AI-powered search and zero-click trends are reshaping digital discovery, requiring Australian businesses to optimise content for AI and focus on brand visibility and influence rather than traditional website traffic.

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Beyond the click

Navigating the LLM search evolution

AI-powered search and zero-click trends are reshaping digital discovery, requiring Australian businesses to optimise content for AI and focus on brand visibility and influence rather than traditional website traffic.

Digital discovery is evolving dramatically with the rise of AI-powered search tools, transforming how your audience finds and engages with your content. For Australian business leaders in the digital and technology space, this shift represents both a challenge and an opportunity to rethink your digital strategy. Recent research shows that while traditional website traffic may be declining, opportunities for brand visibility are actually expanding if you know how to adapt.

The zero-click reality

Remember when driving high-quality traffic to your website was the primary goal of digital marketing? Those days are rapidly changing. According to recent Bain research, about 80% of consumers now rely on "zero-click" results in at least 40% of their searches, reducing organic web traffic by an estimated 15-25%.

What exactly is a zero-click search? It's when users get their answers directly on the search results page without ever clicking through to a website. Bain's research shows that about 60% of searches now end without the user progressing to another destination site. Even among those skeptical of generative AI, about half say most queries are answered on the search page without a click.

For Australian businesses, this trend is particularly significant given our highly connected market. The implications of zero-click searches extend beyond just traffic metrics. They're reshaping the entire customer journey.

LLM-based search - the new front door for your content

While zero-click searches represent one significant shift, the rapid adoption of Large Language Model (LLM) tools like ChatGPT, Gemini, Claude, and Perplexity presents another transformative trend.

The data from Bain tells the story: ChatGPT saw a 44% traffic boost in November 2024, while Perplexity reached 15 million monthly users in late 2024. These platforms aren't just gaining users. They're changing how people search for information.

Australian adoption is also significant. Research by Google indicated that as of January 2025, 49% of Australians reported using generative AI in the previous year, up significantly from 38% in 2023. Another study has designated Australia as “the most AI-addicted country” globally as of October 2024, with the highest AI engagement per capita, putting it ahead of global tech powerhouses.

For many Australian consumers and business decision-makers, these AI tools are becoming significant channels for information discovery, potentially bypassing traditional search engines altogether.

Highlighted in the Bain research, and particularly interesting is how people use these tools differently from traditional search:

  • The average ChatGPT prompt is longer and more conversational than typical search queries
  • 70% of ChatGPT prompts are unique and rarely seen in classic search engines
  • LLM users are increasingly turning to these platforms for research (68%), news (48%), and shopping recommendations (42%)

Answer Engine Optimisation

As these trends accelerate, forward-thinking companies are moving beyond traditional SEO to embrace Answer Engine Optimisation (AEO). Think of Google's search results page or ChatGPT's responses as prime digital real estate, like having your shop front on the main street. 

Even if users don't click through to your website, your brand can still make a strong impression and establish expertise.

Practical Strategies to Optimise for LLMs and Zero-Click

Let's explore how businesses can adapt to this new landscape. Here are some focused strategies to increase your visibility in LLM-based search results:

Structure content for AI consumption

Both search engines and LLMs scan content looking for clear, direct answers to user queries. Making your content easily digestible for AI is crucial.

Implementation Tips:

  • Place concise answers (40-60 words) at the top of your content
  • Use clear headings that directly match search intent
  • Structure content with proper HTML tags to help AI understand your hierarchy
  • Break text into bite-sized chunks with bullet points and short paragraphs

Optimise for Featured Snippets

Featured snippets are prime targets for zero-click optimisation and often serve as sources for AI responses.

Implementation Tips:

  • Create concise definitions that directly answer specific questions
  • Use structured data formats like tables, lists and steps
  • Focus on "how-to" content and comparisons that lend themselves to snippet formats

Consider your Robots.txt strategy

An important technical consideration is how you manage AI crawlers through your robots.txt file. This file can control which AI bots can access your content.

Implementation Tips:

  • Major AI platforms have specific user agents you can target:
    • OpenAI: GPTBot and ChatGPT-User
    • Anthropic: anthropic-ai
    • Google's AI: Google-Extended
    • Perplexity: PerplexityBot

However, there's an important caveat - there are reports that some AI companies may be ignoring robots.txt directives. Additionally, there's debate about whether robots.txt even applies to LLMs in the same way it does to traditional search crawlers.

Explore the emerging llms.txt standard

A new proposed standard called llms.txt is emerging specifically for AI content crawling. Created by Australian technologist Jeremy Howard, it functions somewhat like robots.txt but is specifically designed for LLMs. You can read more about the standard and how to implement it here.

Implementation Tips:

  • Consider implementing an llms.txt file that provides:
    • A flattened version of your content without navigation, ads, etc.
    • Context on how your content should be used by AI models
    • URLs to the most important content sections

This approach allows you to provide LLM-friendly versions of your content while maintaining control over how it's presented.

Measuring success in an LLM world

As the digital landscape evolves, so must our approach to measuring success. As Bain suggests, “it's time to shift from click-focused metrics to measuring search impressions and AI reach. Optimise for influence over direct conversions".

Instead of focusing exclusively on website traffic, consider monitoring:

  • Featured snippet appearances
  • AI mentions per category (similar to "Share of Voice")
  • Brand search volume increases after zero-click exposure
  • Cross-channel engagement increases

What it means for business leaders

For technology and digital leaders in Australian companies, this isn't just another digital trend. It's a fundamental shift in how your audience discovers and engages with your brand.

The good news? Early adopters have a significant advantage. Companies that adapt quickly to this new landscape can position themselves as authorities in their space, even as traditional traffic metrics evolve.

The path forward

The rise of zero-click searches and LLM-based tools represents both a challenge and an opportunity for Australian businesses. While traditional website traffic metrics might decline, brand visibility opportunities are expanding for those who adapt.

The fundamental goal remains the same: connecting with your audience and providing value. The channels and metrics may be changing, but businesses that focus on creating genuinely helpful, authoritative content structured for today's AI-enhanced discovery tools will continue to thrive.

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About the author
Ben Howden
Partnerships Director
Ben Howden | Partnerships Director

As a strategist with a background in digital marketing and go-to-market strategies, I build new partnerships for Inlight and help guide digital product strategies. I'm passionate about helping brands develop impactful digital products using modern approaches like composability and MACH.