Most Australian businesses using AI settle on a single model for every task, from a one-line customer reply to a complex contract review, because switching feels like added complexity for a saving that seems marginal per call. Multiplied across thousands of calls a month, that default costs real money, and a modest amount of task-based routing recovers a meaningful share of it without touching output quality where it actually matters.
This is not the same as vendor hedging
Running two AI vendors side by side to avoid dependence on one is a business continuity decision with its own costs and benefits, separate from what this is about. The strategy here is narrower and more mechanical: within the models available to you, route each task type to whichever one reliably does that specific job well at the lowest cost, rather than sending every task through the same premium-tier model regardless of how simple it is.
Sorting tasks by what they actually need
Not every business task needs the reasoning depth of a frontier model. Classification, simple extraction, and formatting tasks, is this email a complaint or a query, pull the invoice number from this text, reformat this into our standard template, are handled reliably by lighter, cheaper models. Tasks needing genuine reasoning, drafting a nuanced client response, working through a multi-step compliance question, analysing an ambiguous document, need the fuller model. The mistake most businesses make is not distinguishing between the two and running everything through the expensive option by default.
Audit your last month of AI-assisted tasks and sort them into 'simple classification or extraction' versus 'genuine reasoning required'.
Route the simple category to a lighter, cheaper model and measure whether output quality actually drops before assuming it will.
Keep the reasoning-heavy category on your primary model rather than chasing savings where quality risk outweighs the saving.
Revisit the split quarterly as task mix changes, rather than setting the routing once and forgetting it.
A worked example
A Sydney customer support operation handling around 4,000 tickets a month was routing every ticket, from a simple 'where is my order' query to a genuine complaint needing careful handling, through the same model tier. Splitting the workflow so straightforward classification and templated responses ran on a lighter, cheaper model, while anything flagged as a complaint or an ambiguous request escalated to the fuller model, cut the operation's monthly AI spend from roughly $1,850 to $1,140, a saving of $710 a month, without any measurable drop in customer satisfaction scores on the tickets that mattered.
Where this differs from simply picking a cheaper model everywhere
The failure mode to avoid is swapping every task to the cheapest available model uniformly, which saves money on the easy tasks and quietly degrades quality on the hard ones, usually invisibly until a client notices. A portfolio approach keeps the expensive capability where it earns its cost and only economises where the task genuinely does not need it. That distinction is the entire point, and skipping it is how a well-intentioned cost-cutting exercise turns into a client-facing quality problem nobody planned for.
Building the routing without custom engineering
This does not require a complex orchestration system for most Australian SMBs. A simple triage step, even a lightweight classification prompt that tags each incoming task before it is processed, is enough to route work between model tiers. Claude Cowork workflows can implement this as a first step in a saved skill: classify, then route, then process, with the classification step itself running on the cheapest tier since sorting a task into a category is exactly the kind of simple job that does not need a premium model.
Start with your highest-volume workflow, the one generating the most monthly AI cost, and test a two-tier split against a week of real tasks before rolling it out more broadly. Most businesses running this exercise for the first time are surprised how much of their volume was genuinely simple classification work that never needed the expensive model in the first place.
When a three-tier split is worth the extra complexity
Larger Australian businesses running high volumes across several distinct workflow types sometimes go further than a two-tier split, adding a middle tier for tasks that need more than simple classification but less than full reasoning, a moderately complex email draft, a routine document summary. A Perth logistics operator running freight documentation, customer updates, and dispute correspondence through three separate tiers found the middle tier alone, handling routine customer updates that needed some judgement but not deep reasoning, cut costs by close to $400 a month compared to running everything through their top-tier model, while keeping dispute correspondence on the fuller model where getting the tone and detail right genuinely mattered.
The decision on whether a two-tier or three-tier split is worth building comes down to volume. Below a few hundred AI-assisted tasks a month, the engineering time to build and maintain a third tier usually costs more than it saves. Past a few thousand tasks a month across genuinely distinct task types, the extra tier starts paying for itself within weeks. Most Australian SMBs sit comfortably in two-tier territory, and there is no penalty for staying there until volume genuinely justifies more.
