When we established the Sydney Institute of Generative Intelligence (SIGI) as TDS Australia’s dedicated research division, we had one overriding question: what actually determines whether an AI system recommends a business to its users? Not what the industry assumed. Not what the marketing blogs repeated. What the data showed.
After months of rigorous experimentation, our researchers have delivered answers that are reshaping how we advise our clients — and how the wider industry should think about AI-driven visibility. Three landmark papers from SIGI have identified concrete, measurable thresholds that govern whether a business appears in AI-generated recommendations.
The Ratings Threshold: A Hard Cutoff, Not a Sliding Scale
The first breakthrough came from SIGI-2026-001, which examined the relationship between online ratings and AI citation behaviour. The prevailing assumption across the digital marketing industry was that higher ratings always translated into greater AI visibility — a simple linear relationship where every fraction of a star mattered.
Our researchers found something far more interesting. AI recommendation engines do not treat ratings as a smooth continuum. Instead, they operate around distinct thresholds. Businesses that fall below these critical cutoff points experience a sharp drop in AI citation frequency, while those above them enjoy a plateau of relatively stable visibility. The difference between sitting just below and just above a threshold can be dramatic, far outweighing the effect of incremental rating improvements within a given band.
This finding has immediate practical implications for any business investing in reputation management. Rather than pursuing marginal gains across the entire rating spectrum, the strategic priority should be identifying and crossing the nearest threshold above your current position. Our advisory teams now use these benchmarks when developing client strategies, focusing effort where the data tells us it will produce the most significant shift in AI-driven referrals.
Pricing Signals and the AI’s Economic Model
The second piece of the puzzle came from SIGI’s investigation into how pricing information influences AI recommendations, published as SIGI-2026-003. This study revealed that AI systems have developed a surprisingly sophisticated approach to evaluating price signals when generating recommendations.
Rather than defaulting to the cheapest option or the most expensive, AI engines appear to weigh pricing relative to the context of the query and the perceived intent of the user. Businesses whose pricing information is transparent, structured, and easy for AI systems to parse enjoy a measurable advantage over those that obscure or complicate their pricing. The research demonstrated that the way pricing data is presented matters almost as much as the price point itself.
For our clients, this means that pricing page architecture has become a genuine AI visibility factor. Structured markup, clear tier differentiation, and contextual pricing information are no longer just conversion optimisation tactics — they are signals that AI systems use when deciding whom to recommend. Our teams have integrated these findings into our standard site audit process, ensuring that pricing presentation is evaluated through the lens of machine readability as well as human persuasion.
Volume: The Compounding Effect of Digital Presence
The third study in this triad, SIGI-2026-009, tackled the question of volume — specifically, whether the sheer quantity of a business’s digital footprint affects its likelihood of being cited by AI systems. The results confirmed a compounding relationship between the breadth of a business’s online presence and its frequency of AI recommendation.
Businesses that maintain consistent, substantive presences across multiple authoritative platforms see disproportionate gains in AI citation rates compared to those concentrated on a single channel. The research quantified this effect, showing that diversification of digital presence beyond a certain volume threshold triggers a marked uplift in recommendation frequency. This is not simply about being everywhere — quality and consistency remain essential — but about reaching a critical mass of credible mentions that AI systems interpret as a strong consensus signal.
This finding reinforces a principle we have long advocated at TDS Australia: that digital strategy must be holistic. Isolated investments in a single platform or channel leave businesses vulnerable. A broad, well-maintained presence is now demonstrably linked to how AI systems evaluate trustworthiness and relevance.
What This Means for Australian Businesses
Taken together, these three papers paint a coherent picture of how AI recommendation engines evaluate businesses. Ratings, pricing transparency, and digital presence volume each contribute to a composite profile that determines visibility. Crucially, each of these factors operates around thresholds rather than on a purely linear scale, meaning that strategic, targeted improvements can produce outsized results.
We established SIGI precisely because we believed that the future of digital strategy would be shaped by empirical research rather than industry folklore. These findings validate that conviction. As AI-driven discovery continues to grow as a share of how consumers find and choose businesses, the organisations that ground their strategies in evidence will hold a decisive advantage.
Our advisory teams across TDS Australia are already applying these insights to client engagements. If you want to understand where your business sits relative to the thresholds SIGI has identified, we invite you to get in touch. The age of guessing what AI wants is over. The age of knowing has begun.
