In Episode 39 of Beyond the CMS, host Chris Bryce, Partner - Strategy at Dotfusion, sits down with Joel Goodman, VP - Growth Strategy at Squiz, to unpack what it actually takes to make enterprise content discoverable in AI-powered search engines and answer platforms.

Joel brings over 13 years of experience running a digital agency serving higher education before joining Squiz, where he now helps organizations modernize their content platforms and optimize for both human and AI audiences. He explains why traditional SEO practices fall short when ChatGPT, Perplexity, and Google AI Overviews are reshaping how people search, and what organizations need to do differently to stay visible.

The discussion goes beyond surface-level advice and explores the structural, strategic, and technical realities of Answer Engine Optimization. Joel explains how large language models interpret content differently than traditional search bots, why speed and structure matter more than ever, and how content contradictions across a website can kill discoverability entirely.

Key topics include:
• How AI search differs from traditional SEO and why LLMs parse content in fundamentally different ways
• What Answer Engine Optimization means in practice and why it starts with content strategy, not tactics
• Why Squiz built conversational search on top of 15+ years of proven search algorithms instead of bolting AI onto generic tools
• How content intelligence tools surface contradictions, readability issues, and structural problems that block AI visibility
• The role of schema markup and structured data—and how to implement it at scale without manual tagging
• Why web performance directly affects AI discoverability and what a 499 error code means for your content
• How to balance brand experience with the highly structured, parsable content that AI engines require
• Why accessibility, readability, and content clarity benefit both human users and AI simultaneously
• The tension between beautiful UX design and content built for machine interpretation—and where both still matter
• How Squiz's hybrid CMS approach combines traditional page-building tools with full headless API flexibility
• The integration of conversational search, personalization, A/B testing, and customer data platforms within a single DXP
• Real use cases across higher education, professional services, legal firms, government, and content-heavy enterprises
• Why composable architectures can create unpredictable costs and operational overhead—and when a unified DXP makes more sense
• What content contradictions are, how they emerge across large sites, and why they destroy AI trust and rankings
• Why slow websites get skipped by LLMs and how the 499 error code signals abandonment by AI crawlers
• The Guardian Agent model Squiz uses to validate AI-generated responses before delivering them to users
• Why conversational search on your own site matters as users expect the same AI-native interactions they experience elsewhere
• How Squiz's Funnelback search platform works standalone or integrated—and why domain-specific search beats generic web-scale algorithms

Joel explains that making content AI-ready is not about chasing a new trend or gaming a new algorithm. It's about returning to foundational content strategy principles: clarity, structure, consistency, accessibility, and performance. The same work that improves human comprehension and meets accessibility standards also makes content legible to AI systems.

Whether you're a CMO trying to understand why your brand doesn't appear in ChatGPT results, a digital leader evaluating CMS and DXP platforms, a content strategist rethinking taxonomy and information architecture, or an enterprise operator preparing for the shift toward AI-native discovery, this episode delivers practical insight into what works and what doesn't.

🎧 Listen now and join the conversation!