The low-quality output many associate with AI marketing is a symptom of skipping the research phase. Before generating any assets, spend significant time using tools like Perplexity to deeply understand the market, competitors, and customer pain points.

As AI agents increasingly evaluate products and manage buying workflows, Drosos and Truair suggest marketing teams treat agents as distinct personas on the B2B buying committee. Because agents will become more verticalized and specialized, companies must allocate specific budget and strategic focus toward understanding what algorithmic agents want, tailoring content to satisfy their evaluation criteria alongside human buyers.

Unlike traditional SEO, AI-generated answers are personalized based on a user's entire conversation history. Two people can get different results for the same prompt. Therefore, chasing keywords is a flawed strategy. Brands should instead focus on building a deep, structured, authoritative data foundation that the AI can interpret for any context.

Mark Drosos notes that AEO is mirroring the evolution of social media. Initially, marketers tried to game LLMs by churning out content just to win citations and mentions. However, as LLMs increasingly pull from diverse third-party sources like YouTube and LinkedIn, brands must shift from superficial content creation toward earning genuine participation by clearly solving customer problems and demonstrating authentic business value.

Traditional SEO focuses on a limited set of keywords. AEO requires tracking a vast number of specific questions (prompts) that different customer personas ask AI engines, reflecting their unique challenges and buyer journey stage. This is a fundamental shift in content strategy.

Unlike old search algorithms, new AI models function like humans, valuing clarity and consensus. Marketers must shift from rigid SEO structures to human-first content that's easily digestible and conversational to win in what HubSpot calls 'Answer Engine Optimization'.

The true power of tools like Manus isn't in its generic, suggested prompts, but in a skilled marketer's ability to ask specific, domain-aware questions. An expert can dig into details like channel-specific bounce rates to find competitive arbitrage, a level of inquiry the AI won't suggest on its own.

The rise of AI agents means website traffic will increasingly be non-human. B2B marketers must rethink their playbooks to optimize for how AI models interpret and surface their content, a practice emerging as "AI Engine Optimization" (AEO), as agents become the primary researchers.

Many companies fail with AI prospecting because their outputs are generic. The key to success isn't the AI tool but the quality of the data fed into it and relentless prompt iteration. It took the speakers six months—not six weeks—to outperform traditional methods, highlighting the need for patience and deep customization with sales team feedback.