What evidence moves a deal forward?
Write down the required evidence for each stage and who can override it. Check whether the CRM can represent those rules in a way people will actually maintain.
Field notes / Sales operations / 01
Design a pipeline AI can read without guessing.
When stage definitions drift and records go stale, AI output can sound confident while missing the context your team needs. Start with the handoffs and data rules behind the recommendation.
1
Start with one recurring sales workflow.
Explore the workflow checksA relationship map / illustrative example
A decision path to examine, not a product interface.
01 / Workflow checks
Before the suggestion, inspect the record.
Choose a common task, such as preparing an opportunity for a pipeline review. Walk through the information an AI tool would see, what it might suggest, and who should decide what happens next.
Team exercise / 20 minutes
Trace one opportunity from source to decision.
Use a real record in your own environment. Note where a reviewer would need to ask a colleague for missing context.
-
Agree on the stage
Compare the recorded stage with your exit criteria. If teams interpret “qualified” differently, an AI summary or forecast may inherit that ambiguity.
-
Check freshness and gaps
Look at the last meaningful activity, next step, owner, and close date. A complete-looking record can still describe an old conversation.
-
Find conflicting context
Compare notes with structured fields and related records. Duplicate contacts or inconsistent account associations can lead to an incomplete or misleading suggestion.
-
Set the approval boundary
Consider allowing a draft summary for review, while requiring a person to approve stage changes, customer messages, or updates to shared records.
02 / Discovery questions
Make the exceptions part of the conversation.
Bring sales operations, a frontline manager, and a representative into the same review. Ask these questions against a specific workflow rather than an idealized process.
Can a reviewer see where a suggestion came from?
Ask vendors how source information, missing fields, and uncertainty are presented. Test whether a manager can distinguish recorded facts from generated interpretation.
What happens when the context is wrong?
Explore how someone can reject or correct a suggestion, and whether proposed changes can be reviewed before they affect shared records or reach a customer.
Handoff example
Representative → manager: A draft opportunity summary flags a possible next step. The representative checks the source notes and corrects missing context. The manager reviews the evidence before deciding whether the stage should change.
03 / Integration readiness
Follow the data across the handoff.
A useful evaluation goes beyond a connection being available. Map where each piece of context originates, how it arrives in the CRM, and who owns the correction when it is wrong.
List the inputs that matter
Identify where account details, activity notes, and opportunity updates are entered. Ask which sources an AI workflow can access and which important context remains outside its view.
Compare meanings, not just names
Check whether fields carry the same meaning across systems. Document the source of truth for owner, stage, and next step before evaluating any automated update.
Match access to the task
Discuss which roles should see source records, review suggestions, and approve changes. Ask how a proposed tool handles information that a reviewer should not access.
Plan for a bad update
Ask how changes are logged, how an error can be traced, and what reversal requires. Assign a person to investigate discrepancies between connected systems.
04 / Take it to your team
Leave with a decision, not just a tool shortlist.
Pick one workflow, name its data owner, and agree on the actions that need human approval. Use those decisions as criteria when comparing CRM software and AI capabilities.
Revisit the discovery questions