Opus 4.5 and the Effort Parameter

The question is why an ERP-focused firm should care about a new AI model release like Opus 4.5. Most teams already struggle to get value out of the tools they have. Another model, another feature, can feel like noise.

What’s at stake is how precisely we can control the quality–speed tradeoff in AI-driven work. Until now, most ERP and operations automation has treated AI as a single “mode”: you send a prompt and you get an answer. Sometimes it’s excellent, sometimes it’s rushed, but you don’t have much control other than rewriting the prompt and hoping for better. Opus 4.5 changes that with a new control: the effort parameter.

From a first principles perspective, this matters because different business activities require different levels of cognitive effort. A quick classification for a support ticket is not the same as designing a new approval workflow in NetSuite. We intuitively know this for human work; Opus 4.5 lets us express it explicitly for AI work.

What the Effort Parameter Really Does

Opus 4.5 introduces an explicit “effort” control that tunes how much internal reasoning the model performs before responding. At a high level, you can think of it as a knob between speed and depth:

Low effort: fast, shallow, inexpensive
Medium effort: balanced, reasonable depth
High effort: slower, more deliberate, more multi-step reasoning

Instead of re-engineering prompts every time you want the model to “think harder,” you can use effort as a parameter. That makes AI behavior more predictable and more composable inside systems like ERP, CRM, and workflow engines.

The examples below are written as prompts you might actually use when testing or integrating Opus 4.5. They are intentionally simple. The point is how the same task behaves under different effort settings, not the task itself.

Three Ways to Answer the Same Question

Minimal, Normal, Deep: One Instruction, Three Modes

Consider this instruction:

“Generate the same output three ways: minimal thinking, normal thinking, and deep multi-step reasoning. Compare the differences.”

With Opus 4.5, you don’t just rely on that wording. You can tie it to explicit effort settings.

Low effort version might look like this:

Minimal thinking: One or two sentences, no structure, obvious answer.
Normal thinking: Short paragraph with one main point.
Deep reasoning: A few bullet points with slightly more detail.

The model will not try to unpack edge cases or explore tradeoffs in depth. It behaves like a fast typist finishing a familiar task.

Medium effort will turn into something more structured:

Each of the three modes is explained.
The “deep” version includes a basic multi-step outline.
There is some comparison of pros and cons.

High effort goes further:

Each mode is clearly formalized (maybe as “Level 1/2/3 reasoning”).
The deep version walks through explicit steps: assumptions, alternatives, tradeoffs.
The comparison includes when to use each mode in practice.

In ERP terms, this is the difference between “give me a quick summary of this invoice exception” and “walk through every root cause, related transactions, and policy implications before you answer.” The text of the prompt can be similar; the effort setting makes the behavior different.

Fast vs Deep Drafts on the Same Topic

From Surface-Level to Reasoned Analysis

Another prompt seed for Opus 4.5 is:

“Produce a fast, surface-level draft and then a high-effort, deeply reasoned version of the same topic.”

On low effort, the “fast, surface-level draft” is what you would expect: a short outline, generic phrasing, no data, no real tradeoffs. This is useful when you just need shapes of ideas: email scaffolds, subject line variants, a first-pass description of a NetSuite report.

On high effort, you get something very different:

Clear problem definition and context.
Logical structure that walks from premise to conclusion.
Consideration of failure modes and constraints.
Recommendations tied to specific conditions (“If your approval rules are X, then…”).

In practice, this is where Opus 4.5 starts to resemble a junior systems analyst rather than a writing assistant. For teams designing workflows, approval chains, or integrations, this mode is where you get thinking that can be challenged and refined, not just text to be edited.

Simple vs Strategic Answers on Demand

One Instruction, Two Depths of Strategy

A third useful seed is:

“Give me a simple answer, then a complex strategic answer, both based on the same instruction.”

The low-effort response will prioritize clarity and brevity. For example, if the instruction is “How should we approach automating vendor bill approvals in NetSuite?” the simple answer might be:

Enable basic approval routing.
Define thresholds by amount.
Notify approvers via email.

It’s not wrong; it’s just basic.

The high-effort strategic answer changes the nature of the output:

Maps stakeholders and exception types.
Distinguishes between policy, process, and system constraints.
Proposes phased rollout with feedback loops.
Identifies data points to log for later optimization (approval times, rejection reasons).

Same instruction, different cognitive investment. In human terms, this is the difference between a quick hallway answer and a working session with a whiteboard. With Opus 4.5, you can ask for either, explicitly.

Designing Systems Around Effort

Where Low Effort Belongs

In operational systems, low effort is well-suited to tasks where:

The cost of a shallow answer is low.
You mainly need pattern matching or rephrasing.
Human review is guaranteed downstream.

Examples:

Drafting short email replies for AP status checks.
Classifying support tickets by module and urgency.
Suggesting subject lines for internal release notes.

Here, the priority is speed and volume. You care less about deep reasoning and more about getting a usable first pass that a person can refine.

Where High Effort Belongs

High effort should be reserved for work where:

Decisions are expensive or hard to reverse.
The model must consider multiple constraints at once.
You would normally schedule a meeting to think it through.

Examples:

Designing a new revenue recognition workflow across subsidiaries.
Recommending how to migrate customizations during an ERP upgrade.
Diagnosing recurring reconciliation issues between NetSuite and a bank feed.

In these cases, you want Opus 4.5 to slow down, enumerate assumptions, and show its reasoning path. You can then validate or challenge each step.

What This Means for ERP and Automation Teams

For executives and practitioners, the practical value of Opus 4.5 is not only that it “thinks better,” but that its effort can be tuned per use case. You can standardize patterns like:

“For tier 1 support macros, use low effort.”
“For policy recommendations, use high effort and require human approval.”
“For analytics commentary, start at medium and escalate effort only when confidence is low.”

This lets you treat AI not as a single tool, but as a configurable resource whose depth of thinking matches the importance of the decision at hand.

Ultimately, Opus 4.5’s effort parameter is a small feature with large implications. It turns an abstract idea — “think harder about this” — into something you can express in code and process. What this means is that AI can be woven more precisely into ERP workflows, with clear expectations about where speed is acceptable and where depth is mandatory.

The takeaway: don’t just test Opus 4.5 for accuracy in isolation. Test how low, medium, and high effort behave on the same tasks inside your real processes. Once you see the differences, you can start redesigning work so that the right level of thinking shows up at the right step, consistently.