Moving a healthcare technology ABM playbook out of people's heads and into supervised agents
The ABM playbook worked, but only when its authors ran it. A Lead Gen AI Twin encodes their rules, and they keep the decisions that need judgement.
- Challenge
A working ABM playbook depended on a few senior people's memory.
- Approach
Encode ICP, selection, messaging and qualification rules into connected agents with decision gates.
- Result
Agents run the repeatable steps; senior people approve lists and qualify opportunities.
The situation
A healthcare technology company has an ABM playbook that works, but it lives with a small group of senior people. They know which health systems to pursue, why a new CMIO or EHR upgrade matters, and what opens a conversation with a CIO. When they are busy or leave, account selection drifts, outreach turns generic and qualification becomes a matter of opinion. Most of the playbook has never been written down, so it cannot be taught, checked or scaled.
The question that mattered
Can the team's ABM judgement be written down and run by agents, while the people who make the hard calls keep making them?
What we did
- 1
Extract the playbook
Interview the senior team and review won and lost deals to write down the ICP, account selection rules, trigger events and qualification criteria.
- 2
Encode the messaging patterns
Map what works with CIOs, CMIOs, revenue-cycle leaders and value-analysis committees, including their objections on integration, security and clinician adoption.
- 3
Configure the connected agents
Set up agents for account selection, trigger monitoring, persona-specific drafting and qualification, wired into the CRM, email, LinkedIn and calendar.
- 4
Set the decision gates
Keep humans at target-list approval, message approval and opportunity qualification. Agents start in draft-only mode and earn autonomy task by task as reviewers agree with them.
What we delivered
The evidence and working documents the team kept.
- Written ABM playbook: ICP, selection rules, triggers and qualification criteria
- Persona messaging patterns and objection notes for health-system buyers
- Connected agents for selection, monitoring, drafting and qualification
- Decision-gate map showing who approves what, and when
- Autonomy log recording which tasks agents may run without review
The outcome
Agents
apply the selection rules and surface health systems with a current trigger
Drafts
follow the persona patterns the senior team used to write by hand
Senior people
approve target lists and qualify opportunities at set gates
Qualification
follows written criteria rather than individual memory
Debates about whether a health system is worth pursuing now start from written rules anyone on the team can check.
What changed in the GTM motion
The ABM playbook exists as written rules and running agents, not as knowledge held by a few people. New team members learn from the same rules the agents follow, and senior time goes to the gates where judgement matters.
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