness checklist: A field report on checking workflow clarity, evidence, ownership, and measurement before implementation.

Quick Answer

Stale opportunity cleanup finds deals with no recent activity, outdated close dates, missing next steps, weak owner accountability, or unclear revive/loss status. AI should prepare a recommended action and evidence, not close, revive, reassign, or forecast deals automatically. A person should review stage, amount, close date, owner, forecast, loss reason, and any customer outreach.

TL;DR

Stale deals make the pipeline look healthier than it is. The workflow should flag old deals and recommend action, while managers approve stage, forecast, amount, and close-date changes.

What is stale opportunity cleanup?

Stale opportunity cleanup is the operating process for finding open deals that no longer reflect real pipeline status.

Who is this workflow for?

• Companies where sales, marketing, service, and reporting all depend on the CRM.
• Teams preparing to use more AI automation but still fighting duplicate, stale, incomplete, or inconsistent records.
• Owners who need cleaner data without giving automation permission to damage customer history.
• Service businesses, agencies, SaaS companies, consultants, and professional firms where every missed follow-up or bad handoff has revenue impact.

What breaks in the manual process?

The manual process breaks when the CRM is cleaned as a one-time project instead of an operating routine:

• deals sit open with no next step;
• close dates roll forward without evidence;
• forecast includes deals the owner has not touched;
• lost deals stay open because no one wants to close them;
• revive outreach happens without checking whether the deal is real.

The goal is not a prettier database. The goal is a CRM that can support routing, follow-up, reporting, forecasting, and safe automation.

How does the AI-enabled process work?

The workflow checks CRM records against approved standards, prepares a correction or review queue, shows the evidence, and separates safe suggestions from changes that need approval.

AI can identify patterns faster than a person reviewing records one by one. It should still stop before changing ownership, consent, activity history, deal stage, amount, forecast, customer commitments, or any field that affects routing and reporting.

What does this look like in practice?

Example scenario: A pipeline review shows several proposal-stage deals with no next step and close dates that passed last month. The workflow checks stage, stage age, last activity, close date, next step, owner, amount, forecast, and revive or loss reason. It prepares stale-deal report, recommended action, owner task, manager review item, and a flag for any forecast-impacting change.

What decision rules should govern this workflow?

• Flag an opportunity when stage age, last activity, or close date violates the team standard.
• Recommend revive, update, reassign, close-lost review, or manager review based on evidence.
• Route stage, amount, forecast, owner, close date, and loss reason changes to review.
• Do not trigger customer outreach from stale data without owner approval.
• Block cleanup when there is recent activity not reflected in the CRM.

What are the implementation steps?

1. Trigger: A scheduled pipeline hygiene review finds open opportunities with old activity, outdated close dates, missing next steps, or stage age beyond the team standard. 2. Inputs collected: opportunity stage, stage age and last activity, close date, next step and due date, owner, amount and forecast category, revive reason or loss reason, manager approval rule. 3. AI/system action: The system checks the record against the data standard, prepares the suggested output, and flags conflicts or protected fields. 4. Human review point: The opportunity owner or sales manager reviews closing deals, changing stage, changing forecast, changing amount, moving close dates, reassigning owner, and triggering customer outreach. 5. Output generated: stale opportunity report, recommended action, owner task or manager review item, close-date, stage, or loss-reason exception, measurement event for stale count, revived deals, closed-lost cleanup, and forecast correction. 6. Follow-up or next action: The owner approves, rejects, revises, merges, assigns, updates, blocks, or logs the record based on the evidence.

Required inputs

• opportunity stage.
• stage age and last activity.
• close date.
• next step and due date.
• owner.
• amount and forecast category.
• revive reason or loss reason.
• manager approval rule.

Expected outputs

• stale opportunity report.
• recommended action.
• owner task or manager review item.
• close-date, stage, or loss-reason exception.
• measurement event for stale count, revived deals, closed-lost cleanup, and forecast correction.

Human review point

The opportunity owner or sales manager reviews closing deals, changing stage, changing forecast, changing amount, moving close dates, reassigning owner, and triggering customer outreach.

Risks and stop rules

Stop when the source of truth is unclear, the match evidence is weak, a protected field would change, the update affects revenue or routing, activity history could be lost, consent could be overwritten, or the record is tied to an active customer or opportunity.

Best first version

Start with a weekly stale-deal report showing last touch, stage age, missing next step, owner, recommended action, and approval status.

Advanced version

Add source-priority rules, confidence bands, protected-field policy, recurring exception review, import prevention, sync monitoring, and manager dashboards after the first version has been reviewed on real CRM records.

Related workflows

• Sales Pipeline Review
• Deal Risk Detection
• Pipeline Forecasting
• Next Step Enforcement
• CRM Cleanup

Measurement plan

• Stale opportunity count.
• Missing next-step rate.
• Close-date correction rate.
• Revived opportunity count.
• Closed-lost cleanup count.
• Forecast correction count.

FAQ

What is stale opportunity cleanup?

Stale opportunity cleanup identifies open deals with old activity, outdated close dates, missing next steps, or stage age beyond the team standard.

What should AI recommend for stale opportunities?

AI can recommend update, revive, reassign, close-lost review, manager review, or owner follow-up based on CRM evidence.

What should stay under human review?

Closing deals, changing stage, changing forecast, changing amount, moving close dates, reassigning owners, and outreach should stay under review.

What is the simplest first version?

Start with a weekly stale-deal report showing last touch, stage age, missing next step, owner, recommended action, and approval status.

How should stale opportunity cleanup be measured?

Track stale count, missing next steps, close-date corrections, revived deals, closed-lost cleanup, and forecast corrections.