Risk · Governance

Shadow AI Productivity Leak Calculator

Estimate productivity drag and risk exposure from ungoverned consumer AI tools.

Quantify shadow AI productivity drag dollars and compliance risk narrative for CIO/CISO reviews.

How it works

Estimate % of employees on consumer AI, hours/week, loaded cost, and rework/leak %.
Set compliance risk appetite — annual drag dollars and risk narrative update live.
Use governance quick wins in CIO/CISO reviews; unlock a printable summary with work email.

Usage & cost inputs

Directional shadow-AI model — validate with surveys and proxy logs.

Employees using consumer AI (%) Share of workforce using ChatGPT-class tools without enterprise controls.

40%

Hours / week on consumer AI Average among those using it (prompting, waiting, cleanup).

Hourly loaded cost ($) Fully loaded cost (salary + benefits + overhead) ÷ productive hours. Rework / leak (%) Share of AI-assisted time lost to wrong answers, cleanup, or policy issues.

25%

Headcount in scope Employees in the org unit you are estimating.

500

Compliance risk appetite Shapes the risk narrative — dollars still compute either way.

Productivity leak

Defaults match the form. Change any input — numbers refresh instantly.

Annual productivity drag — Leak hours × loaded cost × 52

Users in scope — Headcount × usage %

Risk exposure narrative — Tuned to risk appetite

Governance quick wins — Enablement over blind bans

Governance note

—

Adjust inputs to see a tailored note.

Directional estimate only — not a forensic audit or legal opinion.

How the model works

Users = headcount × employees using consumer AI %.
Leak hours / week = users × hours/week × rework/leak %.
Annual drag $ = leak hours/week × hourly loaded cost × 52.
Risk narrative emphasizes compliance vs productivity based on appetite.
Quick wins prioritize approved tools, DLP, manager guidance, and intake for high-risk use.

All math runs in your browser. Many orgs channel usage to enterprise tools rather than bans alone.

Unlock printable leak summary

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FAQ

How does the Shadow AI calculator work?

You estimate % of employees using consumer AI, hours/week, loaded hourly cost, rework/leak %, and compliance risk appetite. The model estimates annual productivity drag dollars, a risk narrative, and governance quick wins.

What is rework/leak %?

Share of AI-assisted time lost to wrong answers, policy violations, duplicate work, or cleanup. Even “helpful” tools create drag when outputs need heavy review or rework.

Does this mean ban consumer AI?

Not necessarily. Many orgs move usage to approved enterprise tools with DLP and logging. The calculator surfaces cost and risk so leadership can choose enablement over blind bans.

How does risk appetite change the narrative?

Conservative appetite emphasizes compliance exposure and data leakage. Aggressive appetite emphasizes productivity drag and inconsistent quality. Both still show dollar impact.

What are governance quick wins?

Typical first moves: approved tool list, paste/DLP controls, manager guidance, and a lightweight intake for high-risk use cases. The results panel lists wins matched to your inputs.

Are dollar estimates precise?

No. They are directional for CIO/CISO conversations. Validate with surveys, proxy logs, and sampled manager interviews.

How it works

How the model works

Unlock printable leak summary

FAQ

Shadow AI Productivity Leak Summary