Expand an AI tool only when it achieves a positive net value and the evidence quality of time savings is verified. Expansion decisions should be based on the labor value of realized time savings covering the full cost of adoption, including implementation and rework.
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Expand AI adoption when a tool reaches an 'Expand' decision state based on verified evidence. This happens when the realized net value is positive—meaning the labor value of time saved exceeds the total cost of licenses, implementation, training, and necessary rework.
Avoid scaling based on seat utilization or 'Estimate Only' data. A finance-credible expansion plan requires a clear payback period and a distinction between realized savings and speculative future value.
Realized net value is the labor value of time saved minus the full cost of the AI. Labor value is calculated by multiplying hours saved by the loaded hourly cost, which defaults to $75 per hour in NetLift models unless specified otherwise.
Costs are not limited to license fees. They must include usage, implementation, training, and the time spent on review and rework. If these costs outweigh the value of the time saved, the tool requires review or improvement rather than expansion.
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Decisions are only as good as the data behind them. Evidence quality grades range from Estimate Only up to Verified. Objective baselines, such as historical or cohort data, rank above self-estimates.
An 'Expand' state requires high-quality evidence. If a tool shows promise but relies on small sample sizes or recent, unverified estimates, the decision state should remain at 'Continue' or 'Review' until the data matures and the savings are proven to be recurring.
Payback is defined as how long the net value takes to cover the AI cost to date. For a CIO or CFO to approve expansion, the tool must demonstrate that it can recoup its initial implementation and training costs through labor value savings within an acceptable timeframe.
Future value, which is the expected recurring time savings multiplied by expected volume, should always be stated separately from realized value. Expansion cases are strongest when they are built on realized net value rather than just future projections.
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No. AI value management focuses on work and value, not individual monitoring. The framework measures the time the work would take without AI minus the time with AI.
This is not employee surveillance; there is no use of screenshots, keystroke logging, or browser monitoring. By focusing on the work itself, businesses can objectively determine which areas receive an 'Expand' or 'Stop' status based on process efficiency and financial return.
NetLift measures the shift from 'Estimate Only' to 'Verified' evidence by applying a deterministic value model to tracked work. By comparing the labor value of realized time saved against the full cost of licenses and rework, NetLift identifies which tools have earned an 'Expand' status and which require further review.
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About the author
Paige Gilmore · Founder, NetLift
Paige Gilmore is the founder of NetLift, the AI Value Management platform that helps organisations measure the cost, savings and return of AI adoption.
Paige Gilmore on LinkedInAI 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 ai-governanceAI governance value is the labor value of realized time saved minus the total cost of licenses, usage, implementation, training, and rework. Financial performance is measured by comparing hours saved against a loaded hourly cost baseline to determine a clear payback period.
Read ai-governanceAI 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 ai-governanceOrganizations 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 ai-governanceAn 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.
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