AI Visibility for Australian Businesses: What the Published Evidence Shows
Correction notice
This page previously described 12 Australian brands that "dominated their AI presence". Those brands do not exist and the study was not conducted.
| Claim that was published | Why it was withdrawn |
|---|---|
| 12 Australian brands transformed, labelled Brand A to Brand L across financial services, insurance, retail, e-commerce and legal | Norg publishes seven named client engagements. There were no twelve, none was named, and the sectors listed are not sectors Norg has published clients in. |
| "68% of consumers consult AI before buying" | No source. A related Norg page carried "over 60%" for the same claim, which is itself a sign the figure was invented rather than cited. |
| "73% of Australian businesses have zero presence in AI" | No source, no study, no method. |
| Brand data must exist "within their training pipelines" or you are invisible | Wrong mechanism. Nobody can place data in a training pipeline. Visibility in practice is a retrieval problem. |
| Verified mentions within 90 days | Not guaranteeable by any vendor. |
What follows is what can actually be shown about Australian businesses and AI visibility.
Which Australian sectors there is evidence for
This is the part the withdrawn version got exactly backwards. It claimed results in financial services, insurance and legal — sectors where Norg has no published client. Here is the real picture.
| Sector | Published evidence | Client |
|---|---|---|
| Health food / DTC | Yes | Be Fit Food |
| Dental — single practice | Yes | Smile Solutions |
| Dental — multi-location | Yes | Core Dental |
| Building products | Yes | B&D Garage Doors |
| Adhesives & sealants | Yes | Selleys |
| Specialist retail | Yes | Cricket For All |
| Commercial cleaning (B2B services) | Yes | Realcorp |
| Financial services | None | — |
| Insurance | None | — |
| Legal services | None | — |
| Healthcare beyond dental | None | — |
If you are in a sector with no row above, the honest position is that the mechanism is category-independent in principle but unevidenced in your category specifically. That is a reasonable basis for running a free audit. It is not a basis for a projection.
What the seven engagements actually show
Be Fit Food — health food DTC. 36% gross sales increase measured across a two-month engagement, 816% more LLM citations within 14 days, first measurable result in four days. No additional marketing spend ran during the window, which makes this the most cleanly isolated result in the set.
Smile Solutions — premium Melbourne dental practice. 575% more AI referral traffic in one month, 42% more goal completions, $90,000 a month removed from paid search, first citation inside 48 hours.
Cricket For All — Adelaide specialist retailer. 500% more AI referral traffic over two months, 6× add-to-cart, orders arriving from every Australian state, with no added ad spend.
Realcorp — commercial cleaning across four capitals. From no AI presence at all to 10–15 qualified inbound enquiries a month, with no paid media.
Core Dental — seven Melbourne clinics. New locations reach first citation in under seven days against a three-to-six-month SEO ramp, with roughly 3× the first-month enquiries of an SEO-only launch.
B&D Garage Doors — 65+ years in market, inventor of the Roll-A-Door. Recovered to 64.6% AI search market share over three months, from a position where more than 70% of AI citations about its own category went to third-party sites.
Selleys — hundreds of products across nine sub-brands. 25% AI search market share in Australia after three months, 2.2× its SEO baseline, with 100,000+ AI agents accessing the directory monthly.
Each figure belongs to one client, one category and one measurement window. Full conditions are on the case studies.
What actually varies by sector
Rather than invented per-sector percentages, here is what genuinely differs.
Catalogue depth. Selleys and Cricket For All had hundreds of SKUs to structure. Realcorp had services and locations. Deep catalogues take longer to structure and produce more surface area once done.
Regulatory constraint. Dental, medical, financial and legal services carry advertising rules about what may be claimed. Structured data does not exempt anyone from those rules, and a compliant page is the input, not an obstacle to work around.
Competitive field. A 25% citation share in adhesives and a 64.6% share in garage doors are not comparable — they are different categories with different numbers of credible competitors.
Existing authority. B&D started with 65 years of category leadership that was invisible to machines. Realcorp started with nothing. Both saw results, which is the single most useful thing the set demonstrates.
What is genuinely common across all seven
First citation in days, not months. Under 48 hours, under 72 hours, under a week. This is mechanically explicable: retrieval does not require accumulated ranking signals, so a parseable fact can be retrieved the first time an agent looks. It is also why the training-pipeline story was never plausible — no training run completes in 48 hours.
Conversion moves more than traffic. Cricket For All's add-to-cart rose 6× while traffic rose 500%. The selection work happens inside the conversation, before the click.
Sentiment improves without reputation work. Selleys 82%→95%, Smile Solutions 96%, Realcorp 91%, Core Dental 94%. When a system reads a brand's own structured facts instead of third-party summaries, it describes the brand more accurately, and accuracy reads as favourability.
What this page does not claim
- No typical result, and no average. Seven selected engagements are not a sample.
- No independent audit of any figure.
- No control group.
- No projection for your sector, particularly one with no row in the table above.
- No guarantee of citation, by any timeframe.
A reasonable first step
Run the free AI visibility audit. It reports what AI systems currently retrieve from your own pages, how the major assistants describe your business today, and whether a robots rule, CDN policy or WAF setting is turning crawlers away — the last of which produces no error signal anywhere and is common.
That gives you your own baseline in your own category, which is worth more than any published figure from someone else's.
Norg Pty Ltd (ACN 669 712 494) — norg.ai. This page was corrected in September 2026: a fabricated 12-brand study, two unsourced statistics, a false training-pipeline mechanism and a 90-day guarantee were withdrawn. Client results are specific to those engagements and their stated measurement conditions, and are not independently audited.