For almost two decades, digital marketers could rely on a familiar playbook. Traditional SEO was predictable. Google dominated, keywords ruled, and ranking in the top five organic results meant traffic and leads.
That world is disappearing.
The rise of generative AI—tools like ChatGPT, Gemini, Claude, Perplexity, and Microsoft Copilot—is transforming how information is discovered. People no longer search with a few keywords; they ask full questions and expect complete answers. These answers often appear directly on the platform—without a click to your website.
Welcome to the era of AI SEO, also known as Answer Engine Optimization (AEO) or Generative Engine Optimization (GEO). This shift challenges everything B2B and technology marketers know about visibility, measurement, and strategy.
This article explains how AI-driven search differs from traditional SEO, why it matters for B2B brands, and what marketers can do to adapt.
1. From Traditional SEO to AI-Driven Search
The Predictable Era of Traditional SEO
Traditional SEO was based on a relatively stable set of mechanics:
- Keyword search volumes indicated demand.
- Click-through rates followed clear patterns—the top three links captured most of the traffic.
- Analytics tools could measure impressions, clicks, and conversions.
- Performance could be tied back to content optimizations and backlinks.
Marketers could plan campaigns with confidence.
The New Reality of AI SEO
AI SEO turns that predictability on its head.
Search is no longer a single index with ranked pages. Instead, it’s a network of generative systems, each powered by different models and data sources. A single query might trigger a multi-step reasoning process across several engines.
For example:
- Google’s AI Overviews summarise content from across the web into conversational snippets.
- ChatGPT uses retrieval-augmented generation (RAG)—combining its training data with real-time search results.
- Perplexity breaks questions into micro-queries and synthesises citations.
Each platform behaves differently. There’s no single “ranking factor” or universal way to optimise.
For B2B marketers, this creates a major challenge: what exactly are we optimising for?
2. The Disappearance of Keywords and Search Volumes
In traditional SEO, keyword research was the backbone of every campaign. Marketers could look up monthly search volumes, estimate potential traffic, and prioritise topics accordingly.
In AI-driven search, that concept no longer applies.
People don’t type “best CRM software Singapore.” They ask:
“What’s the best CRM for a mid-size SaaS company with 50 employees in Singapore?”
Each question is unique. Search demand is fragmented and long-tail—often never repeated exactly the same way. That means:
- Keyword tools lose accuracy and relevance.
- Estimating search demand becomes nearly impossible.
- Optimization must shift from targeting fixed phrases to building authority across topics.
The goal is not to rank for a keyword, but to become the trusted source the AI cites or references when constructing its answer.
3. Defining Success: From Rankings to Visibility
Traditional SEO success was measured in rankings, clicks, and traffic.
AI SEO changes the definition of success to visibility within AI-generated responses.
When an answer engine cites or mentions your brand, even without a click, that counts as influence. This is sometimes called “impression-less visibility.”
However, tracking this visibility is complex:
- AI responses are probabilistic—the same query can yield different answers.
- Logged-in users see personalised results based on their history and preferences.
- Responses can change daily as the underlying model updates or retrains.
This makes measurement messy—but not impossible. A new class of tools is emerging to track AI visibility, brand mentions, and citation frequency across major AI systems.
4. How AI Search Engines Work
Understanding how large language models (LLMs) retrieve and assemble answers helps marketers know where to focus.
Model Training vs. Contextual Retrieval
LLMs generate answers in two ways:
- Training Data — Information they learned during model training (which can be months old).
- Retrieved Data — Live information fetched from indexed sources, often via APIs or web crawlers.
Google’s AI has an advantage here because it can combine both: its massive index provides up-to-date web data, while the model adds reasoning and context.
Other platforms—like ChatGPT or Claude—use retrieval-augmented generation, pulling from Bing or curated datasets. The variability means the same query can draw from entirely different sources depending on the model.
The Query Fan-Out Effect
AI search engines often expand a single user question into multiple micro-queries to generate a complete and contextually rich answer. This process is known as query fan-out.
For example, when a B2B buyer asks:
“What are the best cybersecurity platforms for financial institutions in Southeast Asia?”
The AI might deconstruct that into several smaller, intent-specific queries such as:
- “Top cybersecurity vendors for finance”
- “Regulatory compliance standards in Southeast Asia”
- “Cloud security frameworks for banks”
- “Customer reviews of cybersecurity software”
Each of these sub-queries pulls data from different indexed sources, which are then synthesised into a unified answer.
If your brand, product, or thought-leadership content appears in any of the sources connected to those micro-queries, your chances of being referenced or cited in the final AI-generated response increase significantly.
This makes broad, consistent brand visibility across authoritative, third-party sources—such as industry journals, review platforms, and technical blogs—critical for success in AI SEO.
5. A Practical Roadmap for B2B and Technology Marketers
Step 1: Keep Traditional SEO Fundamentals
- Maintain strong on-page and technical SEO: schema, metadata, mobile optimisation, page speed.
- Continue building backlinks and authority sites. Google’s AI Overviews still rely heavily on top organic results.
- Ensure brand descriptions, product names, and facts are consistent across all platforms.
Step 2: Redefine Metrics — From Clicks to Mentions
- Move beyond keyword rankings.
- Track how often your brand appears or is cited in AI-generated responses.
- Treat brand visibility as a measurable goal—similar to share of voice.
Step 3: Track AI Referrals
- Use analytics tools to identify referral traffic from ChatGPT, Perplexity, Gemini, and others.
- Even small traffic volumes can indicate meaningful exposure in answer engines.
Step 4: Use AI Visibility Trackers
- SEO platforms such as Ahrefs, Semrush, and SE Ranking, as well as dedicated AI visibility trackers such as Hall, are beginning to measure brand mentions, citations, and visibility across generative search engines.
- Select 40–50 priority questions that align with your ICPs and benchmark performance across these models and tools for a start.
Step 5: Define Target Market Segments (TMS)
- Collaborate with sales and product teams to clearly identify ideal customer profiles (ICPs).
- Use AI tools to mine your own content (webpages, product sheets, case studies) and infer themes, pain points, and messaging clusters.
Step 6: Build Topic and Question Maps
- Generate a comprehensive list of unbranded questions relevant to your ICPs—what they actually ask during research phases.
- Focus on middle- and bottom-of-funnel queries that relate to problems, use cases, and implementation.
- Validate the list with customer-facing teams.
Step 7: Audit Mentions and Citations
- Use AI visibility tools or manual searches to see where and how your brand is being referenced.
- Analyse both:
- Mentions: Brand name or product mentioned in an answer.
- Citations: Directly linked or attributed to your content.
- Mentions: Brand name or product mentioned in an answer.
- Mentions often stem from PR and community discussions. Citations come from content depth and authority.
Step 8: Strengthen Both Mentions and Citations
- Boost mentions through:
- Digital PR campaigns
- Executive thought leadership
- Industry collaborations and podcast appearances
- Community engagement on LinkedIn and Reddit-style platforms
- Digital PR campaigns
- Boost citations through:
- High-quality, information-rich content (whitepapers, explainer articles, benchmark reports)
- Publishing on third-party media and association sites
- Consistent fact-based content that AI can reference as a source of truth
- High-quality, information-rich content (whitepapers, explainer articles, benchmark reports)
Step 9: Scale Content with AI-Assisted Production
- Use AI for ideation, outlines, first drafts, and repurposing.
- Combine it with human review and subject-matter expertise.
- Build an internal editorial framework that maintains factual accuracy and tone while leveraging AI’s speed.
Step 10: Integrate for Holistic Visibility
AI SEO success depends on integration:
- Content creation feeds the corpus.
- PR and social visibility amplify mentions.
- Technical SEO ensures discoverability.
- Analytics and monitoring measure visibility.
Together, these form a feedback loop that strengthens your presence across both traditional and AI-driven search ecosystems.
6. Key Metrics and Tools Emerging in AI SEO
When measuring success in AI SEO (AEO, GEO), traditional rank-based metrics no longer suffice. Marketers should monitor the following five key indicators:
- AI Visibility Score
- What it measures: How frequently your brand or content appears in AI-generated answers.
- Why it matters: Replaces traditional “rank” metrics — visibility in answers is now the new position #1.
- What it measures: How frequently your brand or content appears in AI-generated answers.
- Citation Rate
- What it measures: The number of times AI tools directly link to or reference your content.
- Why it matters: Indicates authority and trustworthiness; higher citation rates mean the AI treats your content as reliable.
- What it measures: The number of times AI tools directly link to or reference your content.
- Answer Share of Voice (ASoV)
- What it measures: The percentage of AI responses that mention your brand versus competitors.
- Why it matters: A modern equivalent of share of voice — shows brand prominence within AI-generated results.
- What it measures: The percentage of AI responses that mention your brand versus competitors.
- AI Referral Traffic
- What it measures: The volume of visitors arriving from AI interfaces like ChatGPT, Perplexity, or Gemini.
- Why it matters: Reflects tangible engagement resulting from visibility within answer engines.
- What it measures: The volume of visitors arriving from AI interfaces like ChatGPT, Perplexity, or Gemini.
- Entity Consistency
- What it measures: How consistently your brand, products, and people are represented across digital platforms.
- Why it matters: Ensures AI engines correctly identify and attribute your content; inconsistent data reduces inclusion in answers.
- What it measures: How consistently your brand, products, and people are represented across digital platforms.
7. What the Data Shows
Recent studies highlight the growing adoption and impact of AI-driven search:
- 56% of marketers already use generative AI in SEO workflows (DemandSage, 2025).
- AI SEO saw a 45% boost in organic traffic and a 38% rise in eCommerce conversions. (DemandSage, 2025).
- The AI SEO tools market is projected to quadruple—from US $1.2 billion (2024) to US $4.5 billion (2033).
- A 2025 Guardian report found that AI search summaries cause major drops in organic news traffic, proving how powerful AI overviews can be.
- Google itself confirmed that “AI Overviews” reduce click-throughs for informational queries but boost engagement for complex, product-driven ones.
The implications are clear: organic traffic patterns are shifting permanently.
8. Common Pitfalls to Avoid
- Over-optimising for old metrics — rankings and clicks alone no longer define performance.
- Ignoring brand signals — inconsistent facts and descriptions across platforms confuse AI engines.
- Producing low-quality AI-generated content — volume without authority erodes credibility.
- Focusing only on owned channels — visibility in third-party sources matters as much as your own site.
- Failing to measure AI visibility — without tracking, you can’t manage or improve.
9. The Future of B2B Search Visibility
Over the next 12–18 months, several trends are likely:
- Answer engines as gateways — tools like Perplexity and Copilot will integrate directly into enterprise software, influencing B2B buying research.
- AI-native analytics platforms will emerge to measure brand presence across AI responses.
- Structured data and entity optimization will become critical. AI relies on clear, machine-readable facts.
- Voice and multimodal search (text, image, charts) will expand the notion of discoverability.
B2B marketers who align early—treating AI SEO not as an experiment but as a strategic pillar—will be better positioned to maintain visibility as organic search continues to fragment.
Conclusion
AI SEO, AEO, and GEO mark the next evolution of digital visibility. For B2B and technology marketers, the challenge is not simply learning a new tool—it’s rethinking how audiences find, trust, and engage with brands in a world where answers are generated, not just linked.
The new goal isn’t just to rank; it’s to be referenced. Not just to attract clicks, but to earn inclusion in the answers your customers see first.
Marketers who understand this—and adapt their content, PR, and analytics accordingly—will define the next generation of digital leadership.
About the Author
Donald Chan is the Founder of IMPACT! Brand Communications, a digital and content marketing agency headquartered in Singapore. With over 12 years of agency leadership and 20 years of marketing experience, he helps brands in B2B, technology, and emerging industries achieve real marketing impact.