Thomas Peham, CEO and Co-Founder of OtterlyAI, recently joined the AI Insiders podcast to unpack how generative AI is rewriting the rules of search, SEO, and brand discovery. His message was clear: If your brand isn’t showing up in AI-generated responses, you’re missing out on your future customers.
Watch the full episode:
In this session, Thomas shared original research from OtterlyAI’s AI search monitoring platform, revealing how large language models like ChatGPT evaluate, reference, and rank content—and why the old SEO playbook isn’t cutting it anymore.
What you’ll learn from this episode:
- Practical steps to make your brand show up in AI-generated results
- Why traditional SEO strategies don’t translate to AI search
- What “AI search visibility” really means—and how to measure it
- Why Reddit and community forums dominate AI citations
- How to structure content for LLMs like ChatGPT and Perplexity
The AI Search Reality
Google’s traditional search dominance faces disruption as AI search systems gain traction among business users. These tools now influence significant purchasing decisions, particularly in specialized B2B sectors. For companies selling enterprise solutions, visibility in AI search results has become crucial for reaching decision-makers during their research process.
“It is important because a lot of senior executives in the B2B space are using AI tools to actually select the product. Their purchasing decisions depend on AI systems. So you have to make sure you’re represented there in the best possible way.”
This trend extends beyond casual research. IT directors now rely on AI tools like ChatGPT when evaluating enterprise products with six-figure price tags. The shift creates both challenges and opportunities for marketers who must adapt their strategies to maintain visibility in this new environment while compensating for declining organic traffic.
Identifying Relevant AI Prompts
Successful AI search optimization begins with understanding what prompts potential customers use. Unlike traditional keyword research, prompt research requires different approaches and sources. Search Console data provides a starting point by identifying longer search queries (five or more words) that often resemble conversational prompts.
OtterlyAI suggests classifying prompts into three categories:
- Informational — “What is zero trust architecture?”
- Comparative — “Best alternatives to Veeam for MSPs”
- Evaluative — “Which backup software is best for HPC environments?”
Each category maps to a different stage of the customer journey—and calls for a different content approach.
In one example, a sales lead shared the actual prompt a buyer used with ChatGPT to compare backup vendors. It wasn’t a search query—it was a paragraph-long evaluation request with specific requirements and attachments.
Direct customer interactions offer invaluable insights. In one example, a sales executive shared a customer’s actual ChatGPT prompt used to evaluate backup software solutions. This real-world example revealed how prospects compare product features against requirements by uploading documentation and white papers directly to ChatGPT.
Effective prompt research categorizes queries into three distinct types: informational prompts that explain topics, comparative prompts that evaluate competing solutions, and evaluation prompts used during the decision-making phase. This framework helps prioritize optimization efforts based on the customer journey rather than traditional search volume metrics.
Structuring Content for LLMs: Clarity Over Cleverness
AI systems like ChatGPT break your content into tokenized “chunks” to process it. That means:
- Each chunk should cover a single, clear idea
- Optimal length: 100–300 tokens
- Use structured headings (H2/H3) and bullet points
- Add quotes and external sources to signal authority
Well-structured, high-trust content is more likely to be cited or synthesized into AI-generated responses. Vague marketing copy? Not so much.
OtterlyAI’s data shows that AI engines prioritize clarity, topical authority, and semantic relevance over keyword density or backlinks.
Measuring AI Search Success
Traditional traffic metrics become less relevant in AI search optimization. Instead, brand mentions within AI responses serve as the primary success indicator. The goal shifts from driving clicks to ensuring your brand appears consistently in responses to relevant prompts, ideally among the top three mentioned solutions.
This creates attribution challenges since AI responses often lack citations or clickable links. Even when citations exist, users rarely click through. Success therefore manifests as increased brand awareness and direct traffic rather than measurable referrals from AI platforms.
Regional and language variations add complexity to measurement. Tracking performance across different markets requires monitoring prompt results in various languages and locations, as AI responses can vary significantly based on these factors.
Leveraging Reddit for Visibility
Reddit emerges as a valuable supplementary channel for AI search visibility. The platform frequently appears in both traditional search results and AI response citations. Creating authentic discussions about industry topics can generate valuable content that AI systems reference.
Effective Reddit strategy involves initiating genuine discussions related to customer problems. Rather than directly promoting products, marketers can pose questions about technical challenges their solutions address. This approach requires transparency about company affiliations while providing genuinely helpful information.
Success on Reddit demands ongoing engagement rather than posting and abandoning threads. The platform’s algorithms favor discussions that gain traction quickly after posting, making it essential to monitor and participate in conversations during the critical first minutes after publication.
Practical Implementation Steps
Related: The 6 steps to get your website cited by AI Search
Begin by ensuring AI crawlers can access your content by checking your robots.txt file. Next, audit existing content to identify gaps in coverage for important prompts. Break content into appropriate chunks, use clear headings, and incorporate authoritative sources to increase the likelihood of being cited in AI responses.
Tools like the GEO Content Check help optimize content structure by measuring text in tokens rather than words. Semantic similarity tools compare content against target prompts, identifying sections needing improvement. These technical approaches complement fundamental content quality principles to maximize AI visibility.
Collaboration between sales, support, and marketing teams proves essential for gathering real customer prompts and understanding how prospects use AI tools during their buying journey. This cross-functional approach ensures optimization efforts align with actual customer behavior rather than assumptions.
Adapting to the Future
The shift toward AI search represents both challenge and opportunity for B2B marketers. While traditional organic traffic may decline, companies that successfully optimize for AI systems can maintain visibility with decision-makers. Success requires understanding how these systems work, creating appropriately structured content, and measuring results through brand mentions rather than direct traffic.
Want to know how visible your brand is in AI search?
Try OtterlyAI’s free visibility scan: https://otterly.ai