AI
Test AI GTM Workflows Like Software Before Anyone Trusts the Output
AI and automation workflows in go-to-market fail quietly: they produce well-formatted output that is wrong. Treat them like software. Check every field definition against the source API, assert sanity bounds (no rate above 100%), run in test mode before real sends, and gate each phase with a review. In one reporting pipeline those checks caught a 115.9% click rate before it reached the CEO. The true figure was 13.9%.
Key Takeaways
- Automation errors look like real numbers. Clean formatting is no evidence of correctness.
- Verify field definitions against the source system's schema, never the field's label.
- Write tests for impossible values, and investigate them instead of capping them.
- Ship with a test-mode switch so nothing reaches a customer or an executive before approval.
Why GTM automation fails quietly
A broken script in engineering usually crashes. A broken GTM workflow usually keeps running. It enriches contacts with the wrong company, scores leads on a stale field or reports a metric with the wrong definition, and the output looks as polished as it did when it was right. Large language models add one specific risk: they write fluent, confident text whether or not the input was correct.
The fix is the discipline engineering teams already use: tests, staged rollout and review gates.
The 115.9% click rate
In an automated daily email report built for a B2B newsletter program (case study 05), one campaign showed 401 unique clicks from 346 delivered emails, a 115.9% click rate. A dashboard would have displayed it. A less careful script might have capped it at 100% and moved on.
Instead the field definitions were checked against Mailchimp's own API schema. 'Unique clicks' turned out to count per-link unique clicks summed across every link in the email, rather than unique people. Switching to the subscriber-level field gave the true click rate: 13.9%. The impossible number was the visible symptom of a definition error that would have inflated every report after it.
Five tests every GTM workflow needs
- Definition tests. For every metric, confirm what the source field actually counts, from the API documentation.
- Sanity bounds. Rates between 0 and 100%, no dates in the future, no count larger than the population it comes from.
- Fixture tests. Run the workflow on a small, known dataset where the right answer can be calculated by hand.
- Test mode. A switch that routes every send to an internal address until someone approves the real run.
- Drift checks. Compare today's output with yesterday's and flag jumps too large to be real.
The reporting pipeline above shipped with 75 automated tests across 21 delivery phases, each approved before any code was written, and ran unattended for 5+ days at launch on a scheduled GitHub Actions job.
Where people stay in the loop
Testing catches mechanical errors. Judgment errors, such as a message that is accurate but wrong for the account, need a person. That is why outbound drafts should be approved by a human before they send (Human-in-the-Loop Agents), and why account scores should come from explicit rules anyone can inspect.
A useful split: automate what can be tested, and review what must be judged.
How to start
Pick the one automated number your team relies on most, often pipeline, MQLs or email engagement. Write down what each input field means, check that against the source system, and add a single bound test. That hour usually finds something.
Clarafold builds workflows this way by default, from lead enrichment to RevOps reporting.
Put It To Work