AI To GTM Translation Guide
Use this when an AI announcement sounds technical but may change revenue work.
Model Capability
Better reasoning, longer context, lower latency, multimodal input, or lower cost.
GTM question: which workflow becomes faster, cheaper, or accurate enough to test?
Common moves:
- Account research
- Campaign QA
- Call prep
- Support triage
- Content refresh
Review point: customer-facing claims, CRM changes, routing, and budget decisions.
Metric: accepted output rate, cycle time, rework, and conversion quality.
Connector Or Tool Use
The AI can read or act inside another system.
GTM question: what can it read, what can it write, and who approves changes?
Common moves:
- CRM field updates
- Lead routing
- Calendar and email prep
- Campaign build checks
- Support escalation
Review point: write access, external messages, ownership changes, and source data.
Metric: approved changes, rejected changes, error rate, and handoff speed.
Agent Workflow
The AI can complete a multi-step task with tools or memory.
GTM question: where does the agent stop and the operator approve?
Common moves:
- Prospecting recommendation QA
- Account brief creation
- Support conversation scoring
- Offer readiness review
- Workflow verification
Review point: any step that affects a customer, record, campaign, or seller action.
Metric: completion quality, escalation rate, rework, and downstream pipeline impact.
Search Or Answer Surface
AI changes how buyers discover, compare, or act on information.
GTM question: which buyer question now needs a better answer or offer?
Common moves:
- AI search page refresh
- Paid search control loop
- Answer-engine visibility audit
- Offer and proof-point mapping
- Conversion quality review
Review point: unsupported claims, bad citations, wrong audience fit, and low-quality traffic.
Metric: qualified visits, cited-source quality, offer engagement, and downstream conversion.
Pricing Or Packaging Change
AI work is priced by seat, credit, task, resolution, or completed outcome.
GTM question: what counts as a good completed outcome?
Common moves:
- Agent ROI ledger
- Support deflection scorecard
- Sales-assist acceptance review
- Cost per accepted output
- Workflow expansion decision
Review point: vague success criteria, low-quality completions, hidden review time, and rejected outputs.
Metric: cost per accepted outcome, time saved, quality issues, and break-even threshold.
The Operating Rule
Do not ask, "Is this AI impressive?"
Ask:
- What workflow changes?
- Who owns it?
- What input does AI need?
- What output needs review?
- What metric proves it worked?