The economic case for an AI sandbox.

CHAPTER 01

The workflow is the unit of value.

An AI assistant can assemble a response in seconds. But in an energy or utility company, someone still needs to check the sources, resolve exceptions, and authorize the outcome. The business pays for that complete process.

If preparation gets faster while verification gets harder, an impressive demonstration may leave operating costs unchanged. A useful evaluation therefore follows a case from intake to an acceptable, approved result. It includes the reviewer’s time, failed attempts, escalations, and work repeated downstream.

Existing research supports measuring results in context. In the November 2023 working-paper version of Generative AI at Work, Brynjolfsson, Li, and Raymond report a 14% average productivity gain among 5,179 customer support agents, with substantially different effects across experience levels. That result is evidence from a particular setting; it is not a savings assumption for a regulated workflow.

The question is how much it costs to produce work your organization can actually accept.

That is the purpose of a workflow sandbox: find out what changes when a specific team uses a specific system on its own work.

CHAPTER 02

Recreate the work. Control the consequences.

Here, a sandbox means an isolated evaluation environment using approved historical or synthetic cases, controlled tool access, and a defined review process. It is separate from live transactions and operational control. It does not imply a regulator-sponsored sandbox or regulatory approval.

The environment should reproduce the parts of the workflow that determine quality and cost: source documents, permissions, templates, tool responses, exception handling, and human handoffs. Synthetic examples help test rare failures; historical cases help test how the system handles the actual mix of work.

In Corvana’s approach, a workflow starts with a baseline and a named owner. An agent prepares work inside a bounded environment; reviewers grade the result against criteria agreed before the test. A failed permission check or unsupported conclusion is recorded as a failure, even when the rest of the output looks convincing.

This approach is consistent with the emphasis on documented test methods, context, and evaluation in the NIST AI Risk Management Framework Playbook. Applying that guidance helps organize evidence; it does not certify a system or replace an organization’s obligations.

CHAPTER 03

Design a test that can say no.

A test designed only around successful examples cannot support an investment decision. Before running it, agree on the following with operations, finance, and the people who own the risk.

1. A representative case mix. Sample routine work, difficult exceptions, missing records, and the cases that previously needed escalation. Report results separately where their costs or consequences differ.
2. A fair comparison. Compare the current process with the AI-assisted process on equivalent cases. Keep a held-out set separate from development, and prevent historical final answers from leaking into the agent’s inputs. Use balanced reviewer assignments to reduce practice effects.
3. An acceptance bar. Define completeness, source support, correctness, appropriate abstention, and required approvals. Score consequential errors by severity. Use qualified reviewers who did not build the workflow where practical.
4. The full cost of completion. Capture preparation, review, rework, and escalation time, plus model and tool costs. Track elapsed turnaround separately from active labor. Include failed cases and any manual fallback work.
5. Reproducible evidence. Record model, prompt, tool, and source versions alongside the outcome. Repeat runs where output varies, report sample size and uncertainty, and keep enough detail to explain an outlier.

Quality is a release condition. A workflow that saves time but fails a critical control should not be rescued by a favorable average. Likewise, a small test with no observed serious failures cannot establish that rare failures will never occur.

CHAPTER 04

Translate minutes into money carefully.

Consider a hypothetical document-review workflow processing 24,000 cases a year. The numbers below illustrate a method; they are not Corvana customer results or a forecast.

The preparation step saves 14 minutes, but additional review absorbs two. The net saving is 12 minutes per case. Assume this is an average across the full tested case mix, including unsuccessful attempts and manual fallbacks.

At 75% adoption, 18,000 cases use the workflow each year. That releases 3,600 hours. At an assumed fully loaded labor rate of $60 per hour, the capacity has a value of $216,000.

Capacity released is not automatically cash saved. Finance needs to identify how those hours change spending: less overtime, lower contractor bills, or a documented reduction in planned hiring. Hours redeployed to a backlog are useful operational capacity, but should be reported separately from expense reduction.

The $84,000 assumption includes platform and model usage, support, monitoring, and recurring evaluation at the assumed adoption level. Case-level review and rework are already included in the 18-minute labor estimate. The first-year illustration assumes a full year at steady state; deployment delays or a gradual rollout would lower that result. Payback excludes discounting and taxes.

This is a positive recurring business case with a negative first year. The sandbox makes that tradeoff visible before the larger commitment. If only 40% of released capacity becomes lower spending, recurring benefit falls to $2,400. At 80%, it rises to $88,800. The adoption and realization assumptions deserve as much scrutiny as the model’s accuracy.

Reduced rework, faster service, and avoided incidents may add value, but only when there is evidence for a separate effect. Do not count the same saved hour twice, treat all faster turnaround as new revenue, or include a hypothetical avoided fine as a guaranteed saving.

CHAPTER 05

Same discipline. Different consequences.

The same financial method can be used across energy and utility workflows. The cases, acceptance criteria, and boundaries must change with the work. These examples describe potential evaluations, not customer results.

Utilities: regulatory response preparation

Replay closed data requests against the source record available at the time. Test citation support, effective dates, and the effort needed to produce a reviewer-approved response. Measure cost per accepted response and contractor effort. Any claim about recoverable costs needs its own evidence and review.

Energy companies: asset work-package preparation

Replay completed maintenance packages using the asset records, inspection notes, and approved procedures available at the time. Test document retrieval, missing-information detection, and draft completeness. Measure planner and reviewer effort together. Qualified people retain the decisions about maintenance and asset operation.

Grid contractors: change-order preparation

Replay closed change orders using approved job records, field tickets, and contract terms. Test whether the draft captures the documented work and supporting evidence. Measure preparation time, reviewer corrections, and missing items. The project manager retains approval, and the evaluation should separate faster preparation from any assumed improvement in collection.

In each setting, the sandbox should test the preparation and review workflow without taking consequential live actions. Production permissions and release decisions remain a separate, explicit step.

CHAPTER 06

A better investment decision is the first return.

The strongest outcome is a workflow that clears the quality bar and has a credible path to financial benefit. But a well-designed test can also show that a workflow needs better source data, that review eliminates the time saving, or that expected volume is too low to justify the cost.

Stopping that investment can be valuable. The avoided spend should be tied to a real proposed commitment, with the evaluation cost deducted, rather than presented as hypothetical savings.

A useful readout gives leadership the baseline, case-level results, control failures, cost assumptions, sensitivity analysis, and an owner for realizing the benefit. It ends with a recommendation: proceed within a defined scope, revise and retest, or stop.

For work that proceeds, the sandbox estimate becomes a hypothesis to validate through a limited production rollout. Track actual adoption, spending, quality, and drift against the baseline. Retest material changes to models, tools, or source records.

Prove the workflow. Price the whole process. Invest where the evidence holds.

That is the economic role of a sandbox: turn uncertainty into a bounded experiment, and turn the experiment into a decision the business can defend.