How to Build a Mutual Action Plan with AI
A Mutual Action Plan (MAP) — also called a Joint Execution Plan or Customer Success Plan — is a shared document that defines every step required to move a deal from verbal agreement to signed contract and successful implementation. It's agreed with the prospect, not imposed on them.
In complex SaaS sales, MAPs are one of the most reliable closing tools available. They create shared ownership of the timeline, surface procurement and legal complexity early, engage the buying committee actively, and give champions a concrete artefact to manage internally.
AI significantly reduces the effort required to build, maintain, and use MAPs effectively — making them accessible for every deal in the pipeline rather than just the largest strategic accounts.
What a Mutual Action Plan Contains
A well-structured MAP has four sections:
1. Shared GoalThe business outcome both parties are working toward — specific to the prospect's stated pain and metrics. Not "implement the platform" but "reduce forecast error rate from 22% to under 8% by Q3, enabling the CFO to approve headcount with confidence."
2. Key MilestonesThe major stages between current state and goal achievement — not just sales milestones but implementation milestones that matter to the prospect. Example milestones: technical discovery complete, procurement approval received, contract signed, data migration complete, team trained, first revenue outcome measured.
3. Action items by ownerEvery action required to reach each milestone, with a named owner (rep or prospect-side) and a committed date. This is the working core of the MAP — the specific tasks that convert a verbal commitment into a closed deal and a successful implementation.
4. Decision dependenciesThe decisions the prospect needs to make internally, with who needs to make them and what information they need to do so. This section surfaces procurement, legal, and EB approval requirements that are often invisible until late in the cycle — the moment when they become deal-killers.
How AI Builds the Initial MAP Draft
Building a MAP manually from scratch takes 45–90 minutes per deal. AI reduces this to minutes.
Step 1: Extract deal context from call and CRM dataAI reads the accumulated call transcripts, CRM notes, and email history for the deal and extracts: the prospect's stated business goals, the pain points confirmed in discovery, the timeline discussed, the stakeholders identified, and the technical and commercial requirements mentioned.
Step 2: Generate the shared goal statementAI drafts the shared goal using the specific metrics and language the prospect has used in calls — not generic language, but the CFO's actual concern and the VP RevOps's specific success definition.
Step 3: Generate milestone listBased on the deal type, product category, and typical implementation complexity (drawn from historical data), AI generates a milestone list calibrated to the specific deal's timeline and complexity.
Step 4: Populate action itemsAI extracts every next step mentioned in calls and emails — with owner attribution and date references — and populates the action item list. Items without a clear date or owner are flagged for the rep to complete.
Step 5: Flag missing elementsAI identifies what's missing from the MAP based on the deal's MEDDPICC status. No procurement contact identified? Flags "Procurement owner to be introduced" as an open action. No legal review timeline established? Flags "Legal process timeline — agree with [Prospect name]." No EB engagement on MAP? Flags "EB presentation / MAP review meeting to be scheduled."
The rep receives a complete first draft in the time it takes to finish the post-call summary — and the draft is specific to this deal's context, not a generic template.
How AI Maintains the MAP Through the Deal
A MAP that's built once and never updated is worse than no MAP — it creates false confidence and an inaccurate picture of where the deal stands.
AI maintains the MAP continuously:
After every call: Extracts completed action items, updates milestone progress, identifies new actions arising from the conversation, and flags any changes to the prospect's stated timeline or requirements. Between calls: Monitors email content for MAP-relevant updates — procurement introductions, timeline changes, stakeholder additions, or commitment confirmations. Risk monitoring: Alerts the rep when MAP action items owned by the prospect are approaching their due date without evidence of progress. A prospect who committed to getting EB approval by Friday and hasn't sent the confirmation email by Thursday morning is a risk signal. Version tracking: Maintains a version history of the MAP so the rep can see how the deal's shape has changed over time — which is both an intelligence tool and a legal protection in cases where verbal commitments are later disputed.Using the MAP to Multi-Thread
One of the highest-value uses of a MAP in complex deals is using it as a reason to engage stakeholders who haven't yet been part of the conversation.
AI generates personalised MAP review invitations for stakeholders who need to be engaged:
For the EB: "The MAP shows that your approval will be needed at [milestone]. We'd like to schedule 30 minutes to walk you through the plan and get your input on the timeline."
For procurement: "We're at the stage where the contract terms and vendor registration process need to begin. Can you introduce us to the procurement contact so we can build this into the MAP timeline?"
For legal: "We'd like to include legal review in the MAP. What's a realistic timeline for the contract review process from your end?"
Each of these is a genuine reason to engage a new stakeholder — not a cold introduction, but a specific request rooted in shared commitment to the timeline.
How Brazn Supports MAP Creation and Management
Brazn generates MAP drafts from deal context automatically, maintains MAP accuracy from call and email content, and monitors MAP progress as a deal health signal. Deals where MAP action items are being completed on time by both sides score significantly higher in Brazn's deal health model than deals where the MAP exists on paper but isn't being actively maintained.
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About the Author
Alex Margarit, Sales AI Expert, SaaS Sales Leader, BMC, ServiceNow, Docusign — 25+ years in SaaS sales.
