Solutions

AI and automation advisory

This page covers AI and automation in the back office and across business process: the document handling, the cross-system work and the assistants inside the tools your staff already use. Every platform now ships AI features, and the gap between the demonstration and the deployed result is wide. We assess what a capability does in your environment, what it needs from your data, and whether the pricing model puts risk on you or on the vendor. Customer-facing work in the contact centre is a different decision with different economics, and it is covered on our page for AI agents for contact centres.

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The opportunity

Where AI actually earns its place in a business

Four patterns account for most of the value Australian organisations are getting from AI today. None of them require a research project.

Work that moves between systems

The repetitive handoffs where somebody rekeys data from an email into a system, or from one system into another. Automation here is well understood and measurable in hours returned each week. Checking whether a task needs AI or only needs an integration is the cheapest step in the whole exercise.

Documents and unstructured data

Quotes, invoices, contracts, application forms and correspondence read and turned into structured data your systems can use. Accuracy on your own documents is the entire question, and it can be measured before you commit to anything.

Assistants inside the tools people already use

Drafting, summarising and searching, inside the email client, the document editor or the CRM your staff already have open. Adoption is the risk to plan for, and the licences are often already sitting inside a bundle you are paying for.

The AI already inside your existing licences

Most organisations are paying for AI capability in platforms they own and have not switched on. Establishing what is already available and already funded comes before any new purchase, and it regularly changes what is worth buying.

The decision

Switch on the AI in the platforms you own, or build automation across them

Nearly every AI project in an Australian organisation resolves to this choice, and getting it the wrong way round is expensive in both directions.

The AI inside your existing platforms

Already licensed in many cases, already integrated with your data, and already covered by the security review that platform passed. Capability is limited to what that vendor has built and to the release schedule you sit on. It is almost always the right place to start, because it costs a configuration exercise rather than a project.

Suits first projects, tight budgets, existing Microsoft or Google estates

Automation built across your systems

Workflows that reach into several systems at once and do the work no single vendor covers. It handles the processes that actually consume your team, and the saving can be counted in hours. It needs somebody named to own it after go live, and it breaks when an underlying system changes without notice.

Suits repetitive cross-system work, measurable volumes, an owner in the business

The process

How an automation project runs with us

Five stages, from where the time actually goes through to an owner named for the day after go live. You sign directly with the vendor you choose, and the advisory service costs you nothing.

Find the work worth automating

We look at where time genuinely goes: the tasks done many times a week, to a consistent pattern, on data a system already holds. Most organisations have three or four of these, and they are rarely the ones proposed first in a workshop.

Baseline it before anything changes

Volume, time per task, error rate and rework, measured across a normal period. Without that baseline there is nothing to hold a vendor to and no way to settle afterwards whether the automation paid for itself.

A shortlist that includes what you already own

The capability sitting in your existing platforms goes on the list beside the specialists, scored on the same criteria. Sometimes it wins, which saves you a purchase, an integration and a security review.

A pilot with exit criteria agreed first

One process, a defined period, and success criteria written down before it starts. Pilots without exit criteria run indefinitely and get renewed on enthusiasm rather than on the numbers.

Commercial terms, and an owner named

Pricing model, behaviour at volume, data residency, and who inside your organisation owns the workflow once the implementation team leaves. An automation with no owner degrades within a year and nobody notices until it stops.

Due diligence

What we check that an AI demonstration will not show

Every AI demonstration works. It runs on clean data, a cooperative example and a sandbox that always answers. These are the checks that decide whether it still works on your worst Tuesday.

Accuracy measured on your own data

Tested against a sample of your real documents, records or messages, including the messy ones, before anybody signs anything.

Where your data is processed, and what it trains

Which country the data is processed and stored in, how long it is retained, and whether your material is used to improve the vendor models.

What the automation is allowed to act on

Reading is the easy half. We check what it can write into, which systems it can change, and what approvals sit in front of an action that costs money.

Human review where it matters

Which decisions require a person to approve, and whether that step is quick enough to survive a busy week rather than being clicked through.

The audit trail

What was decided, on what input, by which version of the model, and whether you can reconstruct that in six months when somebody asks.

How the pricing behaves at volume

Per user, per task, per document or per outcome. Each behaves differently as volume grows, and the cheapest at pilot size is regularly the dearest at full rollout.

What breaks when a connected system changes

Who is responsible when an upgrade at one end silently stops the automation, and how quickly you would find out.

Who owns it after go live

Whether your own team can change a rule, a prompt or a threshold, or whether every adjustment is a professional services request with a lead time and a price.

Common questions

Asked on most AI and automation projects

The questions that come up in nearly every first conversation about AI, answered without a qualification call first.

Where does AI belong in the back office?

Three places reliably: reading documents and turning them into structured data, moving work between systems that do not talk to each other, and drafting or summarising inside the tools staff already have open. All three are measurable in hours returned each week, and none of them require a research project.

Why do AI agent deployments underperform?

Usually because the underlying knowledge is scattered, out of date or contradictory. An AI agent answers from what it can reach. Organisations that fix their knowledge base first get results, and organisations that expect the model to compensate for missing information do not.

What is outcome based pricing and should we accept it?

Some vendors now charge per resolved conversation rather than per agent. It sounds aligned, and it can be, provided the contract defines resolution in a way you would recognise. Ask who decides whether a contact was resolved, and what happens when a customer comes back the next day about the same problem.

How do we measure whether it worked?

Agree the baseline before you start. Hours spent on the task each week, error and rework rates, and how long a case takes end to end are the usual measures. Capture them for a normal month before deployment, because reconstructing a baseline afterwards is guesswork and every vendor will offer you their own numbers instead.

Which AI projects pay back fastest?

The ones that remove a repetitive step performed many times a day on data a system already holds. Automated summaries and note taking, document data capture and cross-system handoffs return measurable hours within weeks. Projects that depend on a knowledge base being accurate and current take longer, because the knowledge work has to happen first.

Do we need our own AI models?

Very few Australian organisations do. The capability available inside platforms you already licence, and from specialist vendors, covers the great majority of practical use cases. Building or hosting your own becomes worth considering where data cannot leave your environment, or where a workload is large enough that the running cost outweighs the build.

We are vendor funded and completely free to your business. Always focused on the right outcome.

Find the automation that returns real hours

We find the work worth automating, baseline it before anything changes, shortlist against what you already own, and hold the pilot to criteria set before it starts. You sign directly with the vendor you choose, and our service costs you nothing.