The Business Case for Matching AI Models to Workloads

From Model Selection to Workload Strategy

In the early stages of AI adoption, organizations typically selected the most capable model available and used it across an entire application. This made sense when model choice was limited, applications were experimental, and usage volumes were low.

As enterprise AI moves into production, organizations are processing more requests and running models more frequently. AI costs are becoming recurring operating expenses, while organizations have access to a wider range of models. More efficient, lower-cost models can now perform many routine tasks that once required a frontier model.

Organizations must determine which model best meets the requirements of each workload, and, collectively, which set of models delivers the right balance of quality, performance, reliability and cost across the complete application.

This requires a workload-based approach:

1. Break the application into distinct workloads.
2. Define the requirements of each workload.
3. Choose the right model for each workload.
4. Evaluate the cost of completing the task successfully.

Understanding the Workloads Within an Application

Most AI applications perform several different tasks. A customer-support application, for example, might identify the nature of a customer’s request, retrieve account or policy information, extract relevant facts, determine the appropriate action, draft a response and check that response against company policy.

Each of these tasks can place different demands on the model. Classification may be repetitive and high-volume. Drafting a response requires more flexibility. Resolving an unusual problem may require stronger reasoning and carry greater consequences if the answer is wrong.

These tasks can be organized into several common workload categories:

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The workload-based view is particularly useful for understanding AI agents. A single agent may combine several workloads as it retrieves information, uses tools, evaluates results and repeats steps. Each step contributes to the quality, latency and cost of the completed task.

Defining the Requirements of Each Workload

Identifying the workload is only the first step. The same type of work can vary considerably from one application to another. Extracting clearly labelled information from a standard form may be relatively simple, while finding the same information across legal documents with different formats and wording may require more context and judgment.

Before evaluating models, organizations should define what each workload requires. This includes how accurate and reliable the results must be, how difficult the task is, how quickly and frequently it must be performed, what security or privacy requirements apply and what happens if the model makes a mistake.

Comparing these requirements may show that routine tasks account for most of the usage, while less frequent, complex tasks have more serious consequences if they fail. It may also show that retries, additional checks, tool use and human review add significantly to the total cost.

Matching Model Capability to Business Need

One way to assess models is to group them into three broad tiers: high efficiency, balanced performance and maximum capability. High-efficiency models may suit predictable, high-volume work such as classification, extraction and routing. Balanced models can support summarization, retrieval, generation and moderately complex tool use. Maximum-capability models remain important for complex reasoning, ambiguous decisions, advanced coding and other tasks where errors are especially costly.

These are starting points rather than fixed assignments. A more capable model may be more economical if it completes difficult work without retries. A more efficient model may deliver the required result for routine work at lower cost. Unusual or low-confidence cases can be escalated instead of processing every request at the highest capability level.

In practice, model selection is often determined by software defaults rather than workload requirements. As VentureBeat reports in “Companies are spending millions rewiring how AI gets used. Almost none can prove it’s working”, some enterprise AI tools preselect premium models, high reasoning settings and large context windows. At Promova, for example, use of Anthropic’s Opus model with a one-million-token context window accounted for approximately one-third of monthly AI spending. Recommended defaults or automated routing can help organizations direct routine work to more efficient models while escalating tasks that require greater capability.

The objective is not to use the least expensive model wherever possible. It is to use the least costly model that reliably satisfies the requirements of the workload.

Evaluating the Complete Business Outcome

Token prices provide an incomplete view of production economics. A task may require multiple model calls, retrieval, tool use, validation, retries, escalation or human intervention. A low-priced model can become expensive if it fails frequently, while a higher-priced model may be more economical if it completes difficult work on the first attempt.

Organizations should therefore evaluate cost per successfully completed task: the total cost of all attempts divided by the number of results that meet the workload’s requirements. As McKinsey argues in “The cost of intelligence: How CIOs can manage AI demand at scale”, the completed business outcome—not token consumption—should be the unit used to govern AI spending. This means connecting costs to the work performed, such as a customer interaction, processed claim or code review, and accounting for all the model calls and other resources required to complete it.

The results can vary considerably even within the same organization. VentureBeat reports that Everlaw spent $3,500 in AI tokens on one project and reduced estimated implementation effort from 9.5 to 2.5 engineer-months. In another project, however, the company spent thousands of dollars generating code that was ultimately discarded. The contrast demonstrates why token consumption and generated output are insufficient measures of value: the result must be usable and improve the economics of completing the work.

Public benchmarks and model pricing can help organizations identify candidate models for a workload, but they cannot determine whether those models will perform that workload successfully in a specific enterprise environment. Each proposed match must ultimately be tested using representative work and measured against the required quality, reliability, speed and total cost.

As the range of models expands, selection will become less about choosing a single winner and more about developing a portfolio that reflects the work across the organization. Starting with the workload gives enterprise leaders a practical basis for managing AI performance, risk and cost.

Sources

• McKinsey, “The cost of intelligence: How CIOs can manage AI demand at scale”
• Venture Beat, “Companies are spending millions rewiring how AI gets used.”

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