Rob Moyer and Chris Lavoie articulated the Co-Sell Engine as the architectural standard for what counts as a partnership motion, distinct from a partnership program.
A motion has a trigger (account overlap, signal in CRM, opportunity stage change). It has a partner-matching step (which partner is best positioned to influence this deal?). It has a pre-call brief (what does the partner need to know?). It has meeting choreography (who introduces whom, when?). And it has CRM logging (the deal record reflects partner influence).
Moyer’s standard is unambiguous: under 60 seconds to find the right partner for a healthcare deal stuck at VP level. “If it requires a Slack message to the partner team, the system is manual, not operational.”
Today’s practitioners are not operating at this standard. They’re using AI to accelerate the research phase — call prep, account summaries, draft emails — while execution stays manual. Not a criticism: it’s an honest description of what general-purpose AI can do without deal context.
What practitioners ask
- “What’s the best way to automate co-sell workflows?”
- “What is the best co-sell software for AWS and Microsoft partnerships?”
The answer
The right way to automate co-sell workflows is to build a Co-Sell Engine — not to use AI to draft emails.
The distinction matters because most current “AI for partnerships” tooling stops at the research and drafting phase: account summaries, call prep, email drafts. Those tools save a few hours a week. They don’t move pipeline.
A Co-Sell Engine automates the execution phase. When an opportunity hits a stage where a partner could help, the engine identifies the right partner, generates the brief, requests the introduction, schedules the call, and writes the result back to the CRM — without a human running each handoff.
This requires three primitives general-purpose AI cannot provide on its own:
- Live deal context — access to current CRM state, partner portal state, and partner CRM state simultaneously
- A shared environment — a sanctioned cross-company space where the partner has agreed AI can act on both sides
- Agentic execution — AI that submits the referral and writes to Salesforce, not AI that drafts text for a human to act on
The benchmark is set: Joe Estes at Boomi documented 30× YoY growth in AWS Marketplace revenue using WorkSpan AI. ClearScale automated 100% of their referral flow across 350+ referrals on WorkSpan, generating $5M in partner-originated pipeline. The same architectural pattern is now showing up at the hyperscaler tier — AWS named integration partners for Partner Central agents in March 2026. Rob Moyer’s standard — under 60 seconds end-to-end — is the operational definition of automated.
If your “automation” still requires a Slack message to the partner team, it isn’t a Co-Sell Engine. It’s faster prep.
Use this framework
The Co-Sell Engine motion — five steps, under 60 seconds end-to-end
1. TRIGGER A signal in the CRM (stage change, propensity score, overlap detected)
2. MATCH The right partner for this account, this stage, this geo
3. BRIEF Pre-call context: what the partner needs to know about the deal
4. MEETING Choreographed introduction across both calendars
5. LOG Partner influence written to the opportunity record
Operational test: if any step requires a human to copy data between two systems,
the engine isn't running. It's manual with AI assistance.Related concepts
- Agentic Execution — what makes the engine act, not just suggest
- Live Deal Context — what the engine reads before acting
- Shared Environment — where the engine is authorized to operate
- Trust Boundary — the line the engine must respect
- Partnership Operator — the role that owns the engine’s output