AI Search Optimisation: How Generative AI Is Reshaping SEO for SaaS

The digital landscape is experiencing a profound shift as generative AI transforms traditional keyword-based searches into conversational experiences. Users now pose natural, complete questions, expecting immediate, synthesised answers rather than traditional rankings and clicks. This represents the most significant change in search behaviour in decades, fundamentally altering how users discover and engage with online content.

Google’s Search Generative Experience (SGE) exemplifies this transformation, delivering comprehensive conversational responses that fundamentally change user interactions. Instead of typing fragmented keywords and scanning through results, users now engage in natural language conversations with AI systems that remember context and personalise responses. For instance, a user might ask, “What’s the best channel manager for my boutique hotel in The Rocks in Sydney?” followed by “What about revenue management and optimising my rates?” without needing to restate the initial context. The AI maintains session memory, making search more like an ongoing, personalised consultation than isolated keyword queries.

This shift from “10 blue links” to conversational answers announced by Google today poses critical challenges for businesses: How do you maintain visibility when AI intermediaries filter and synthesise information? How do you ensure your content is selected as a trusted source for AI-generated responses? For SaaS marketers especially, this transformation demands a complete rethinking of SEO strategy beyond traditional keyword optimisation.

Google traditional Search providing sponsored and ranked links

Gemini AI search providing a summary with reference links to passages highlighted

The Dual Architecture Driving AI Search Visibility

First of all, it is critical to understand that AI search systems operate on a sophisticated dual architecture consisting of pre-trained knowledge and real-time search augmentation. This two-tier architecture defines how information is selected and presented — and thus how SEO must evolve to influence each layer.

Pre-trained Knowledge Base

AI models like GPT-4o, Claude3.7, and Gemini Pro 2.5 encode vast amounts of information within their neural network parameters during pre-training, creating an implicit understanding of source authority, credibility, and domain expertise. This pre-training effectively makes the model a knowledge base of facts, language patterns, and associations, meaning an AI can answer many questions from its own parametric memory without needing to look anything up.

During pre-training, models develop preference patterns for sources they encountered frequently in high-quality content. This creates an outsized advantage for established sites that were well-represented in the training data. Research by the Tow Center for Digital Journalism found that 92–93% of pages cited by AI engines have fewer than 50 referring domains, suggesting that traditional SEO metrics like backlink profiles may be less influential than representation in pre-training data.

However, pre-training is static — a snapshot frozen in time at the dataset cutoff date. The most common models have knowledge cutoffs ranging from on average 6–24 months before the current date, creating significant visibility challenges for newer companies or information that emerged after these cutoffs. For example, GPT-4’s knowledge was initially limited to data available through 2021–2022, meaning it might still “think” and report that a given product offering is missing capabilities since introduced from that period, unless updated through other means.

In effect, the pre-trained model contains a latent hierarchy of information importance drawn from historical online content. SEO influence at this layer comes through long-term authority building — content that gets cited extensively across the web (in articles, forums, reference sites) has a better chance of being embedded in the model’s memory, akin to being part of the AI’s knowledge graph. For instance, if your site’s explanation of a technical concept became the most linked resource online, an AI might answer questions about that concept using information similar to your content even without live retrieval.

Real-time Search Augmentation

To stay relevant and accurate, the more recent AI search systems use real-time search augmentation — essentially performing live searches or data lookups at inference time (when a query is asked by the user). This process, often called Retrieval Augmented Generation (RAG), grounds the AI’s answers in up-to-date information. Instead of relying solely on memory, the AI issues search queries, reads content from the web and other connected sources, and integrates that with its own knowledge to formulate a response.

This technique enhances LLM outputs by retrieving relevant current information from external sources and incorporating it into the response (answer) generation process. For SaaS companies, in theory this creates opportunities to be discovered regardless of pre-training representation. Sophisticated algorithms determine which sources to retrieve and cite based on relevance, authority, semantic matching, and other factors, with preferences often shown for comprehensive, factual content over marketing materials. However, testing on my end using a number of models highlight that the original product ranking of the pre-training product (disabling search) strongly influences what online sources are looked up and included as references. The model specific original product ranking itself did not change!

Let’s look at how different platforms implement this grounding:

OpenAI’s ChatGPT with online “search” mode selected, automatically triggers web searches when user questions ask for specific information. It fetches relevant pages using Bing search as a provider and will cite the sources. As of late 2024, ChatGPT produces answers with footnoted web sources, linking to news articles and blog posts via a “Sources” sidebar. For complex queries, ChatGPT’s “Deep Research” agent conducts and synthesises extensive searches, executing often hundreds of searches autonomously, following links and refining queries.

OpenAI providing model choice, a Search choice and Deep Research capability

Leverages Microsoft Bing as primary search provider
Features “Deep Research” capability for conducting and synthesising extensive searches
Behaves as an “authority seeker” — heavily favouring high-authority, fact-focused sources like Wikipedia, reputable news outlets, and well-known reference sites
Largely avoids user-generated content and rarely cites ecommerce or company blogs
Partners with publishers (e.g., Le Monde, Reuters) for direct content feeds

Anthropic’s Claude introduced online search in March 2025, allowing it to pull in current information. When enabled, Claude appends relevant snippets from the web with direct citations in its responses, ensuring users can verify sources and helping keep Claude’s answers up-to-date. Claude employs a “hybrid reasoning model” approach with strong focus on providing verifiable information and clear citation practices. Claude also features an agaentic “Research” mode, that will run multi-step agentic web searches across hundreds of web sites.

Anthropic providing web search and thinking choice, as well as a Research capability

Added web search capability in March 2025
Research functions agentically, conducting progressive searches based on earlier results
Employs “hybrid reasoning model” approach with Claude 3.7 Sonnet
Places strong emphasis on verifiable information with clear citation practices using inline citations within responses
Offers integrations with user data (e.g., your docs, with permission) for personalised context

Perplexity.ai was the first AI search service built around real-time AI augmentation from the start. It performs multiple searches for each query and synthesises answers with footnotes linking to the source of each fact. In its “Research” mode, Perplexity conducts dozens of iterative searches and can reads hundreds of sources autonomously to deliver comprehensive reports. In contrast to the other models, Perplixity supports user choice of models to be used and control over the types sources to be included in a given search.

Perplexity providing model choice and source type choice, as well as a Research capability

Utilises a combination of proprietary models and third-party models (Sonar, Claude 3.7 Sonnet, GPT-4.1)
Implements “set sources for search” feature allowing users to narrow searches to specific types of content (academic, social, video)
Shows preference for sources with established domain age (10+ years)
Operates as an “expert curator” often quoting expert review sites or niche authorities rather than broad general sites
Infrastructure powered by Amazon SageMaker and NVIDIA hardware

Google’s Gemini leverage Google’s formidable search index directly and now offers an AI first Search via its Pro tier. In addition, Google’s Search Generative Experience displays AI-generated answers atop search results, often with small citations or “about this result” links. Google’s Gemini AI introduced its own “Deep Research” agent for Advanced tiers that creates multi-step research plans and iterates through hundreds of searches and web pages.

Deeply integrated with Google Search infrastructure and Knowledge Graph
Directly incorporates Google’s existing ranking systems and quality evaluations
AI Overviews appear in 74% of problem-solving queries, taking up 42% of screen space on desktop and 48% on mobile
Described as a “balanced synthesiser” that mixes authoritative sources with community input (e.g., blogs, videos, forum threads)

As discussed, the interaction between pre-training data and inference-time search creates a complex environment that rewards different factors than traditional SEO. SEO keywords are rapidly becoming secondary to demonstrating genuine expertise, comprehensive coverage, and factual accuracy.

Fragmented AI Search Platforms

The different AI search platforms use different methods and capabilities, depending on the specific model and tool configuration selected by the user. Understanding the nuances of each platform is crucial for optimising content visibility.

Google-to-Bing Shift

A significant trend across AI search platforms is the shift from Google’s proprietary index (powering Gemini models) to Microsoft Bing as the underlying search provider for all other major AI platforms. OpenAI’s ChatGPT, Perplexity, and Claude now all rely on Microsoft Bing as their primary search engine.

This shift has significant implications for marketers, as optimising for Bing’s algorithms is now the most important way to indirectly improve visibility across multiple AI search platforms. If you’re not optimised for Bing, your content might be essentially invisible to these AI systems, regardless of how well it ranks in Google. Microsoft’s early investment in OpenAI and willingness to provide API access to its search index has positioned it as the backbone for AI search systems.

Conversational Discovery and User Behaviour Shifts

AI-driven conversational searches have reshaped user behaviours and expectations in ways that fundamentally impact how content should be structured and optimised.

Longer, Conversational Queries

Traditional search has long relied on users adapting their queries to match search engine expectations through keyword-based searches. AI search tools are reversing this dynamic. Google’s internal data shows “the volume of searches with five or more words grew 1.5X as fast as shorter queries” in 2023–2024 compared to the previous year. This trend has since only increased. Users are increasingly formulating queries as complete questions rather than fragmented keyword strings, with studies showing AI-enabled chat experiences are 66% longer than traditional search processes and mostly involve fully formulated questions. Users also refine queries and delve deeper into topics in an ongoing conversation.

For marketers, this shift requires content strategies that anticipate and address the specific questions users are likely to ask, including complex multi-part questions that weren’t practical in traditional search. This means specifically targeting long-tail, high-intent keywords with much greater specificity than before. For example, rather than optimising for broad terms like “channel manager,” successful hospitality tech SaaS companies now target phrases like “best channel manager for boutique hotel in Australia 2025” or “channel manager that connects with dominant chanel in source market X” These highly specific queries directly match the conversational way users now interact with AI search systems.

Search Pattern Opacity

Unlike traditional search data, which has some public transparency through tools like Google Search Console and Google Trends, AI query patterns are largely opaque to the outside world:

While Google Trends provides “access to a largely unfiltered sample of actual search requests”, there are no comparable public tools for AI search systems today. The conversational nature of AI search creates data structures fundamentally different from traditional search logs, making them more sensitive and less suitable for public aggregation. In particular search prompts by enterprise users are contracturally protected and cannot be shared.

This lack of transparency creates challenges for traditional keyword research and content optimisation strategies. If millions of people start asking AI systems about topics relevant to your business, neither Google Trends nor any SEO tool will reflect that surge. Those queries aren’t hitting a public search engine index anymore, they’re asked behind the closed walls of AI systems.

Several strategies can help navigate this opacity:

Focus on underlying intent and assume continuity — people’s fundamental questions aren’t entirely new, just expressed more naturally
Create content tailored to long-form questions — FAQ pages, how-to guides, and comprehensive resources addressing detailed questions
Monitor referral traffic from AI platforms when it does occur — identify when clicks are coming from chat.openai.com, bing.com/chat, or other AI interfaces
Use social listening — users often discuss how they use AI on forums, Reddit, or social media

Source Concentration and Lower Click-through Rates

Another notable shift with AI answers is that they tend to draw from a smaller set of sources compared to a traditional SERP. In a classic Google search, a user might scan 5–10 different results (or more) and each site gets a chance to capture the click. In AI-generated responses, however, the AI agent will synthesise an answer from just two or three main sources.

Multiple studies highlight how AI search tends to cite fewer, more concentrated sources compared to the 10+ links provided by traditional search results. Research analysing 40,000 AI responses containing 250,000 citations found that AI search engines heavily favour certain types of content, with product-related content dominating AI citations (46–70% depending on query type). AI search engines show “a definite preference for stronger domains,” with higher domain authority sites receiving disproportionate representation in responses .

The introduction of AI search features is also significantly reshaping traffic patterns. Analysis of 300,000 keywords found that “the presence of an AI Overview in search results correlated with a 34.5% lower average clickthrough rate for the top-ranking page”. While the average CTR drop was 15.49%, branded queries actually saw CTR increase by 18.68% when featured in AI Overviews. Despite overall CTR declines, businesses cited within AI Overviews saw improved performance, with both paid and organic CTRs improving “substantially for brands that were cited”. Search impressions increased by 49% year-over-year, while click-through rates declined by 30%, indicating users are seeing more results but clicking less frequently.

All this suggests that being cited within AI search responses may become as important as — or more important than — traditional organic rankings, representing a fundamental shift in how companies should measure SEO success.

Leveraging Google’s E-E-A-T Framework

The E-E-A-T framework (Experience, Expertise, Authoritativeness, Trustworthiness) also significantly influences AI search visibility, with each component playing a crucial role in how AI systems select and prioritise content:

Google’s addition of an extra “E” for Experience in E-E-A-T underscores that who and how content is produced matters for quality. AI search, which tries to surface reliable information, likely uses these signals heavily when choosing which content to trust and cite.

Here’s how each element influences AI search visibility:

Experience: Content demonstrating first-hand experience is increasingly valued by AI systems. For example, a travel article written by someone who actually visited the location and provides original photos or insights might be deemed more reliable. To build this signal, encourage content from practitioners with direct experience. If you’re in healthcare, have doctors or patients share insights; if in tech, have engineers write technical explainers. Label content clearly with author profiles and “expert review” tags that AI systems can parse.

Expertise: This reflects the knowledge level of content creators, with credentials particularly important for sensitive topics. From an optimisation standpoint, showcase credentials through author bios with clear qualifications. Implement schema markup indicating relevant expertise (e.g., indicating Dr. Jane Doe is a board-certified dermatologist who wrote your skincare article). When AI scans content, it gives weight to such signals of specialised knowledge.

Authoritativeness: This correlates with your site’s overall reputation in its niche. Are you recognised as an authority by other experts? This combines traditional SEO factors (quality backlinks, brand recognition) with broader signals like being mentioned by other authorities. If your site appears frequently in trusted contexts (news articles, academic papers), the AI might have learned that you’re a go-to source for that subject.

Trustworthiness: Perhaps the most critical element, as any hint of deceptive content or misinformation can cause an AI to skip your content entirely. Ensure accuracy and transparency by citing your sources, keeping content updated, using HTTPS, providing clear disclaimers when needed, and maintaining consistency across your content. AI systems evaluate trustworthiness through both direct signals and consistency with known facts.

From a practical perspective, implement these technical approaches to strengthen E-E-A-T:

Add author and reviewedBy schema to articles:

Implement content fact-check panels where applicable
Link out to authoritative references (citing reputable sources can improve your own credibility)
Ensure user-generated content on your site is monitored and credible
Develop off-site E-E-A-T through Wikidata entries, Google Knowledge Graph inclusion, and industry association memberships

In summary, E-E-A-T is your moat in an AI-driven search world where low-quality content will be filtered out algorithmically. High E-E-A-T not only helps you in classic SEO ranking but also increases the likelihood that AI systems select your content as the basis for answers.

5-Point Framework for AI Search Optimisation

SaaS companies should strategically optimise for AI search using a comprehensive framework that addresses both technical and content considerations. This approach must encompass structured data, content architecture, and measurement evolution.

1. Leveraging Structured Data for AI Comprehension

Implement comprehensive schema markup to help AI systems understand your content’s context and meaning:

Ensure every piece of content that could be a rich result or answer is marked up with appropriate schema. Use FAQ schema for Q&A content so AI can extract Q&A pairs easily — evidence suggests structured data surfaces faster in AI answers than traditional SEO updates, giving you a potential first-mover advantage:

Use HowTo schema for instructional content so AI might present step-by-step answers. Mark up products with Product schema so AI knows key facts like price, availability, and reviews:

Implement Article schema with headline, datePublished, author, publisher — especially for news or informative posts.

Proper schema implementation creates machine-readable context that helps AI systems correctly interpret and potentially feature your content. For SaaS companies specifically, marking up features, pricing, and integration capabilities can help AI systems accurately represent your offerings when answering relevant queries https://xponent21.com/insights/optimize-content-rank-in-ai-search-results/.

2. Building Topic-Focused Content Architecture

Shift your site architecture thinking from pure keywords to topic clusters and entity relationships. Identify key pillar pages that cover broad topics comprehensively. Around them, create cluster pages that dive into subtopics or answer specific questions. Create diverse content types within these clusters — comprehensive blog posts, how-to guides, checklists, comparison matrices, case studies — and link them contextually. This not only helps traditional SEO but ensures an AI agent following a chain of questions can find all answers within your network of content.

Create a robust internal knowledge base or glossary for technical terms relevant to your industry. For example, if your SaaS product uses specialised terminology, create dedicated pages for each term with clear definitions (and use DefinedTerm schema). AI might directly use these definitions when answering user questions about your product category.

Structure each page to clearly target a specific entity or intent. Ambiguous pages that mix many topics could confuse AI. Splitting content logically with clear headings helps AI pick the relevant chunk to answer a user query.

3. Creating Question-Driven, Conversational Content

Use tools to mine common questions (Google’s People Also Ask, forums, your site’s search queries, etc.). Then produce content whose titles are those questions and that answer them directly. A title like “How do I troubleshoot X in your SaaS product?” written as an explicit question can be very AI-friendly. Maintain an updated FAQ section on key pages. Not only does FAQ schema boost snippet potential, but AI might use the Q&A pairs in its training or real-time answers.

Embrace a conversational tone where appropriate. While maintaining professionalism, write as if directly answering a user question. This increases the chance of your text being selected as a direct answer since it “sounds” like a response. For example, start answer paragraphs with phrases like “Yes — in fact, you can use X to achieve Y…” rather than burying the answer mid-paragraph.

Include context in your answers. If the question is about your SaaS product’s integration capabilities, a great answer might be: “Our platform integrates with over 50 popular tools including Salesforce, HubSpot, and Slack through our API…” This way, if an AI quotes you, it’s a fully meaningful sentence on its own.

4. Multilingual and Multimodal Optimisation

AI models are multilingual. They will translate on the fly. This means a user asking in Spanish could still get an answer that comes from an English source, translated by the AI. Don’t shy away from having content in one language just because your primary audience is another — if it’s best-in-class content, AI might is likely to use it for users of other languages (and translate it in the process).

However, creating multilingual content for key pages can improves your chances of being the preferred source in local searches. Use hreflang tags and clear language indicators so search engines and AI systems know your multilingual pages:

Additionally, incorporate multiple content formats beyond text. Include relevant images/diagrams with informative filenames and alt text. AI might “see” these if it has vision capabilities (like GPT-4o vision or Gemini Pro 2.5). Provide transcripts for videos and audio. If you have a podcast or video, publish the transcript or a detailed summary. AI will pick information from it. Experiment with formats like infographics or tables of data. These can sometimes get extracted into answers.

5. Measurement Evolution for AI Search Success

For SaaS companies, implement a comprehensive measurement framework that includes tracking AI search visibility and citation rates across platforms, monitoring AI-referred traffic through specialised analytics, and analysing which content elements are most frequently cited to expand on those areas.

Traditional SEO metrics need to be supplemented with new often qualitative measurements focused on AI visibility: Track AI referral traffic separately by setting up filters in analytics for known AI agent referrers (e.g., chat.openai.com, bing.com/chat, etc.) to see traffic coming via those channels. Monitor search ranking vs. AI inclusion. You might rank #1 but still get fewer clicks if AI answers take over. If possible, monitor whether your content is being cited.

Introduce metrics like “AI Visibility Score” — a composite that measures how often your content appears in AI results across key queries. You might approximate this by periodically querying AI platforms for your main keywords and noting citations.

Keep an eye on conversion attribution by capturing if users mention or arrive via AI. For example, add a “How did you hear about us?” field in important lead forms with “Chatbot/AI” as an option. This helps understand the growing role of AI in your customer acquisition funnel.

Future Trends in AI Search

The evolution of AI search is rapidly evolving, with several emerging trends likely to shape the landscape in the coming months:

Multi-modal integration: AI systems are increasingly incorporating image, voice, and video inputs alongside text, expanding content discovery beyond traditional text-based queries. SaaS companies should prepare by optimising visual assets, creating video content, and ensuring accessibility across all modalities.
Enhanced personalisation: AI search is becoming increasingly tailored to individual users based on their chat history, preferences, model selection, tool selection and behaviour patterns. This personalisation will influence how AI systems select and present content, requiring search optimisation strategies that account for different user segments and contexts.
Improved citation accuracy: Current issues with fabricated or incorrect citations (studies show 60–94% of queries resulted in at least one incorrect citation) will likely be addressed through improved verification mechanisms and transparency. Hallucinations however will continue for the foreseeable future.
Agent-based search: More sophisticated AI agents that can perform multiple searches, follow links, and synthesise information across hundreds if not thousands of sources will further transform how users discover SaaS solutions. These agents will conduct deeper, more thorough research on behalf of users, placing greater emphasis on comprehensive, factual content often provided by third parties.

Organisations may need to create new roles or teams for AI Search Optimisation. This might include an “AI content liaison” who works with AI platforms (like coordinating data partnerships or monitoring AI references to the brand), or content strategists who specifically craft content likely to be used by AI. PR and SEO will intersect more, as getting cited by authoritative third-parties boosts AI visibility. We might also see investment in first-party AI: many organisations will deploy chatbots on their own sites using LLMs trained on their content.

The evolution of SEO tools will continue, with traditional rank tracking expanding to include whether an AI box appeared for a query and which sources were cited. Often, these will however rely on synthetic searches that cannot accurately reflect a real user search scenarios. On the production side content optimisation tools could score text not just for keywords but for “AI readiness” (readability, answer-like snippets, schema completeness). New analytics will likely emerge to estimate AI impressions and measure content inclusion in synthetic answers.

FAQ

What is AI Search and how is it different from traditional search?

AI Search uses generative AI to deliver conversational, synthesized answers instead of a list of links. Users ask full questions and receive direct responses — similar to chatting with a knowledgeable assistant, not scanning a results page.

How does AI Search impact SEO for SaaS companies?

It requires a shift from traditional keyword tactics toward building authoritative, well-structured content that demonstrates expertise. AI prioritizes trustworthy, well-formatted answers that are easy to reuse in its responses.

Does optimizing for Bing matter if I only focus on Google?

Yes. Most major AI search platforms — including ChatGPT, Claude, and Perplexity — rely on Bing for their live search capabilities. If you're not optimized for Bing, you may be invisible to AI responses on those platforms.

How can I make my content visible to AI models?

Use structured data (like schema.org), demonstrate strong E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness), answer real user questions clearly, keep content fresh, and publish in multiple formats (text, video, images).

Why isn't my site showing up in AI responses, even if I rank #1 in Google?

AI doesn’t just look at rankings — it uses trust signals, formatting, clarity, and historical authority patterns. Your content might not be structured well enough or considered trustworthy or specific enough for AI to synthesize.

Can I tell if my site is being used in AI-generated answers?

Partially. You can track referral traffic from platforms like chat.openai.com or bing.com/chat, run sample prompts in AI tools, and monitor mentions. Adding “How did you find us?” with an "AI/chatbot" option in lead forms also helps.

Does having an FAQ section improve visibility in AI search?

Yes. AI models prefer clear Q&A formats. FAQ sections — especially when marked up with FAQ schema — are easily extracted and often reused directly in AI answers.

How can I adapt my content strategy for AI-first search?

Write in a conversational tone, directly answering real questions
Use topic clusters and entity-based site architecture
Add structured data (schema.org) for clarity
Include images, tables, and videos where relevant
Track AI-specific metrics like citation rate and visibility in AI tools

Do I need new team roles for AI Search Optimization?

Possibly. Many companies are creating roles like AI Content Strategist or AI Search Analyst. PR, content, and SEO are converging — cross-functional collaboration is now essential to being cited by AI systems.

Sum up: Your Strategic Roadmap

Navigating AI-driven SEO demands comprehensive adaptation, prioritising conversational and authoritative content alongside technical optimisation. The shift from traditional SEO to AI Search Optimisation represents both a challenge and an opportunity for marketers. Here’s a prioritised action plan to thrive in this new era:

Ensure Your Content Can Be Found, Understood, and Trusted by AI: Implement comprehensive schema markup so AI engines can readily parse your content structure and meaning. Simultaneously, audit your content for quality and credibility — update thin content, add expert insights and citations, and highlight author expertise. Position your site as a go-to authority that an AI would confidently cite.
Monitor and Measure New Performance Indicators: Expand your definition of success beyond organic traffic. Set up mechanisms to track when and how your content appears in AI-generated responses. Regularly sample important queries across all AI search platforms and note if your brand is mentioned or your content is used. Track referral traffic from these platforms and socialise metrics like “AI citation count” to complement traditional SEO measurements.
Optimise for Conversation, Not Just Clicks: Train content generators to write with conversational queries in mind, anticipating follow-up questions and answering them within content. Use headings that are questions or contain likely conversational phrases. Write as if answering users directly, incorporating natural language variations of queries in your content to align with how people talk and ask questions.
Strengthen Your Cross-Platform Presence: The future search ecosystem is diverse. Audit where your audience seeks information — from traditional search engines to YouTube to forums like Reddit — and bolster your presence accordingly. Consider producing different content formats (videos, podcasts, infographics) to ensure your brand appears across the sources AI systems draw from when answering queries. AI models particularly value structured authority from platforms like Reddit, Quora, and Medium — these serve as validation signals for relevance and authority. This lightweight PR strategy creates multiple touchpoints that AI systems can use to verify your expertise.
Integrate SEO, Content, and PR Efforts: SEO can no longer operate in a silo. Your PR efforts (getting mentioned in news, securing expert quotes in publications) directly feed SEO now, because those mentions boost your authority and likelihood of being referenced by AI. Likewise, content teams should coordinate with SEO to create pieces that serve both the website and off-site AI answers. Establish workflows that produce comprehensive content strategies for key topics, ensuring your information appears across the sources AI systems consult.
Stay Agile and Continuously Learn: The AI search landscape is evolving rapidly. Encourage experimentation and iteration, piloting new techniques and testing how content performs across different AI platforms. Keep an eye on platform announcements and be ready to adapt your strategy. Invest in training your team on AI technologies — even simple practices like using AI systems to simulate how they would answer queries about your content can provide valuable insights.

In conclusion, the fundamentals of understanding your audience and providing value remain unchanged, but the methods of discovery and delivery are fundamentally being transformed. SEO professionals and marketers must broaden their scope to AI Search Optimisation, ensuring their content is not only visible to algorithms looking for keywords, but valuable in a synthesised answer context. Those who do will find that they can maintain or even expand their reach in the age of intelligent search assistants. Brand awareness will be increasingly created in conversations that reference your content even if not resulting in any immediate clicks. AI conversations are the new internet and search combined.