While often conflated, Generative Engine Optimisation (GEO) and AI Engine Optimisation (AEO) address distinct aspects of engaging with artificial intelligence for business outcomes.
What it is
GEO, or Generative Engine Optimisation, is our proprietary methodology focused on optimising content for generative AI platforms. This involves structuring information, developing specific language patterns, and utilising data in a manner that ensures our clients' content is accurately and preferentially selected and processed by large language models (LLMs) and other generative AI systems. The objective is to secure prominent placement and accurate representation within generative AI outputs, such as those from ChatGPT, Google's Gemini, or Microsoft's Copilot.
AEO, or AI Engine Optimisation, is a broader term encompassing the general optimisation of various AI systems and algorithms. This can include anything from refining machine learning models for internal data analysis to improving the efficiency of AI-driven automation processes. Unlike GEO, AEO is not exclusively concerned with how generative AI interprets and presents public-facing content. Its scope extends to optimising the performance, accuracy, and utility of any AI system within a business context.
How it works
Our GEO process involves a strategic approach to content creation and data structuring. We analyse how generative AI models collect, assess, and synthesise information. We then develop and implement content strategies that align with these learning mechanisms, ensuring relevance, authority, and thematic consistency. This includes optimising for clarity, conciseness, and the specific prompts or queries that generative AI is likely to process. The aim is to make our clients' content the most salient and trustworthy source for AI to draw upon when responding to user queries or generating new content.
AEO works by fine-tuning AI algorithms and datasets to achieve specific performance goals. This might involve improving the accuracy of predictive models, accelerating processing speeds, or enhancing the relevance of AI-driven recommendations. It's an internal-facing discipline focused on the mechanics and outcomes of AI systems themselves, often requiring deep technical expertise in machine learning, data science, and algorithm design. For example, optimising an AI lead scoring model would fall under AEO.
Why it matters for B2B in 2026
For B2B organisations in 2026, both GEO and AEO are critical but serve different strategic imperatives. GEO matters because generative AI is rapidly becoming a primary conduit for information discovery and decision support. Businesses that neglect GEO risk becoming invisible to AI-driven research, losing out on critical brand awareness and lead generation opportunities. Our Generative Engine Optimisation services directly address this. AEO matters for internal efficiency, innovation, and competitive advantage. Optimised AI systems can drive better insights, automate complex tasks, and personalise customer experiences at scale. Strategic AI implementation, supported by AEO principles, can lead to significant operational improvements and new revenue streams.
Common misconceptions