How to choose AI models for each business task?

A company decides to incorporate artificial intelligence into two different processes.

In the first, the model must classify purchase requests into predefined categories. In the second, it needs to analyze contracts, locate relevant clauses, and explain possible discrepancies to a specialized team.

Both processes use language, but they do not present the same complexity, the same risk, or the same operational requirements.

Purchase classification may require fast, structured, consistent responses. Contract analysis may need greater interpretive capacity, broader context, identification of the evidence used, and review by specialists.

Adopting the same model for both processes can raise costs without improving the simpler task and create a false sense of security on the more complex one.

Selection must start with the work that will be performed.

Define the task before comparing models

A generic description such as “analyze documents” does not offer enough criteria for the choice.

It is necessary to detail which documents will be processed, what result the model must produce, which errors are acceptable, how long the response may take, and who will use the outcome.

It is also important to clarify whether the model will only support a decision or whether its response will be used automatically by another system. The greater the impact of a response, the greater the care required with evaluation, oversight, and logging.

PwC recommends that organizations assess each option in the context of the use case, considering the data involved, performance requirements, fault tolerance, and the company’s ability to operate and protect the model over time [1].

Before researching vendors, the company needs to answer some concrete questions.

What input will be received? What output will be considered correct? Must the response follow a structured format? Does the process require querying internal documents? Is there human review? What is the impact of a wrong response?

Without that definition, comparison tends to favor popularity, size, or general capabilities of models rather than assessing their fit for the process.

Criteria must reflect the work

There is no single criterion capable of determining which model should be chosen.

The decision must combine functional, operational, economic, and risk requirements. These criteria also need different weights depending on the task.

An initial evaluation can consider:

1. Functional capacity: verify whether the model meets the modalities, languages, context size, output format, tool use, and other resources required by the process.
2. Quality: measure whether responses are correct, complete, consistent, and useful within the task being evaluated.
3. Operational performance: observe latency, processing capacity, stability, and behavior during periods of higher demand.
4. Data and security: analyze which information will be sent, where it may be processed, how it will be handled, and which access and retention controls will be needed.
5. Integration: assess compatibility with applications, APIs, identity mechanisms, monitoring, and existing architecture.
6. Total cost: consider consumption, infrastructure, development, integration, human review, rework, and ongoing operation.

BCG recommends that the choice start from purpose and consider the balance between performance, cost, and operational capacity. The best-known or largest model will not necessarily be the most suitable for every task [3].

An internal classification process may prioritize cost, speed, and consistency. An application that analyzes contracts may require greater interpretive capacity, verifiable evidence, careful handling of documents, and participation by specialists.

Benchmarks help build the candidate list

Public benchmarks are useful for reducing the initial number of alternatives.

They make it possible to observe how different models behave on standardized tasks of reasoning, knowledge, safety, coding, latency, and processing capacity.

However, an aggregate score does not represent all conditions of an enterprise application.

A model ranked well in a general evaluation may not show the same performance when classifying internal documents, interpreting the vocabulary of a given sector, or producing responses in the format required by an integration.

PwC highlights that public benchmarks can guide the decision but do not replace tests with representative business tasks, real data patterns, quality thresholds, latency requirements, and fault tolerance [1].

Technical documentation from AWS and Microsoft offers mechanisms to run automatic evaluations, apply custom prompt sets, use evaluator models, and incorporate human review [6][7]. These resources are useful for running tests, but success criteria must be defined by the organization itself.

The benchmark helps build the candidate list. The decision must be confirmed in the real process.

Compare models under the same conditions

A reliable comparison requires that all models receive the same instructions, the same examples, and the same evaluation criteria.

The sample must represent common situations, difficult cases, and relevant exceptions. The data used must be authorized for this type of test. When there is sensitive information, the team should assess the use of anonymization, masking, or synthetic data appropriate to the objective.

The evaluation should also define in advance what will be considered success.

In a classification task, it may be possible to compare the result with expected categories. In contract analysis, evaluation may require specialists to verify whether clauses were identified correctly, whether the explanation is supported by the document, and whether relevant information was omitted.

Automatic evaluations speed up comparison. Evaluator models can also help examine large volumes of responses. Even so, output produced by another model should not be treated as absolute truth.

For important processes, human review and business criteria remain necessary.

NIST recommends that organizations evaluate relevant capabilities and safety measures before deployment and on an ongoing basis, considering context, risk tolerance, and possible impacts [5].

The lowest price per run does not represent total cost

A comparison limited to input and output price can distort the decision.

A model that appears cheaper may require larger instructions, generate more rework, or depend on greater human review. Another may cost more per run but reduce corrections and deliver structured responses that simplify integration.

McKinsey treats this problem as part of the broader economics of artificial intelligence. Cost depends on model selection, routing, flow design, agent behavior, call volume, and the amount of wasted processing [2].

Total cost can also include prompt preparation and updates, document retrieval, storage, tool calls, human review, error correction, observability, and maintenance of integrations.

BCG adds that model, architecture, and control platform represent related but distinct decisions. Optimizing only the advertised price from one provider may hide integration costs, technological dependency, governance, and operation [4].

A small difference per run can become significant at scale. Likewise, saving on the model may not pay off if the rework rate increases.

The right question is not only how much a call costs. The company needs to know how much it costs to achieve an acceptable result for the process.

An example of selection by task

Consider a company that wants to automate triage of purchase requests.

The task consists of reading a short description, identifying the corresponding category, and returning a simple structure. The company has reviewed historical examples and can clearly define the expected categories.

Candidate models can be compared on classification accuracy, format stability, response time, and cost for the estimated volume.

In another process, the same company wants to support contract analysis. The model must work with lengthy documents, locate clauses, relate excerpts, and produce an explanation for specialized review.

The evaluation then considers context, grounding of responses, ability to indicate the excerpts used, relevant omissions, document privacy, and quality of human review.

The outcome may point to different models for each task.

That does not force the company to create a complex architecture from day one. It only means selection was guided by the work to be performed, not by an attempt to standardize the entire organization prematurely on a single alternative.

A company may end up with more than one model

After evaluating different tasks, the organization may conclude that distinct models better serve different parts of its processes.

A smaller model can perform classifications, extractions, and structured transformations. Another can be reserved for analyses that require greater reasoning or interpretive capacity. There may also be differences related to data, languages, modalities, or deployment conditions.

PwC observes that model selection does not need to be treated as a binary choice between a single open or closed option. A portfolio can combine models and deployment forms according to the requirements of each use case [1].

This approach also reduces dependence on a single model family. However, it increases the need for common criteria, integration, access control, observability, and cost tracking.

A multi-model environment should be the consequence of decisions by task. It should not arise merely because many alternatives are available.

The choice must be reviewed over time

Models, prices, versions, and terms of use change. The process served may also receive new rules, data, and integrations.

Therefore, the decision must be documented.

The company should record which version was evaluated, which data were used, which criteria were applied, and which results justified the choice.

Relevant changes should trigger a new evaluation. That includes version replacement, changes to the main prompt, addition of new tools, broader audience, and use of the outcome in higher-impact decisions.

The ability to replace a model should also be part of the architecture. The more durable investment lies not only in the model chosen but in the evaluation process, controls, test data, and mechanisms that allow revisiting the decision when conditions change [1].

From selection to operation: ARGUS

After the organization defines which models serve each task, the challenge becomes controlling how applications and agents access those resources in production.

With AI Gateway capabilities, ARGUS can centralize access to integrated models, apply policies defined by the organization, and record consumption and interactions. The platform can also support operational tracking of agents that use those models.

This control layer makes it possible to relate model use to the agent, application, and process being served. It also facilitates observation of consumption, failures, latency, and operational changes in integrated components.

ARGUS does not automatically choose the best model for each task. Defining criteria, preparing samples, and deciding on quality, risk, and cost remain the organization’s responsibility.

The platform turns a documented choice into policies and operational evidence that can be tracked over time.

About Target Solutions

Target Solutions has spent more than 15 years helping organizations evolve their infrastructure, monitoring, observability, automation, and operational intelligence operations.

Across that journey, it has participated in projects in complex environments in telecom, technology, government, energy, and large enterprises, contributing to higher operational efficiency and service reliability.

In AI model selection, Target Solutions can support definition of use cases, comparison criteria, and the evaluation process. Services can also cover architecture, governance, integration, observability, and preparation of the operational model needed to put applications into production.

This work is not limited to deploying a specific platform. The goal is to help the company structure technical and operational decisions suited to its tasks. When it makes sense for the environment, Target Solutions can also support deployment and integration of a platform such as ARGUS.

About the author

Paulo Florêncio de Menezes is cofounder of Target Solutions and has spent more than 15 years on projects related to infrastructure, monitoring, observability, automation, and IT and telecom operations.

He holds a degree in data processing from the Federal University of Amazonas (UFAM), an MBA in business management from Fundação Dom Cabral (FDC), and a professional master’s in economics from Fundação Getulio Vargas (FGV).

In his master’s research, he developed the IBEOP-IA (Brazilian Index of Occupational Exposure Potential to Artificial Intelligence), studying the potential exposure of Brazilian occupations to AI capabilities through a combination of occupational data, artificial intelligence patents, and natural language processing techniques.

References

[1] PwC. The open-weight decision: What CISOs should consider before they say yes.

[2] McKinsey & Company. The cost of intelligence: How CIOs can manage AI demand at scale.

[3] Boston Consulting Group. Laying the Foundation for Gen AI Success.

[4] Boston Consulting Group. There’s More to Cloud AI Cost Than Token Price.

[5] NIST. Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile.

[6] Amazon Web Services. Evaluate the performance of Amazon Bedrock resources.

[7] Microsoft. Model leaderboards in Microsoft Foundry.