Executive AI Strategy · 2026-10-29 · 2 min read
What Is an Agent Leverage Map?
An agent leverage map is a diagnostic that shows where AI agents will create the most value in a company's revenue and marketing work, so leaders can choose the right pilots first. Here is how it works and how to start.
An agent leverage map is a diagnostic that shows where AI agents will create the most value in a company's revenue and marketing operations, so leaders can choose which agent pilots to run first. Linda Vu Nguyen developed the Agent Leverage Map at Skylark 118 as the first phase of the Executive AI Ramp. It updates her 2025 Revenue Impact Assessment framework for teams that now deploy agents as well as AI tools.
Why it matters
Most AI pilots get chosen by projected ROI. ROI can only be measured after something is built, which makes it a poor guide for deciding what to build first. In The ROI Trap That's Stopping AI Pilots From Scaling, Linda compares it to "using a speedometer to decide which route to take."
An agent leverage map asks a question a team can answer today: where in the revenue architecture would an agent take on the most valuable work? The answer points to pilots that tie back to revenue and that teach the team the most, the fastest.
How it works
- Map the revenue architecture. Draw how the business makes money, from total revenue down to the drivers and the processes behind each one. Skylark 118 calls this a revenue tree.
- Find the leverage points. Mark the processes where an agent could take on repetitive, rules-based, or research-heavy work that slows a revenue driver today.
- Score readiness. Rate each candidate on impact, confidence, and effort. Data quality and governance needs count toward effort.
- Choose a pilot portfolio. Pick a small set of pilots and rank them by learning velocity, meaning how quickly each one will show the team what works.
- Flag trust requirements. Note which pilots touch customers, money, or compliance, so the trust layer for those agents is designed before they go live.
The output is a prioritized plan the team can act on with or without outside help.
An example
The following is a hypothetical illustration of how the map changes a decision.
A B2B software company's marketing team plans to start with an AI agent for content production. Mapping the revenue tree shows that pipeline depends heavily on how fast new inbound leads are researched and routed to sales, and that step takes days of manual work. An agent that researches and routes leads scores higher on impact and learning velocity than a content agent. Its risk is contained, because a person still owns the outreach. The routing agent becomes the first pilot, and the content agent moves to the second wave.
How to start
Answer three questions from the Skylark 118 entry diagnostic:
- Can you draw your revenue architecture on a whiteboard?
- Do you know which revenue drivers have the highest AI leverage potential?
- Are you selecting pilots based on learning velocity, or on financial projections alone?
If any answer is no, build the map before adding more tools. Skylark 118 runs the Agent Leverage Map as a two-week sprint, the entry point to the Executive AI Ramp. For the thinking behind it, see Demystify AI for Leaders.