Something significant happened over the past six months that didn’t get the attention it deserved. AI models stopped just predicting the next token and started actually reasoning through problems. Not perfectly, not reliably enough for every use case, but enough that the distinction matters for anyone building enterprise software.

OpenAI’s o3, Anthropic’s extended thinking in Claude, and Google’s Gemini 2.0 reasoning mode all represent a genuine architectural shift. These aren’t just bigger models — they’re models that can break down complex problems into steps, check their own work, and backtrack when they hit dead ends. For enterprise applications, this changes the economics of what’s worth automating.

Where Reasoning Models Actually Help

The most obvious win is in anything involving multi-step logic. Think compliance checking, where you need to evaluate a transaction against dozens of overlapping rules. Or financial modelling, where assumptions cascade through interconnected calculations. Or legal document review, where understanding one clause depends on interpreting three others.

Traditional AI could handle each of these in isolation — flag a suspicious transaction, extract a number from a spreadsheet, identify a specific clause. But reasoning models can chain these operations together in a way that actually reflects how humans think through problems.

I’ve been watching a few Australian enterprises pilot reasoning models for internal audit workflows. The results are genuinely interesting. One team reported that their reasoning-based review system caught 40% more policy violations than their previous ML pipeline — not because it was better at pattern matching, but because it could follow logical chains that spanned multiple documents and policy sources.

The Cost Question Nobody Wants to Answer

Here’s the catch that vendors aren’t eager to discuss: reasoning models are expensive to run. When a model “thinks” through a problem step by step, it’s consuming compute at every step. A single complex reasoning query can cost 10-50x more than a standard model call.

For enterprise deployments, this creates a genuine architectural challenge. You don’t want every API call routed through a reasoning model. Most queries — data lookups, simple classifications, template generation — work perfectly well with standard models. The trick is building systems that intelligently route only the hard problems to reasoning models and handle everything else with cheaper, faster alternatives.

This routing problem is becoming a discipline in itself. Some teams are building classifier layers that assess query complexity before choosing which model to invoke. Others are using standard models as a first pass, with reasoning models handling only the cases where the first pass has low confidence.

What This Means for AI Strategy

If you’re an enterprise planning your AI roadmap for the rest of 2026, reasoning models change the calculus in a few important ways.

First, the scope of viable automation just expanded. Tasks that previously sat in the “too complex for AI” bucket — anything requiring judgment across multiple factors, context-dependent decision making, or multi-step logical analysis — are now worth re-evaluating. Not all of them will work, but some definitely will.

Second, your AI infrastructure needs to support model routing. Running everything through a single model endpoint is leaving money on the table. You need architecture that can dispatch to different models based on task complexity, latency requirements, and cost constraints. This is where specialists in this space are proving most valuable — helping enterprises build the routing and orchestration layer that makes reasoning models economically viable at scale.

Third, evaluation just got harder. How do you measure whether a reasoning model’s multi-step analysis is correct? Traditional accuracy metrics don’t capture the nuance. You need domain experts involved in evaluation, and you need tooling that can trace the reasoning chain so humans can audit the logic, not just the final output.

The Next Twelve Months

I expect reasoning models to follow a pattern similar to what we saw with large language models in 2023-2024. The capability is proven, the costs will come down as hardware improves and inference optimisation advances, and the real innovation will happen in how enterprises integrate these capabilities into existing workflows.

The companies that move early on reasoning-model architecture — building the routing layers, evaluation frameworks, and human-in-the-loop systems — will have a meaningful advantage. Not because the models themselves will be hard to access (they won’t be), but because the integration expertise takes time to develop.

Reasoning isn’t a feature. It’s a new category of AI capability, and it’s going to reshape enterprise software in ways we’re just beginning to understand.