What do we mean by ‘overtake’?
When we say “overtake” we are talking about the point that generative AI search (searching via chat agents, answer engines, or conversational assistants) handles the majority of informational queries, both in volume of searches and their business impact. We mean when generative AI search displaces classic search engines (i.e. keyword and link results) as the default for both users and brands. We mean when AI-powered search becomes the de-facto first stop for queries about what to do, where to go, and what to buy…
Overtaking also implies that AI answers, not a list-of-links, become the primary entry point for brand and information discovery, and that traditional SEO features (such as rank, clickthrough) become secondary.
Read time: 4 minutes.
What do current trends and data tell us about the rise of generative AI search?
Generative AI search is already gaining share… and at a pace. In the U.S. desktop context, AI-based tools captured 5.6% of search traffic in June 2025, more than double the figure a year earlier and Gartner has forecast that traditional search volume may decline by 25 % by 2026, due to users shifting to AI-powered assistants and agents.
Academic experiments also uncovered that users of LLM-based search tended to complete tasks more efficiently, using fewer but more complex queries and, unsurprisingly, leading to higher satisfaction. However, another large-scale 2025 study found that people are less trusting of generative search over traditional search, but that linking sources and citations in AI responses significantly raise trust.
This evidence (and more) points to a rapid transition in search that is certainly well underway.
What are the possible timelines for a full shift to generative AI search as the primary choice of brands and users?
Looking across technical business studies and academic papers, we have found the most commonly held projections for AI search overtaking traditional search and it seems that a tipping point in the late 2020s is the most likely.
| Timeframe | Projection |
| 2026 | Early inflection: traditional search volume begins sustained decline (Gartner’s 25 % drop) |
| 2027 | AI begins parity in value-generating queries (e.g. high-intent, research tasks) |
| 2028 | AI-powered search could handle 30-50 %+ of queries; crossover in some verticals (e.g. programming, research) |
| 2029 – 2030 | Generative search becomes dominant globally in both volume and business impact |
It looks from this wide consensus that 2028–2030 is the window most likely for full overtaking, although domain-specific or regional exceptions may occur earlier.
Which domains might flip over to generative AI search dominance first?
Generative AI search is likely to surpass traditional search in certain verticals before other catch up:
- Technical research and developer queries:
These users already trust LLMs (e.g. Copilot, StackOverflow assistants) in code-related search, so an evolution into wider search behaviours is natural and inevitable - Academic and scientific research:
Useful synthesis of multiple sources is a great strength of AI assistants, making these valuable research tools, particular as sources are well-cited and easy to trace.
On the other hand, domains that are grounded in local intent, navigation, or very fresh news may lag behind, as link-based results do (and will) provide stronger coverage for dynamically changing content for some time.
What are the key structural barriers to AI search dominance?
Even the most optimistic forecasts will depend on AI search overcoming a few bottlenecks:
Trust & accuracy
- This is the big one. Users are cautious. “Marketers must build topical authority’, says Gartner, with 53 % of consumers claiming that they don’t fully trust AI search results, and 61% wanting the option to disable AI summaries
- The generative trust gap is real. AI algorithms prioritise content that is factually accurate, trustworthy, and comprehensive. Brands, and businesses need to play their part. By creating well-structured content that cites authoritative sources, includes verifiable data (like statistics and expert quotes), and ensuring the content is well-written, easy to understand, and well-structured, AI search results will, in turn, strengthen in credibility (and drive users to your business!).
Feedback and iteration loops
- Traditional search benefits from explicit click data, dwell time feedback, and continual ranking updates. Generative AI search compresses those steps, making fine-grained feedback harder to trace
- That makes it harder to define a clear success metric. The system can’t easily tell whether the answer was genuinely useful, partly wrong, or completely off. Without fine-grained feedback, it’s harder to improve accuracy and relevance at scale, and it’s harder to attribute value to visibility.
Economic model & incentives
- Ad monetisation, data licensing, and content rights will all need rethought in an AI-first search landscape
- Generative AI search doesn’t just change how information is found, it changes who gets paid, who gets credited, and who owns the data that fuels the discovery.
What else could delay a full takeover of Generative AI search?
Two plausible impacts might slow or reshape the transition:
- Regulatory intervention
Governments may demand attribution, copyright protection, or transparency for AI systems, which may slow adoption or impose constraints on content reuse - User backlash
If generative models produce repeated errors or hallucinations (when a generative model produces information that sounds plausible but isn’t true) at scale, user trust may erode, pushing people back toward traditional search engines that they trust more.
Either of these factors could take the expected transition point beyond the predicted 2030 mark or, more likely, reshape it into a long-standing hybrid model rather than full dominance of generative AI-search.
Generative AI search appears to be on track to overtake traditional organic search in volume and business impact around 2028–2030, though domain-by-domain variation looks highly likely. What’s certain is that brands that optimise for extraction-friendly content, third-party citations, and trust signals will survive and thrive during the shift.
Top three takeaways
- The transition to generative AI-first search is likely to be between 2028 and 2030
- Authority through external citation and structured, extractable content are absolutely essential tactics in this new paradigm
- Until generative AI-search dominates (and there is a chance it may not for a while), brands must establish and maintain a hybrid strategy, optimising both for traditional SEO and AI answer engines.
About the author
What we do
Frontier15 helps brands achieve visibility in the era of AI search, creating content strategies that make your expertise discoverable and trusted by both people and machines. Get in touch and ask us how we can map a GEO-informed content strategy that helps future-proof your brand.
Further reading
- How can I measure my brand’s visibility in AI search?
- Can I test how my site appears in ChatGPT, Claude, Co-Pilot, Perplexity or other LLMs?
- What Is Google’s Search Generative Experience (SGE) — and How Does It Change SEO?
- When will generative AI search overtake traditional organic search?
- What is the difference between traditional SEO and AI-driven search optimisation?
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