AGENTS · AUTOMATION · WORKFLOWS
AI Growth Automation: connecting data, decisions and action
AI growth automation is the systems layer between information and action: retrieve the right data, apply rules or AI reasoning, route the output and keep a human in the loop where judgement matters.
What it means
I use automation to remove repetitive handoffs and make useful context available at the moment a person needs it. The goal is not autonomous marketing for its own sake. It is a clearer, more reliable workflow.
The problem it addresses
Growth teams often have plenty of tools but weak connective tissue. Research lives in one system, CRM history in another, follow-up in email and the decision still happens manually. Automation is useful when it joins those steps without hiding how the decision was made.
How I approach it
I start with a bounded workflow, define the inputs, decision points and output, then choose the simplest toolchain that can support it. My current work includes Claude plugins and agents, Clay, HubSpot Breeze AI agents and automation platforms including n8n, Make and Zapier.
What this looks like in practice
Tools and systems
Results and current status
Contact Intel, the 146-account intelligence agent and EngineRoom are documented builds. My CV also records a pilot of the HubSpot AI Nurture Agent on Tier 1 and Tier 2 nurture segments.
I separate a working workflow from a proven commercial outcome. Unless a time-saving, adoption, conversion or revenue effect has been measured, I describe it as an intended benefit rather than a result.