AI Governance20
Evidence Quality 5
How to Audit an AI ROI Calculation
An AI ROI audit involves validating time-saved claims against objective baselines, deducting all implementation and rework costs, and grading the evidence quality of the results to ensure a true net return.
Read blog articleHow Much Evidence Is Enough to Prove AI ROI?
Evidence is sufficient when net value—labor savings minus total adoption costs—is validated against objective historical or cohort baselines rather than subjective estimates. High-quality evidence requires a 'Verified' grade, accounting for implementation, training, and rework to justify an 'Expand' or 'Stop' decision.
Read blog articleSelf-Reported vs Observed AI Savings
Self-reported savings rely on subjective employee estimates that often inflate ROI, whereas observed savings use objective baselines to measure the actual time delta and net labor value of work.
Read blog articleAI Estimates vs Measured Evidence
AI estimates represent theoretical potential based on assumptions, while measured evidence uses objective baselines to calculate the actual net return of AI adoption. The goal is to move from 'Estimate Only' to 'Verified' evidence quality by tracking the delta between historical work time and AI-assisted task completion.
Read blog articleWhat Is Evidence Quality?
Evidence Quality is a grading system that measures the reliability of AI value claims, ranking objective baselines and historical data above subjective self-estimates.
ReadAI Procurement & Vendor Evaluation 4
AI Contract Cost Checklist
A complete AI contract checklist accounts for licences, usage fees, implementation, and training, alongside the ongoing costs of human review and rework. True net value is only realised when the labour value of time saved exceeds these total costs.
Read blog articleAI Pricing Evaluation Framework
An effective AI pricing evaluation framework measures net value by subtracting the total cost of ownership—including licenses, training, and rework—from the labor value of realized time savings. This shift from per-seat costs to deterministic value allows procurement teams to categorize spend into five decision states: Expand, Continue, Review, Improve, or Stop.
Read blog articleAI Procurement Checklist
Successful AI procurement focuses on net value: the labour value of verified time savings minus the total cost of ownership, including implementation and rework. It requires a move away from license-counting toward evidence-based decision states like Expand, Review, or Stop.
Read blog articleAI Vendor Evaluation Scorecard Guide
An effective AI vendor evaluation scorecard prioritizes realized net value by subtracting the full cost of adoption from the labor value of time saved. It replaces subjective feature checklists with deterministic metrics like payback periods and evidence quality grades.
ReadTrust and Surveillance 4
No-Screen-Tracking AI Measurement
AI value is measured by calculating the difference between the time a task takes without AI and the time it takes with AI, avoiding any need for screen tracking or keystroke logging. This methodology focuses on work outcomes and net value rather than individual employee monitoring.
Read blog articleHow to Report AI Value Without Individual Scores
AI value is reported by measuring the net labor value of time saved across specific work categories rather than tracking individual productivity. This approach uses deterministic models to compare time spent on work against objective baselines, deducting the full cost of adoption to find the net return.
Read blog articleAI Time Evidence Without Surveillance
Organizations can prove AI time savings by measuring the duration of tracked work against objective baselines rather than monitoring individual activity. This approach quantifies net value and payback without using intrusive surveillance like screenshots or keystroke logging.
Read blog articleMeasure AI Productivity Without Employee Monitoring
AI productivity is measured by comparing the time taken to complete work with AI against objective historical baselines, focusing on work output rather than individual activity. This methodology eliminates the need for invasive surveillance like screen tracking or keystroke logging by prioritizing the net value of time saved.
ReadAI Performance Review 2
When to Improve an AI Workflow
An AI workflow should move to the 'Improve' state when the labour value of realised time savings is offset by high rework costs or when evidence quality suggests current savings are based on unreliable estimates.
Read blog articleWhen to Review an AI Tool
An AI tool should be reviewed when its current net value is negative, its payback period exceeds projections, or the evidence quality for reported time savings is low.
ReadAI Risk and Waste Reduction 2
AI Tool Consolidation Framework
An AI tool consolidation framework prioritizes software by its net value—calculated as labour value of time saved minus total adoption costs—and assigns decision states like Expand or Stop based on evidence quality. This allows organizations to move from subjective estimates to deterministic value management.
Read blog articleWhen to Stop an AI Tool
An AI tool should be stopped when its current net value—the labour value of realised time saved minus all costs—is consistently negative and the evidence for future value is weak. Decisions are based on deterministic value models and evidence quality grades rather than sentiment.
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