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Scaling AI economically takes more than choosing a cheaper model or giving more employees access. The work itself must be redesigned: break processes into clear steps, reserve deterministic tasks for software, use models suited to each step, and make execution auditable. That is the central argument in David Villalón’s 24 August 2026 opinion article for TechRadar Pro Perspectives. It is a proposed strategy, not a proven guarantee of lower costs.
Why pilot economics can break at production scale
A controlled pilot can make an AI workflow look predictable. Production adds changing volumes, unusual cases, and retries, which can make both performance and cost harder to forecast. Villalón presents these as practical challenges enterprises encounter when moving beyond pilots; his article does not quantify their frequency or measure their financial impact.
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The distinction matters because access expansion does not fix an inefficient process. If every request invokes a high-cost model, including tasks that could be handled by ordinary code, more usage may simply multiply the expense. Conversely, lowering the price of a model call may not resolve repeated calls, unnecessary steps, or poorly defined handoffs.
What the article’s headline percentages do—and do not—show
Villalón’s article reports that “80%” of enterprises miss AI cost forecasts by more than 25%, and that “over 95%” of generative AI pilots fail to reach meaningful production. The article provides no originating organization, study, sample, methodology, or year for either figure. They should therefore be treated as unattributed claims in an opinion article, not as independently established industry statistics.
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The argument for workflow redesign does not depend on treating those percentages as verified. Leaders can assess their own deployments by checking whether actual production volume, retries, exceptions, and model use match the assumptions in their business case.
Redesign the process, not just the model choice
Villalón’s proposed approach is to represent an AI-enabled process as discrete, code-backed steps instead of relying on one opaque generation to perform an entire workflow. Deterministic operations can remain in software; model calls can be reserved for tasks that need interpretation or generation. A record of the steps taken can make the execution easier to inspect.
- Define the work in steps. Identify inputs, decisions, transformations, model calls, and outputs so that each part has a clear purpose.
- Keep deterministic work deterministic. Use ordinary software for repeatable rules and operations that do not need a model’s judgment.
- Match the model to the task. Consider frontier, open-source, or smaller specialized models according to the task’s cost and performance needs. The article does not name vendors or establish that one model class is universally preferable.
- Retain an execution trace. Make it possible to inspect which steps ran and where exceptions or retries occurred; the article proposes an auditable trace but does not specify a logging standard or implementation.
This is a design principle, not a guarantee that modularity alone reduces total cost. The benefit depends on whether the process is genuinely simplified, whether models meet the required quality bar, and whether the cost of operating and maintaining the workflow is justified.
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Use a concrete process to test the economics
Loan processing is Villalón’s illustrative example of a workflow that could be divided into steps. It is not a documented customer deployment: the article identifies no lender, baseline, implementation, or measured savings. A team evaluating a similar process could map the work before selecting models:
- Map the existing workflow. Record the inputs, routine checks, judgment-dependent tasks, exception paths, and points where a person must review or approve an outcome.
- Separate rules from interpretation. Keep fixed validations and repeatable transformations in software; identify the narrower steps where a model may help interpret documents or produce a response.
- Set quality and control requirements. Specify what counts as an acceptable result, which cases must be escalated, and what evidence reviewers need to reconstruct the processing path.
- Estimate production costs by step. Include expected volume, model calls, retries, exception handling, and operational overhead rather than extrapolating from a small, controlled pilot alone.
- Compare candidate designs under representative conditions. Evaluate the full workflow for quality, traceability, and cost at plausible production volumes. Do not infer savings from model price in isolation.
Decide whether a workflow is ready to scale
Before expanding deployment, leaders can use four questions to expose gaps in the business case:
- Process control: Can the team explain the workflow’s steps and change them independently, or does one broad model call obscure the operation?
- Auditability: Can an operator trace how a result was produced and investigate failures or exceptions?
- Cost predictability: Has the estimate accounted for variable volume, retries, and non-routine cases, and can actual costs be monitored against it?
- Model flexibility: Can the team select an appropriate model for each task without compromising the required outcome?
If these questions have no clear answers, broader access is unlikely to resolve the underlying uncertainty. The next useful step is to make the workflow and its assumptions visible, then test the economics against realistic operating conditions.
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