How To Train a Team to Optimize for AI Search

Louisamay Hanrahan
Jun 17
3 min read

Updated: Jun 18

How We Trained a Team to Think Like an AI Search Engine, A case study on training a team for AI-driven discovery (GEO)

Executive Summary

As generative AI becomes a dominant force in how people access information, content strategies must adapt. Traditional SEO approaches—focused on keyword density and link-building—are no longer sufficient for visibility in AI-powered environments like ChatGPT, Perplexity, or Google’s AI Overviews.

At Callm Intelligence, we recently led a client-facing training session focused on transforming their content creation process to align with AI search behaviours. What began as a routine workshop quickly revealed a fundamental gap in how teams are conceptualizing SEO in the era of AI. This article outlines what we learned, how we reframed the team’s thinking, and the tangible outcomes that followed.

Background: The Challenge with Traditional SEO Mindsets

Callm Intelligence works with companies seeking to integrate AI into critical workflows. One client tasked us with improving the organic reach of their blog content.

During a training session with their content team, we ran a practical exercise: outline a blog article based on a common question likely to be asked in tools like ChatGPT. For illustrative purposes, we used the question, “What is AI Search?”

Immediately, the responses were framed around keyword strategy:

“We need to include key terms like ‘Keyword,’ ‘Keyword,’ ‘Keyword,” - they wen't into other SEO techniques as well.

We paused the exercise there.

This was the pivotal moment: we realized that the team wasn’t thinking in terms of answers. They were still approaching creating content like traditional SEO.

AI Search Optimization - The Reframing: Start Answering Questions

We introduced a new approach: answer first. Instead of starting with structure or keywords, we began with the core question itself.

What is AI Search?

The team responded:

“AI search is the ability for generative AI tools—such as ChatGPT or Perplexity—to retrieve and synthesize information across sources and return a direct, human-like answer to a user’s query.”

That became the lead paragraph.

We repeated the exercise across multiple content titles, with a simple rule: begin by answering the question as clearly, accurately, and efficiently as possible. Only then should structure, subheadings, and supporting information be developed.

What emerged was not just better content—but content that AI search engines could use directly.

Why AI Search Requires a New Content Model

Generative AI search engines do not rank web pages based on backlinks or keyword matching alone. They summarize. These models parse the web, identify content chunks that most directly answer a user’s question, and return a synthesized summary—often citing the source.

In this context, the winning content isn’t necessarily the longest or most keyword-optimized—it’s the most useful. That means for optimal AI search optimization:

Clear, question-aligned headlines
Direct, well-written answers at the top of the page
Semantic richness and precision in language
Authoritative tone with original insights or experience

Methodology: Embedding AI Search Thinking

Our approach during the training included:

Live Q&A drills: We had the team respond to blog titles as literal questions.
Reverse outlining: Instead of starting with H1/H2 structure, we built the structure after a strong answer was developed.
Writing sprints focused on "answer-first" paragraphs.

Results: Early Evidence of Success

Within days of publishing the first few blog posts written using this method, GEO reported a sharp uptick in impressions and citations from AI tools.

This aligns with broader industry trends—LLMs favouring high-signal, low-fluff content that mirrors human communication: concise, relevant, and trustworthy.

Strategic Implications for Content Teams

Most marketing and content teams are still optimising for the Google of five years ago. But AI search is already reshaping how B2B buyers, technical stakeholders, and decision-makers discover and consume information. At Callm Intelligence, we advise clients to consider the following:

1. Rebuild your content workflows around questions.

Every title should be treated as a real user question. The job of the content is to provide the best possible answer—fast.

2. Structure for language models, not crawlers.

Use clear headings, semantic phrasing, and strong opening paragraphs. LLMs chunk content for meaning—not for metadata.

3. Invest in human-led insights.

LLMs reward factual thinking, and user experience storyes. Share experiences, frameworks, and real outcomes—like the one we’ve shared here.

Conclusion

AI search optimization is not coming. It’s here. The transition from keyword-first to answer-first thinking is nontrivial—but critical. Content teams that adapt early will find their thought leadership not just indexed, but amplified by the next generation of AI-powered search engines. At Callm Intelligence, we help businesses bridge this transition with practical training, AI strategy, and implementation support. If your team is ready to rethink how it shows up in the age of AI, we’d love to talk.