Most teams deploying Claude have no AI model selection strategy for business workflows. Every task runs through the same tier regardless of whether that task requires it. A credit card reconciliation. A document summary. A complex legal analysis. All Opus. The cost compounds quickly, and when organizations start tracking their AI spend, the results are often surprising.

This pattern appears consistently in Cadre's AI implementation work with clients. By the time teams recognize the problem, workflows are already built around the wrong model tier and rearchitecting them is expensive. The better approach is building a model selection policy before scaling. This post covers how to do that.

The Three Claude Model Tiers and What Each Is Built For

Claude offers three distinct tiers, each optimized for a different balance of speed, cost, and reasoning depth.

Claude Haiku is the fastest and lowest-cost tier. It is built for high-volume tasks where accuracy on straightforward inputs matters more than complex reasoning. Haiku is the right choice for document classification, data extraction, routing and triage workflows, summarizing structured content, and responses to well-defined, repeatable questions.

Claude Sonnet is the balanced middle tier. It handles more demanding language tasks including nuanced writing, multi-step analysis, and longer documents, without the cost of Opus. Most knowledge work in a business setting belongs in Sonnet territory. It is the practical default for the majority of AI-enabled workflows.

Claude Opus is the highest-capability tier, built for tasks that require complex reasoning, multi-step problem solving, or outputs where errors carry real consequences. Opus is the right choice for synthesizing conflicting information across multiple sources, producing analysis reviewed by senior decision-makers, and handling edge cases in regulated workflows.

The cost difference between tiers is substantial. Anthropic publishes per-token pricing for each model, and the gap between Haiku and Opus on a high-volume workflow can exceed 90 percent per task. Organizations that use Opus as their default are paying premium pricing on work that does not require it.

Why Organizations Default to Opus and Why It Becomes a Problem

The logic that leads to Opus overuse is easy to follow. Opus produces the best outputs, so using it everywhere feels safe. The problem is that "best" only means best for tasks that genuinely require its reasoning depth. For everything else, the cost is unjustified and the speed trade-off is real.

In a recent Cadre office hours session, a client team was processing credit card reconciliation entries through Opus. The task required matching transaction descriptions to department and product codes, which is a pattern-recognition job that Haiku handles accurately at a fraction of the cost. The team had not made a deliberate model selection decision. They had built the workflow with Opus because it worked during testing, and nobody revisited the choice as usage grew.

That same team was also evaluating Microsoft Copilot as an alternative for their SharePoint-based accounts payable automation because they had hit Claude's file management limitations in that context. The model selection question and the tool selection question were compounding each other. Without a policy for which tool at which tier for which task, every new workflow becomes a judgment call with no foundation.

This scales poorly. What starts as a flexible approach to AI deployment becomes an undifferentiated cost center with no clear line between spend and value.

What Does an AI Model Selection Policy Need to Include?

An AI model selection policy needs to answer four questions: What task types belong in each tier? Who decides when a task requires a higher-cost model? How will usage be monitored? And when will the policy be reviewed as model capabilities evolve? A policy that answers these four questions is sufficient to change default behavior across most teams.

Define task categories by tier. Build a simple reference document that lists the most common workflows in your organization and assigns each a default model. A one-page guide covering 10 to 15 common use cases is enough to create consistency.

Establish a decision process for exceptions. Most tasks should default to Sonnet. Opus should require a deliberate choice with a stated reason, not just a preference. Define what criteria justify moving a task to a higher tier and who has the authority to make that call.

Set up usage monitoring. Without visibility into token spend by model and workflow, the policy has no enforcement mechanism. Logging shows where cost is concentrated and where the policy is not being followed.

Build in a review cadence. Model capabilities change as providers release updates. A policy accurate today may need adjustment in six months. A quarterly review is sufficient for most organizations.

Common Use Cases and the Right Model for Each

Haiku: Document classification and routing, extracting structured data from forms or records, generating templated responses to common questions, summarizing short or well-structured documents, and simple validation tasks.

Sonnet: Writing and editing at any length, multi-step research and synthesis across several sources, drafting policies or internal documentation, customer service responses that require judgment and tone, and most analytical work across sales, marketing, and operations.

Opus: Complex due diligence where errors carry significant consequences, synthesizing conflicting perspectives across long-form documents, high-stakes communications where quality matters more than speed, and tasks where the reasoning chain must be auditable.

In Cadre's experience scaling AI across client operations, roughly 60 to 70 percent of business workflows belong in Haiku or Sonnet. Opus is essential for a meaningful subset of work, but it should be the deliberate choice, not the default.

What Getting Model Selection Right Actually Changes

Cost is the most visible benefit. Running Haiku instead of Opus on a high-volume classification task can reduce per-task cost by 90 percent or more while maintaining equivalent accuracy. At scale, that difference compounds with every workflow added.

The adoption benefit is less obvious but equally real. Opus is slower. Slower responses change how people interact with AI tools. Workflows that should feel instant start to feel like waiting, and teams stop using them. Matching the model to the task makes AI faster where speed matters and preserves Opus capacity for work that genuinely requires it.

The governance benefit pays off over the longest horizon. As part of Cadre's AI Transformation Intensive, model selection policy is one of the first structural elements addressed before any workflow is built. A clear policy creates a standard for new team members, a basis for monitoring spend, and a foundation that adjusts as capabilities evolve.

Conclusion

Model selection is not a technical decision. It is a strategy decision that most teams do not recognize as such until cost becomes a problem. The organizations that get this right treat AI deployment as a repeatable discipline, not a series of one-off choices.

Choosing the right model for each task type is one of the most consistent leverage points Cadre identifies across client engagements. It is also one of the easiest to get right early and one of the most expensive to fix later.

Auditing your current workflows and assigning each a default tier is a one-day exercise. The policy does not need to be perfect on day one. It needs to exist.

If your organization is scaling AI usage and model selection has not been formally addressed, Cadre can help you build the framework. See how Cadre structures AI implementation for mid-market companies that want measurable results from the tools they are deploying.