Complex data.
Clear advantage.

Frontier models are extraordinary generalists, but most enterprise tasks are not general. For classification, extraction, routing, and domain-specific drafting, a small, fine-tuned model is often faster, cheaper, and more private.

Smaller models run closer to your data — even on-premise — which matters when compliance forbids sending sensitive inputs to a third-party API.

When smaller wins

Narrow scope, high volume, latency sensitivity, and strict data-residency requirements all tilt the decision toward a focused SLM over a giant.

Responsible AI fails when it stays a slide of principles. It succeeds when those principles become shippable controls — code, not slogans.

That means identity and access management on every model and tool, audit trails that capture what was asked and answered, and evaluation gates that block a release if quality or safety regresses.

Trust by design

Bake governance into the deployment pipeline from day one. Retrofitting it after an incident is slower, costlier, and far less convincing to regulators and customers alike.

Pilots prove feasibility; they rarely prove value. The gap between a successful demo and measurable business impact is where most AI initiatives quietly stall.

The fix is to define the metric before you build: cycle time, cost-to-serve, conversion, revenue lift. Instrument a baseline, then measure the delta the system actually produces.

Beyond the pilot

Tie every deployment to a number a CFO recognises. Initiatives with a clear, tracked metric survive budget cycles; science projects do not.

For two decades, business process automation meant rigid, rules-based workflows that broke the moment reality deviated from the script. AI-driven automation changes the equation: systems that read unstructured documents, interpret intent, and make context-aware decisions without a human hand-off at every step.

The shift is from automating tasks to automating outcomes. Instead of digitising a single approval step, modern platforms orchestrate an entire process end-to-end — extracting data, validating it against policy, routing exceptions, and learning from every correction.

Where to start

The highest-ROI candidates share three traits: high volume, meaningful variability, and a clear definition of done. Start there, instrument everything, and let measured results — not hype — guide the next wave.

Off-the-shelf AI is the right default for most teams: it is fast to deploy, continuously improved by the vendor, and removes the burden of training and serving models yourself. The real question is when that default stops paying off.

Custom models earn their keep when your data is genuinely proprietary, when latency or cost at scale becomes a constraint, or when a few points of accuracy translate directly into revenue or risk. Below that bar, customisation is usually a distraction.

A practical rule

Prototype on a hosted model to prove the use case. Only invest in fine-tuning or a custom model once you have real usage data showing exactly where the generic solution falls short.

Retrieval-Augmented Generation grounds a model's answers in your own knowledge base. But there are two very different ways to wire it up, and conflating them leads to systems that quietly under-deliver.

A RAG agent follows a fixed pipeline: retrieve, then generate. Agentic RAG hands control to the model — it decides what to retrieve, when to retrieve again, which tools to call, and when it finally has enough to answer.

Choosing between them

Fixed RAG is cheaper, faster, and easier to evaluate — ideal for well-scoped Q&A. Agentic RAG shines on multi-hop, open-ended questions where a single retrieval pass is not enough.

Read the full comparison →