The core idea
Unit economics and payback narratives turn "shut it down" into "how do we scale?"
Costs rose until leadership threatened to kill the LLM feature. "Shut it down" was on the table. We rebuilt unit economics: cost per successful outcome, adoption, deflection rate. A clear payback narrative and evidence turned the conversation around.
Anonymized but real
Names and identifying details are removed. The process and outcomes are preserved.
Executive summary
The client had an LLM feature (customer support deflection) in production. Costs rose; leadership questioned ROI. "Shut it down" was on the table. We rebuilt unit economics: cost per successful outcome, adoption, deflection rate. We showed the payback—deflection reduced support load, which had a clear cost. The feature turned positive ROI. Leadership continued investment.
This usually starts with LLM cost too high: leadership sees spend before they see value. Use Cost Optimization to connect cost, adoption, and business outcomes before the feature gets cut.
The situation
Before the rescue:
- Cost per resolution: Unknown—only total cost was visible
- Adoption: Unclear—how many users were using it?
- Deflection rate: Unmeasured—how many support tickets did it avoid?
- Payback: No narrative—leadership saw cost, not value
What we did
We built an LLM cost optimization and unit economics framework:
- Cost per successful outcome: Total cost / successful resolutions
- Adoption: Users and sessions using the feature
- Deflection rate: Support tickets avoided (estimated from usage and intent)
- Payback: Cost of avoided support tickets vs. LLM cost
Unit economics
We framed the metrics so leadership could see the tradeoff:
- Cost per successful outcome: $X per resolved query
- Cost per support ticket (avoided): $Y
- Deflection rate: Z% of queries that would have been tickets
- Payback: When Z% × $Y > $X, the feature is net positive
Payback narrative
We built a simple dashboard: cost per successful outcome, deflection rate, and payback. Leadership could see that the feature was cost-positive when deflection was measured correctly. The "shut it down" conversation turned into "how do we scale adoption?"
Why this worked
Leadership needed evidence, not opinions. Unit economics gave them a clear framework. The payback narrative showed that the feature was worth keeping—and scaling.
Next steps
If your LLM feature is under cost pressure and ROI is unclear, an AI system audit can baseline unit economics and build a payback narrative. We help teams measure cost per successful outcome, adoption, and deflection—and turn the conversation around after clarifying the symptom on the cost pain page.
ROI under pressure?
We build unit economics and payback narratives for LLM features—so leadership can see the value. LLM cost too high is usually where this conversation starts.
Lead magnet
Before/After Benchmark Template (CSV + rubric) — Track cost per successful outcome and payback. Request it.
What made this hard
Measuring deflection rate—estimating support tickets avoided from usage data.
What made this work
Clear unit economics: cost per successful outcome, adoption, deflection, payback.
Need an LLM ROI rescue?
If your LLM feature is under cost pressure, our AI audit builds unit economics and payback narratives after isolating the real cost pressure on this pain page.
Last updated
February 1, 2026
