AI-assisted delivery: a good first cut

Yamagigu is Australia's largest First Nations consulting business. A member firm of the Deloitte network and previously PwC Indigenous Consulting, it has spent 11 years working with government, corporates, and Aboriginal and Torres Strait Islander communities, on everything from economic development and social policy to reconciliation action plans.

It is also small: around 50 to 60 people nationally, carrying the accumulated knowledge of roughly 2,000 projects, across more than 800 communities.

That mismatch, a small team sitting on a huge history, is where the problem lived. Before any project begins, a proposal that matches the needs of the client with a credentialled, experienced expert is the first step. Doing so quickly is just good business.

"Being a small business, time is of the essence," says Ching Tan, Partner and Digital and Transformation Lead. "How do we streamline our knowledge so it can be reused? Everything's stored in SharePoint, but it's such a pain to go through and draw information."

Every new proposal meant someone hunting through old files for the right CVs, the right citations, the right slides from past work.

The task ate days, and it ate them from a team with none to spare.

Working with ARM Hub's AI Adopt Centre over the past year, yamagigu built a proposal automation system: a single central source holding the firm's CVs, citations and past project material, which assembles a strong first draft of a new proposal on demand.

Tan is precise about what the system does and does not do.

"It's not supposed to finish it. It's just supposed to compile and get a good draft," he says. "We still collaborate and we still refine."

The gain is real all the same. A job that once took someone two days of gathering citations, checking formatting and copying from old decks now produces a consistent first cut in around ten minutes. The hours handed back go into the work that actually wins proposals: tailoring the response to the client in front of them.

The system went into hands-on testing in February 2026 and has been making a positive contribution since the end of June. The pattern of who picked it up first surprised nobody.

"Our juniors are using it more than our seniors," Tan says. "They're more tech ready."

Those juniors are now the change engine, migrating past proposals into the system and acting as champions across the firm. Above them, the structure ARM Hub recommends for every adoption was in place from the start: a delivery lead who understands change, a partner driving the project, and a CEO who wanted it.

"It's his investment. He wants to drive it," Tan says of the CEO's backing. "We didn't have that many questions asked from other people."

The hardest part, he says, was never the technology. "It's more just how busy people really are. Finding time to implement it." A problem, he notes, that belonged to yamagigu rather than the platform, and one any busy firm adopting anything will recognise.

Tan has a phrase for what his consultants now work with: AI-assisted delivery. The system doesn't replace the work. It assists the delivery of the work they were already doing, smoothing out the repetitive bumps and handing hours back to team members to focus on judgement, relationships, and tailoring the response to the community or client in front of them.

The build itself taught the firm something too.

"Back in the day, you would have to go through a process of prioritising things, scoping things out," Tan says of the year working with ARM Hub. "The velocity has changed a lot. If you can turn around a beta really fast and enhance on it, then we can iterate, and we can do many iterations."

His advice for anyone eyeing the same path comes back to foundations.

"To reap the rewards of this, we need to be diligent on how we store information, where it gets kept."

The next step is a round of enhancements planned between October and December, shaped by what the team asks for now that they've lived with it.

Yamagigu exists to support the self-determination, wellbeing, prosperity and independence of Aboriginal and Torres Strait Islander peoples. The system matters only as far as it strengthens that work. Efficiency, at this firm, is capacity returned to the communities it serves.

Lessons

The concern: institutional knowledge trapped in thousands of past projects nobody can search.

The evidence says: clear data turns your own history into your fastest tool.

The take away: the knowledge is already yours. Structure is what makes it usable.