Where does your company sit on the path to self-driving GTM?
The four-level framework Relevance uses with enterprise GTM leaders. For senior teams with ambitious growth targets.
Where most teams are today.
Account research, ICP fit, and messaging templates are encoded. Reps run plays the system has already shaped.
You may be at L3 for one use case and still at L1 for another. The diagnostic maps each independently.
Four levels.
One road.
Most GTM teams sit between Level 1 and Level 2 on their core use cases. It's normal to be further along on one and behind on another. Each level shifts where the human spends time and how much leverage the system delivers. Start wherever you are. Relevance grows with you.
Assisted
Someone on your team opens an AI tool and gives it a task. Here, the human is directing every interaction.
Copilot
Repeatable workflows start to get encoded. Knowledge and processes start moving into a system but relies on a human to kick things off.
Autopilot
Proven workflows wired to triggers. Agents run autonomously. The review model shifts from inspecting outputs to governing the system that produces them.
Self-Driving
The system extracts patterns, creates new workflows, and improves itself. It stays model-agnostic, choosing the right model for each task to keep cost in check.
Here's how each use case is scored.
Every use case gets broken down into these six ingredients. Together they determine the level of the use case itself.
Most use cases are stuck in the messy middle.
Across enterprise GTM teams, most use cases sit at Level 1 to 2. Many teams have at least one use case at Level 3 already. AI's in the workflow, but the work hasn't fundamentally changed.
is the typical ceiling on enterprise copilot adoption rates. Most teams stay stuck at L1 or L2, with no scalable path to L3 and no measurable ROI.
↗ Forrester Copilot Reality Check, 2024of enterprises report negative outcomes from disconnected AI tools.
↗ Zapier AI Sprawl Survey, 2024Send Payments moved from L1 to L3 in six months.
Send Payments started where most enterprise teams are: AI tools sitting alongside the work. Within six months they had named agents running customer response, call QA, and CRM admin end-to-end. Here's how their level changed.
"If you can write a job description for what you want to happen, you can create an agent for it." — Send PaymentsRead the full Send Payments story →
See where your team sits.
- Live mapping against the L1 to L4 framework
- A use case teardown of your choosing
- A specific next step, even if Relevance isn't the right fit