How to choose an AI consultant in Sydney

Write down the workflow you want to improve, who uses it and what currently goes wrong. For example, an enquiry may move through several inboxes before anyone takes responsibility. That is a more useful brief than simply asking for an AI chatbot.

Collect a few representative examples and describe the desired outcome. You do not need to choose the technology first. A consultant should help determine whether the task needs AI, ordinary automation, a process change or a combination.

Ask what the provider actually built, what their role was and how the result was measured. A working demonstration can help, but clarify whether it is a prototype, a fictional example or an operational client system.

Experience in a large organisation is not the same as delivering the proposed solution for a business like yours. Ask about the people assigned to the project and the technical work they will perform. Where client confidentiality applies, discuss what evidence can reasonably be shared.

A roadmap, a prototype and a maintained production workflow are different purchases. Check which one the quote covers. Ask about the integrations, testing, documentation, training and handover included.

Clarify who owns the accounts, source code and business data, which subscriptions are required and what happens if you change providers. If ongoing support is separate, understand its scope and the responsibilities that remain with your team.

Ask which information leaves your systems, which providers process it and how access is controlled. The answers should reflect the proposed architecture rather than a blanket promise that every AI tool is private.

Also ask what happens when information is missing, an integration fails or an AI answer is wrong. A sensible proposal includes appropriate human review, escalation and a way to recover. Avoid treating a polished demonstration as proof that these cases have been handled.

Choose a baseline before work begins. It might be handling time, rework, missed handovers or the quality of a draft. Include the effort needed to review outputs and maintain the solution when evaluating the benefit.

A manageable first project makes the dependencies and success criteria easier to understand. Agree what would justify expanding the solution, changing the approach or stopping. No provider can credibly guarantee that every AI idea will deliver a positive return.

• Does the proposal address a defined business problem?
• Are the deliverables, exclusions and costs clear?
• Is the evidence relevant and accurately labelled?
• Are data handling, access and human review explained?
• Are ownership, handover and support responsibilities agreed?
• Can you evaluate a smaller first step before expanding?

For help turning a problem into a scoped first project, explore our AI strategy consulting. If you already have a workflow in mind, see AI automation or send an enquiry.