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A fast, reliable AI pilot can still be uneconomic in production. In an anonymized audit described by Richard Ewing, a document-review workflow reportedly cost $12–$14 per file to automate work that had cost about $0.80 in labor—and that comparison became worse once exception handling and production inputs were counted. The figures are Ewing’s account, not independently verified benchmarks, but the lesson is broadly useful: measure the cost of a successfully completed task, not just the model’s token price or the pilot’s speed.
What the case study reportedly left out
Ewing’s October 8, 2026, opinion article, listed by CIO and hosted by Tiatra, describes an anonymized enterprise that automated review of vendor-onboarding agreements. A clerk previously checked five or six clauses, verified vendor details and filed each document in about four to five minutes. Ewing estimates the fully loaded labor cost at roughly $0.80 per document. The automated pipeline, he says, cost $12–$14 for the same file.
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Those are reported case-specific figures. The underlying invoices, transaction records, payroll assumptions and original vendor case study were not available for independent verification. They should not be treated as industry averages or as a current quote for any AI service. Ewing’s account is the basis for the deployment details; CIO’s listing confirms the article’s author and date.
The pilot’s computing bills, data lookups and third-party model fees reportedly came from a central innovation fund, rather than the department that would ultimately operate the workflow. Once those costs were allocated to the department, Ewing says, the economics turned negative within 60 days at full transaction volume. A pilot can therefore appear inexpensive to its users when its costs are paid elsewhere or are not yet allocated to the operating budget.
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Why production work cost more than a clean demonstration
The production queue reportedly included low-resolution scans, rotated photocopies, handwritten notes and conflicting payment terms. Ewing says these inputs triggered additional extraction passes, reference-data lookups and validation steps without reliably resolving the ambiguity. A demonstration set of clean documents can consequently understate how much processing a real workflow needs.
Human review was another material cost. According to Ewing, roughly 40% of daily transactions fell below the system’s confidence threshold and went to a person. Each exception took about ten minutes to handle—twice the reported manual baseline time—because staff had to inspect both the source document and the system’s partial output. The relevant comparison is not simply automated versus manual processing; it is the full automated workflow, including the work humans still perform.
Token prices and workflow costs are different measures
Model inference has become dramatically cheaper on some benchmark comparisons. Stanford HAI’s 2025 AI Index reports that inference for a model scoring at GPT-3.5-equivalent level on MMLU fell from $20 per million tokens in November 2022 to $0.07 per million tokens in October 2024—more than a 280-fold decline. That is a benchmark-level model-query price, not the total cost of completing a business task. Stanford HAI’s figures do not include every workflow’s compute, data access, repeated processing or staff review.
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At the same time, Gartner’s August 17, 2026, release forecasts that inference cost per agentic workflow will increase more than fivefold through 2028 as workflows grow more complex and use more tokens, even while model prices improve. This is a forecast about agentic workflows, not a universal observed outcome. Gartner analyst Will Sommer cautions: “Product leaders cannot rely on more efficient token economics to rationalize AI costs.” He also says, “Each successive generation of AI capability will necessitate more, and often more expensive, tokens. There is no reliable, economical one-size-fits-all model on the horizon.” Gartner’s release provides the forecast and quotes.
The two metrics are not contradictory: a lower price per token can coexist with a higher bill per workflow if each task uses more tokens, more steps or more external services. Neither the Stanford benchmark nor Gartner’s forecast verifies Ewing’s case-specific costs.
How to calculate the cost of a completed task
Compare workflows using the same definition of a successful completion and a representative set of documents. Count all costs incurred to reach that outcome, including tasks that need retries or human intervention.
- Compute and model access: Include infrastructure compute and every third-party model charge, not only the headline price of one model call.
- Data and supporting steps: Count database or reference-data lookups and other processing steps used by the workflow.
- Human intervention: Measure how often people step in, how many minutes each case takes, and the loaded labor cost of that time.
- Workflow intensity: Track steps and model calls per task, including extra passes used to handle poor-quality or ambiguous inputs.
- Successful outcomes: Divide the workflow’s attributable costs by completed tasks, using a consistent definition of completion and including unresolved or rerouted cases in the comparison.
This makes the unit of comparison fully loaded cost per successfully completed task, rather than cost per token, per seat, or per processed document regardless of outcome.
Questions to answer before moving a pilot into production
- What does one completed transaction actually cost compared with the manual baseline, accounting for all infrastructure compute, database lookups and third-party model charges? Use billed production usage and a consistent task definition, not an estimate based only on model pricing.
- How often does a human still have to intervene, and what is the loaded payroll cost of that review time? Include exception frequency, review minutes and any extra effort needed to inspect partial system output.
- Do the economics still work at production volume, or is the organization simply moving an operational expense from payroll into an unpredictable consumption meter? Allocate pilot and shared costs to the team that will own the operating budget, then check the result at expected transaction volume.
Use representative production inputs when answering these questions. If the pilot relied on clean documents, test the workflow against the scans, handwriting, rotations and conflicting information it will actually encounter. Ewing’s account illustrates why throughput and uptime alone do not establish whether a workflow is affordable.
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What the available comparison can—and cannot—tell you
The case is a warning about cost measurement, not a ranking of AI products. The available evidence does not compare named vendors or establish that a particular model, cloud provider or automation platform is cheaper. For a real proposal, compare options on the same workload and these dimensions:
- Fully loaded cost per successfully completed task.
- Human intervention rate and labor minutes per intervention.
- Workflow steps and model calls per task.
- Performance on representative, messy inputs—not only demonstration examples.
- Economics at the volume the operating team expects to handle.
Revisit the calculation when the workflow, input mix, model usage, transaction volume or cost allocation changes. A pilot fund can hide costs that appear once the process becomes an operating responsibility.
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