AI and the SEO Industry: A Global Shake-Up for Agencies and Their Clients
Artificial Intelligence is reshaping SEO from the ground up. What was once a stable mix of keyword research, link acquisition, and technical audits now includes a new reality: AI answers increasingly sit between users and websites. For agencies and their clients, the question is no longer only “How do we rank?” but also “How do we get referenced by AI systems?”
From rankings to citations: the new battleground
For years, success was measured by page-one rankings. Today, AI-led experiences such as AI Overviews, chat assistants, and answer engines often present summaries first. Visibility is not only about position in the SERP, it is about whether your brand is referenced or cited inside those AI answers.
According to a recent Forbes Analysis, AI summaries can reduce traditional organic click-through rates by up to 70%. This means that even top-ranked pages may see dramatic drops in traffic if they’re not referenced by AI systems. SEO agencies must now optimise content not just for search engines, but for large language models (LLMs) that synthesise information in real time.
The practical impact is clear. Even well-ranked pages can see fewer clicks when an AI answer satisfies the query. Winning now means making your content easy for machines to understand, trust, and reuse responsibly.
What this means for SEO clients
If your business relies on organic search for leads, sales, or brand discovery, expect:
- Lower traffic with similar rankings. More questions are answered before a click happens.
- Shorter decision journeys. Users compare options within AI interfaces and click fewer results.
- More competition for attention. Publishers with clear structure and authority can win citations even in niches they never dominated.
Clients still need rankings, but they also need AI visibility. That means appearing in AI-generated answers for priority topics and queries.
SEO agencies: reinventing the toolkit
Agencies must broaden their approach beyond keywords and links.
- Optimise for entities, not only keywords: Make brand, products, locations, and expertise unambiguous. Align site content, internal links, structured data, and third-party profiles so machines recognise who you are and what you do.
- Structure content for safe reuse: Use clear headings, concise summaries, Q&A blocks, tables, and step-by-step sections. This helps assistants extract accurate passages and reduces misinterpretation.
- Prove credibility: Add author bylines, expertise signals, first-party data, and transparent sourcing. Content with evidence is more likely to be referenced by AI systems and trusted by users.
- Tighten technical foundations: Maintain fast pages, clean crawl paths, canonical clarity, and robust schema. Reduce duplication and content cannibalisation that dilute authority.
- Monitor AI surfaces: Track how often brands and URLs appear within AI answers for target prompts, then close gaps with new or improved content.
Global impact: not one-size-fits-all
Digitally mature markets are adapting quickly with AI-focused audits, retraining, and new services around entity SEO and LLM monitoring. In emerging markets, adoption is uneven due to tooling access, search behaviour differences, and language bias in AI models. English-language sources often dominate, so non-English brands may need extra effort to be represented.
Content strategy: quality, proof, and originality
AI has raised the bar for what “good content” means. Thin rewrites add noise and risk being ignored by both users and assistants. Prioritise:
- First-party research and data such as surveys, benchmarks, calculators, and case studies.
- Expert opinions with named authors and clear credentials.
- Unique insights drawn from real experience, not generic summaries.
- Practical usefulness with steps, frameworks and checklists that genuinely help.
Use generative tools to accelerate drafts and outlines, then apply human editorial judgment for accuracy, nuance and brand voice.
New KPIs for an AI-shaped landscape
Traditional metrics remain useful but are no longer sufficient. Add:
- AI citation presence: How often your brand or specific URLs are referenced in AI answers for agreed topics.
- Prompt match rate: The percentage of common questions where your pages provide a direct, high-quality answer.
- Entity coverage and consistency: Completeness and alignment of business attributes across your site, schema, and major profiles.
- Cited-content share: Which pages earn references from assistants compared to those that only rank.
- Post-answer performance: Conversions from users who arrive via AI interfaces or branded follow-up searches after an AI interaction.
What agencies and clients should do now
- Run an AI visibility audit to see where brands appear or are missing in AI answers.
- Invest in entity and schema work so machines can identify and trust your expertise.
- Create differentiated, evidence-led content that assistants can cite with confidence.
- Educate stakeholders on the shift from rankings alone to rankings plus references.
- Track AI-era metrics alongside traditional SEO KPIs and iterate monthly.