AI Benefits Realisation Plan

By Paige Gilmore, Founder, NetLift · Published 2026-07-28 · Updated 2026-08-07

An AI benefits realisation plan is a financial framework that measures the labour value of time saved minus the total cost of AI implementation, licenses, and rework. It uses objective evidence to categorize AI projects into decision states: Expand, Continue, Review, Improve, or Stop.

An AI benefits realisation plan ensures that technology adoption translates into measurable financial gain. Instead of relying on qualitative surveys, it uses a deterministic model to compare the time work takes with AI against historical baselines. This provides leadership with a clear view of current net value and the time required to achieve payback.

Effective plans focus on the work itself, not individual productivity. By measuring the labour value of hours saved at a loaded cost—typically $75 per hour—organisations can identify which tools are delivering a genuine return and which require rework or decommissioning.

What is the core formula for AI value?

Net value is calculated by taking the labour value of realised time saved and subtracting the full cost of the AI initiative. This cost includes more than just the license; it accounts for usage fees, implementation, training, and the time spent on human review and rework. If the cost of checking the AI's output exceeds the time it saved, the net value is negative.

How is time saved measured without surveillance?

Time saved is the difference between how long a task would take without AI and the time it takes with AI. This methodology focuses on work and value, not individual monitoring. It does not use keystroke logging, screenshots, or browser monitoring. By using objective baselines, such as historical data or cohort comparisons, companies can verify time savings while maintaining employee trust.

Why does evidence quality matter to the Board?

Not all data is equal. A robust realisation plan grades evidence quality from "Estimate Only" up to "Verified." High-quality evidence relies on objective baselines and large sample sizes. This allows the CFO and CIO to trust the reported ROI and move away from speculative projections toward deterministic financial reporting.

How do you manage the AI portfolio?

Every measured area in the plan is assigned one of five decision states. "Expand" and "Continue" are reserved for initiatives with proven net value and high evidence quality. "Review" or "Improve" apply to tools where rework is high or savings are inconsistent. "Stop" is used when the cost of the AI and its required human oversight consistently exceeds the labour value of the time saved.

NetLift provides the deterministic framework needed to move from AI hype to financial reality. By applying a consistent value model to tracked work, NetLift measures time saved vs. baseline and assigns an Evidence Quality grade to every figure. This ensures that the payback period is calculated based on realised value, giving stakeholders a clear signal on whether to expand or stop their AI spend.

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