Google’s AI Overviews are reconfiguring the SERP faster than most advertisers adjust their budgets. When an AI-generated summary captures the answer at the top of the page, organic traffic on informational queries drops significantly. For businesses that rely on organic search or paid search, the question is no longer whether the model is changing, but how to restructure the SEO and media mix around this new interface.
SEO and AI Overviews: Adapting Strategy When Google Answers Instead of the Site
The massive deployment of AI Overviews pushes traditional results lower on the page. BrightEdge, in its analyses reported in 2026, confirms that the available space for organic links decreases with each new query covered by an AI summary. In practical terms, a well-positioned product page or blog post can lose a significant share of its clicks overnight, without any classic algorithmic change.
We observe that short informational queries (“what is”, “how does it work”) are the first to be affected. Transactional or highly specialized queries fare better because Google does not yet systematically generate an AI response for them.
The challenge for SEO teams is twofold. First, target the queries where the AI Overview is not yet displayed, particularly technical long-tail queries, purchase-intent comparisons, and geo-qualified local queries. Second, structure the content to be cited as a source in the AI summary itself. Google announced in May 2026 more visible citations and mentions of subscription content in AI Overviews. Being the cited source becomes a full-fledged SEO objective.
Brands that treat marketing on Business Futur as a strategic monitoring lever are already closely following these developments, as the trade-off between organic content and “AI-citable content” redefines editorial priorities.
Media Buying in Google’s AI Interfaces: An Unstable Advertising Terrain
Displaying ads in Google’s AI responses constitutes a new acquisition channel. Ads appear directly in or around AI Overviews, altering the traditional bidding logic of search ads.
The problem: this terrain is highly regulated and still opaque. Marketers do not have the same level of control over placement as in traditional search. The format, position, and triggering of ads depend on how the AI structures its response, not just on Quality Score or max CPC.
We recommend three concrete adjustments for media buying:
- Segment Google Ads campaigns between queries covered by an AI Overview and queries without an AI summary, to measure the real impact on CTR and cost per conversion
- Monitor impression rates in AI placements via Search Terms reports, and isolate performance variations related to format
- Test shorter, direct-response creatives, suited to a context where the user has already read a summary before seeing the ad
The cost of acquisition in AI interfaces is likely to be more volatile as long as Google has not stabilized the format. Allocating a dedicated test budget rather than shifting all search campaigns remains the most cautious approach.
Regulatory Pressure and AI Citations: What Changes for Brand Visibility
The UK has imposed a regulatory evolution on Google’s AI responses, pushing the company to make source attributions more explicit. This precedent influences how AI Overviews cite content in other markets, including Europe.
Google reacted in May 2026 with several adjustments: more visible citations in AI summaries, better identification of subscription content, and modifications to AI Mode to distinguish editorial sources from generated content. For businesses, this means that the quality of attribution now depends as much on the technical structuring of the site (structured data, author markup, E-E-A-T signals) as on the content itself.
Brands that publish expert, signed, and technically marked content are more likely to be specifically cited in an AI summary. Conversely, generic content without authority signals will be ignored by the citation system.
Technical Structuring to Prioritize
- Implement Schema.org markup (Article, FAQPage, HowTo) on target pages to facilitate extraction by AI systems
- Associate each piece of content with an identifiable author with an indexed profile page, reinforcing E-E-A-T signals
- Maintain a tight internal linking structure between thematic pages to consolidate the topical authority of the domain
- Declare paywall content via appropriate tags to benefit from the new support for subscriptions in AI Overviews
First-Party Data and Client Strategy in a Post-Cookie Context
The gradual disappearance of third-party cookies forces businesses to rethink data collection. SMEs that have not yet structured their first-party database are already losing targeting precision on their display and retargeting campaigns.
First-party data becomes the foundation of any reliable personalization. Forms, customer spaces, loyalty programs, and interactions on social media are the most direct collection channels. The goal is not to accumulate volume but to qualify each contact with actionable attributes (purchase history, channel preferences, engagement frequency).
CRMs connected to activation tools (segmented emailing, custom audiences on Meta or Google) allow for maintaining advertising performance without relying on third-party data. For B2B companies, coupling CRM with LinkedIn Ads offers targeting by function, industry, and company size that more than compensates for the loss of cookies.
Digital marketing in 2026 plays out on three simultaneous fronts: the ability to exist in Google’s AI responses, mastery of new advertising formats in these interfaces, and the strength of the proprietary database. Companies that treat these three axes as a coherent system, rather than isolated projects, are the ones that will keep their acquisition costs at a sustainable level.
