The recent Deloitte Australia scandal has once again thrown the spotlight on the dangers of using generic generative AI models without proper oversight. According to reporting by both ABC News and The Guardian (October 2025), Deloitte was commissioned by the Australian Department of Employment and Workplace Relations to deliver a 237-page report into automated welfare compliance systems – a report later found to contain fabricated quotes, references to non-existent academic papers, and even made-up judicial citations. The culprit? Generative AI.
After the errors were exposed, Deloitte admitted that the report had used “a generative artificial intelligence (AI) large language model (Azure OpenAI GPT – 4o) based tool chain” (The Guardian, October 2025) in its production. The firm has since agreed to partially refund the AU$440,000 contract, as academics and politicians alike condemned the misuse of AI in what was supposed to be an authoritative government review.
Generic large language model tools, like ChatGPT, are extraordinary – but they’re not infallible. They are trained on vast swathes of the internet, often including outdated, biased, or unverified data. When asked to produce something that looks factual, these systems can confidently generate citations, statistics, and quotes that simply do not exist. In AI terms, this is known as hallucination.
For creative brainstorming or first drafts, generic AI is a game-changer. But when accuracy, credibility, and traceability matter – as they do in research, government reporting, and strategy – using a general-purpose AI model is like hiring a gifted improv actor to write your audit report.
The Deloitte debacle highlights an important truth: not all AI is created equal. The future belongs to purpose-built AI systems that are designed with domain specificity, data integrity, and accountability at their core.
That’s where Yabble’s Virtual Audiences come in.
Yabble’s Virtual Audiences aren’t just chatbots with personality. They’re AI research agents built to analyze, summarize, and simulate real-world insights from verified, validated data sources – both public and proprietary. Every output is grounded in structured, traceable data rather than guesswork.
With guardrails in place to reduce hallucinations and promote recency bias, Yabble’s platform empowers researchers and strategists to:
Unlike generic AI, which scrapes and predicts, Yabble synthesizes and verifies – turning information into insight.
Like any new tool or process, how an organization trains and implements AI systems is almost more critical than the vetting and procurement of the technology itself. The best AI in the world can still deliver poor outcomes if humans use it without understanding its structure, prompting style, or limitations. As the saying goes: garbage in, garbage out.
Minimizing human error starts with education. Teams need to learn not just how to use their AI tools, but how to use them correctly. The art of prompting is not universal: what works in a generic AI like ChatGPT might fail in a specialized tool designed for structured data and precise insights. While generic systems can often forgive messy or open-ended prompts, purpose-built AI tools reward specificity, discipline, and domain expertise. Effective training ensures organizations unlock the true value of their AI investment – reducing risk, improving accuracy, and amplifying results.
The Deloitte case isn’t just a scandal; it’s a cautionary tale. As organizations rush to adopt AI, the question isn’t whether to use it – it’s how to use it responsibly. Using AI without the right guardrails and training risks misinformation, reputational damage, and wasted investment. Using the right AI, however, can accelerate the path to truth.
In a world flooded with synthetic content and unverifiable data, the ability to distinguish between hallucination and reality is your competitive edge.
If you want to harness AI for research you can trust – not fiction dressed as fact – try Yabble’s Virtual Audiences today.