Case studies · real client engagements

Case studies from real AI consulting work

Advisory work is confidential, so we describe clients by industry rather than by name. The engagements are real, the numbers are real, and private references are available with the client's consent.

CF-01AI Usage Review2026

ClientAustralian health services business

Sector

Health services

Team

Whole team in scope

Engagement

AI usage review

Deliverable

Usage snapshot + adoption path

AI usage review for an Australian health services business

The situation

The business handles sensitive health information, and its enterprise clients run their own assurance checks on the systems and suppliers they rely on. Staff were already using AI day to day, and there were no written rules about what was allowed. Leadership did not want to slow anyone down. They wanted to know what was actually happening before deciding anything.

What we did

01Surveyed the team on how they actually use AI, tool by tool, task by task, with room for the honest answers.
02Mapped the platforms in use across the business, work accounts and personal ones alike.
03Read the results against the data the business holds and the assurance expectations its enterprise clients set.
04Recommended a sequence rather than a tool list. Ground rules first, then a proper look at where AI fits, then a plan, then training.

Where it landed

Staff wanted guidance more than anyone expected. The sharpest question about data safety came from reception rather than management, so the recommendation started where the appetite was. A short plain-English policy and an approved list of tools came before anything else.

Leadership got a snapshot they could read in one sitting, and a six to twelve month adoption path that starts with the admin work staff already do, done on tools the business has approved.

CF-02Data, Privacy & AI Advisory2026

ClientAustralian business running on a third-party SaaS platform

Sector

SaaS

Subject

Third-party SaaS platform

Engagement

Independent platform advisory

Deliverable

31-page advisory report

Data, privacy and AI review of a third-party SaaS platform

The situation

The client runs its business on a third-party SaaS platform that holds its most sensitive client data, and wanted an independent look at it, an advisory review rather than a penetration test. How does the platform actually handle our data? What is its AI footprint? And does the assurance evidence stand up when an enterprise customer asks for it?

What we did

01Sat with the team on site and watched the platform used in anger, then put a structured question set to the platform's developer.
02Worked through the vendor's policy library, its independent penetration test results, and a third-party risk assessment one of the client's own enterprise customers had commissioned.
03Mapped how AI is used inside the platform and what data reaches it.
04Rated every area for likelihood and impact, and wrote it up in plain English for leadership rather than for engineers.

Where it landed

Leadership ended up with a report they could actually use. Every area was rated, every rating was explained, and the reasoning sits on show rather than buried in an appendix.

The practical output was a prioritised conversation list for the vendor. The client now knows exactly what to ask, in what order, and what evidence to expect back, which is the position their own enterprise customers expect them to be in.

Why no names

Why we publish industries rather than client names

Advisory work only functions if clients can speak freely, so we publish the shape of the work, what an engagement covered and what the client walked away with. The detail stays in the report, where it belongs. References are available in private, with the client's consent, when you are weighing us up for similar work.

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private references available with client consent.