With generative artificial intelligence and Search rapidly becoming more integrated, user engagement is happening in new ways — and in places that traditional tracking tools struggle to measure.

As AI-driven search interactions increase, businesses are finding that conventional SEO metrics no longer tell the full story.

In this article, we explore the cause of gaps in current reporting methods, what this means for your digital presence, and how TDMP is responding to this challenge.

The shift toward AI search: Why SEO metrics are becoming less reliable

AI-powered search engines often bypass traditional search engine results pages. Instead of clicking on web links, users interact directly with AI-generated answers, meaning engagement has moved off-site and off-SERPs, beyond the scope of traditional SEO metrics and analytics tools.

In turn, this leads to unreliable SEO metrics and reporting gaps.

This doesn’t mean that SEO is losing relevance itself: As AI platforms still rely on web crawling, search indexes, structured data, and well-optimised content to generate responses, SEO plays a critical role in gaining visibility in AI search.

However, addressing this measurement gap is key, particularly as Google advance their Gemini AI model. Thankfully, ChatGPT and other AI models now attribute sources more frequently than in the past, making AI referral tracking more viable — an area TDMP is closely monitoring and making strong progress in.

Google Gemini and the rise of mainstream AI search

Google Gemini, Google’s flagship AI model, is at the core of the company’s efforts to redefine search.

Among other things, Google uses Gemini to power AI Overviews, their generative SERP feature that offers a summary response to search queries (mostly informational) at the top of Google’s SERPs.

But the most significant Gemini integrations are yet to come…

AI Mode for Google Search

Now, Google is working on leveraging Gemini to introduce a fully generative version of Search, which will fundamentally alter how users engage with search results.

Like competitors, such as ChatGPT and Perplexity, Google’s AI Mode will reduce website clicks. However, because Google dominates the search market, the scale of this change will be far more significant, leading to a more pronounced reporting gap.

Gemini and Project Mariner

Building on this foundation, Google is also testing Project Mariner, a Chrome-based AI agent powered by Gemini.

Unlike Google’s imminent ‘AI Mode’, which is basically an always-on AI Overview, Mariner can navigate and perform tasks on websites autonomously, moving the cursor, clicking buttons, filling out forms, etc.

This new capability introduces significant challenges:

• Reduced website visits: Information is gathered without users visiting your site directly.
• Task completion without conversions: Mariner can perform actions like filling out forms, leaving businesses blind to user intent.
• Fragmented attribution: Multi-site navigation complicates the ability to track sources effectively.

Voice search adds another layer of complexity

Capable of processing conversational, long-tail queries, as AI search engines become more prevalent, so too will voice search, adding yet another layer of complexity to attribution.

Voice search analytics are currently significantly underdeveloped. Unlike text-based searches, voice queries are often private and less trackable, limiting the data available to marketers. 

This challenge is compounded by the nature of voice search itself: answers are delivered as a single spoken response, bypassing traditional search result pages. 

This lack of visual interaction data and the inability to track user behaviour post-query make attribution difficult, leaving marketers with an incomplete picture of how voice search drives engagement or conversions.

The cost of reporting blind spots

As engagement moves off measurable channels, businesses face:

• Misguided strategies: Without a clear picture of where and how users interact, marketing teams may allocate resources inefficiently or pursue ineffective tactics.
• Missed opportunities: Key insights into audience behaviour and preferences are lost, making it harder to refine campaigns or identify growth areas.
• Declining ROI: When performance metrics no longer reflect reality, businesses risk seeing reduced returns on their digital investments.

These limitations aren't just technical inconveniences; they have the potential to impact revenue, brand visibility, and overall competitiveness — but TDMP can help.

Closing the reporting gap — How TDMP is responding

As AI search disrupts traditional SEO metrics, businesses need smarter ways to track performance, uncover user engagement, and adapt their strategies. While no universal solution exists yet, TDMP is actively developing reporting frameworks that bring clarity to our clients.

Our approach focuses on two key areas:

• AI-specific traffic attribution: We’re testing new GA4 tracking models designed to identify AI-driven referrals, allowing us to differentiate between organic search, direct traffic, and AI-generated visits.
• Integrated performance dashboards: We’re developing custom reporting solutions that differentiate data from traditional and AI-driven sources, ensuring a more complete picture of digital visibility and engagement.

By differentiating AI-driven traffic from traditional sources, we gain clearer insights into how AI search influences engagement — insights traditional SEO metrics miss.

This allows us to refine technical SEO, backlinking, and content strategies, all of which directly impact performance across other attribution channels. Because these elements are interconnected, optimising for AI-driven search strengthens overall visibility.

Don't let outdated metrics undermine your digital strategy. At TDMP, we’re leading the charge in bridging the gap between traditional SEO and the future of AI search. Contact us today.