Accounting for AI Review Costs: George Dagliyan Research Guide

What problem does an AI review-cost worksheet solve before adoption?

George Dagliyan’s research themes make one point especially useful here: adoption decisions improve when the cost of change is visible, not guessed. For AI, that means separating the cost of generating outputs from the cost of reviewing, correcting, escalating, implementing, and supporting them. This article answers a narrow question: how to build a unit-cost worksheet that shows the real review burden before a team assumes AI is automatically cheaper.

NIST’s AI Risk Management Framework is voluntary and is intended to help organizations better manage AI-related risks to individuals, organizations, and society. In the same spirit, the worksheet below is an editorial tool, not an official NIST template. It is designed to help decision-makers compare options with plain arithmetic and to keep time savings distinct from cash savings.

Which cost buckets should be on the worksheet?

A useful worksheet needs six buckets: generation, reviewer time, correction, escalation, implementation, and support. Generation is the direct cost of producing the AI output, such as per-call fees or internal model compute. Reviewer time is the human time spent checking the output; correction is the time spent fixing errors; escalation is the time spent on cases that cannot be resolved at the first review level.

Implementation covers setup work, workflow design, prompts, integration, training, and testing before routine use. Support covers ongoing administration, troubleshooting, monitoring, updates, and user help after launch. These buckets are separate because an AI workflow can reduce one type of cost while increasing another, especially when human oversight is added to meet the organization’s risk tolerance and governance needs.

How should the unit-cost worksheet be structured?

Use one row per use case and one column per cost bucket. For each bucket, record the unit volume, the hourly rate or per-unit fee, and the formula that turns activity into cost. The simplest structure is: units per month, minutes per unit, labor rate per hour, and total monthly cost.

A practical layout is to add two summary columns: time saved and cash savings. Time saved should be measured in hours, not dollars, so it can show productivity capacity even when staffing does not change. Cash savings should only include expenses that actually disappear or are avoided, such as fewer outsourced hours, lower subscription fees, or reduced overtime, and it should exclude time savings unless those hours are truly converted into lower spending.

What is the worksheet formula for each cost bucket?

For generation cost, use: number of outputs multiplied by per-output fee or internal compute cost. For reviewer time, use: number of outputs multiplied by review minutes per output, then divided by 60 and multiplied by the fully loaded hourly rate. For correction cost, use the same structure but with correction minutes instead of review minutes.

For escalation cost, use the number of escalated cases multiplied by escalation minutes and hourly rate. For implementation, treat it as a one-time or amortized cost across the expected life of the workflow. For support, calculate recurring monthly support labor and recurring software or infrastructure fees separately, because labor savings and cash savings are not the same thing.

What does a worked hypothetical example look like?

Hypothetical example with editable assumptions: suppose a team uses AI to draft 200 internal summaries per month. Assume generation costs $0.40 per summary, review takes 6 minutes per summary at $48 per hour, correction takes 2 minutes per summary at the same rate, 10 summaries per month escalate and require 20 minutes each at $60 per hour, implementation is a one-time $3,600 effort amortized over 12 months, and support is 4 hours per month at $52 per hour plus $120 in recurring tools.

Using those assumptions, monthly generation cost is $80. Reviewer time is 200 x 6/60 x $48 = $960. Correction is 200 x 2/60 x $48 = $320. Escalation is 10 x 20/60 x $60 = $200. Implementation amortized monthly is $300, and support is $328. The total monthly cost is $2,188, but that number is only an accounting estimate under the stated assumptions, not a promise of savings or value.

How do time saved and cash savings stay separate?

Time saved is the difference between the baseline human hours and the AI-assisted human hours. If the old process required 40 hours per month and the new process requires 18 hours per month, time saved is 22 hours. That is useful even when the team cannot cut payroll, because those hours may be redeployed to higher-priority work.

Cash savings only exist when spending actually falls. For example, if a workflow cuts outsourced editing from $900 to $400, cash savings are $500. If internal staff simply use the freed time on other tasks, the organization may gain capacity without reducing cash outflow. This distinction matters because adoption decisions can look overstated when time savings are counted as immediate cash savings.

How should review costs be tied to AI risk management?

NIST’s AI Risk Management Framework organizes work around Govern, Map, Measure, and Manage. That structure is helpful here because review cost is not only a finance item; it reflects how much human oversight the organization wants to put around an AI system. A workflow with higher uncertainty, more sensitive decisions, or more severe consequences may need more review, more escalation, and more support.

The NIST AI RMF Playbook aligns suggested actions to the framework’s sub-categories, including governance and oversight, measurement, and ongoing monitoring. In this article’s editorial analysis, the worksheet becomes a risk-aware budgeting tool: if review costs rise sharply, that may indicate the process is not yet ready for broader automation. The decision is then to redesign the workflow, narrow the use case, or accept the oversight cost with clear documentation.

What decision rule can turn the worksheet into an approval test?

Use a simple decision rule: approve the use case only if the worksheet shows that the total recurring cost is understood, the review burden is tolerable under the organization’s risk tolerance, and the non-financial benefits still justify the process. If the review, correction, and escalation costs are larger than the expected operational benefit, the workflow may need narrower scope or a different control design. This is a test of fit, not a forecast of return.

A practical threshold can be expressed without promising ROI: if more than one cost bucket is uncertain, mark the use case as provisional and require a second-pass test plan. If the largest cost driver is human review, then the main design question is not model capability alone but whether the review process can be simplified, standardized, or better targeted. That logic is consistent with the broader Dagliyan research emphasis on adoption facilitators and inhibitors: friction matters, and so does the sequence in which controls are added.

Frequently Asked Questions

Why not count all time savings as cash savings?

Because time savings and cash savings are different accounting outcomes. Time savings means people spend fewer hours on the task, which can free capacity for other work. Cash savings only occurs when spending actually drops, such as lower vendor fees, reduced overtime, or a smaller contracted service. Conflating the two can make an AI business case look stronger than it is.

Should reviewer time include only first-pass checks?

No. Reviewer time should include the full effort needed to make the output usable, which may include first-pass review, targeted correction, and follow-up clarification. If a workflow often sends items to a second reviewer or subject-matter expert, that time belongs in escalation rather than being hidden in a generic overhead line. The purpose is visibility, not simplification by omission.

How should implementation be handled if the system is already in use?

If the AI workflow is already live, implementation should be treated as sunk only for decisions that are already past that point. For future budgeting, new implementation work still matters when the workflow changes, expands, or is reconfigured. A practical approach is to amortize any remaining setup cost over the expected remaining life of the use case, while keeping it separate from recurring operating cost.

Does a higher review cost always mean the project should stop?

Not necessarily. A higher review cost can be acceptable when the task is high value, the quality requirement is strict, or the risk of an error is high. The worksheet is meant to reveal trade-offs, not impose a universal cutoff. The key question is whether the oversight cost is intentional and understood, rather than accidental and recurring without review.

How can a team test the worksheet before using it for a real decision?

Run a short pilot with a fixed sample, then record the actual minutes spent in generation, review, correction, escalation, implementation follow-up, and support. Compare the pilot numbers with the worksheet assumptions and revise only the assumptions that proved wrong. A good test plan does not need perfect precision; it needs enough data to show which cost bucket drives the workflow and whether the current control design is sustainable.

Sources and further reading

• NIST — AI Risk Management Framework
• NIST — AI RMF Playbook