New global principles and practitioner guide set out a responsible way to measure how organisations are found, interpreted and represented in AI-generated answers
AMEC, the International Association for the Measurement and Evaluation of Communication, has launched the AMEC GEO Principles and a companion resource, A Practitioner’s Guide to GEO Measurement , to help communications professionals measure the growing influence of AI-led discovery, generative search and large language models.
The resources respond to a fast-changing information environment in which AI-generated summaries, conversational search and zero-click discovery are increasingly shaping how organisations, brands and issues are found, understood and trusted online.
GEO, or Generative Engine Optimisation, is increasingly used to describe how organisations appear in AI-generated answers and discovery environments. AMEC’s principles are designed to help practitioners assess this responsibly, without reducing measurement to simplistic rankings, vanity metrics or opaque scores from individual tools.
The principles were developed over more than six months through AMEC Agency Group collaboration, AMEC board review, academic scrutiny, vendor and practitioner feedback, and iterative testing. The work was led by primary contributors James Crawford of PR Agency One , Mary Elizabeth Germaine of Ketchum , Ben Levine of FleishmanHillard TRUE Global Intelligence , Matt Oakley of Hotwire Global , Amber Daugherty of Big Valley Marketing and Rob Key of Converseon , with input from AMEC’s Academic Advisory Group and wider AMEC members.
The resources were launched at the AMEC Global Summit in Dublin on 20 May, during a panel chaired by Rayna Grudova-de Lange, Founder and CEO of InsightHQ .
The AMEC GEO Principles set out a practical framework for measuring AI-led discovery across three connected areas: upstream reputation signals, including earned coverage, third-party commentary, reviews, expert content and owned assets; search and content readiness, including whether an organisation’s digital presence is credible, accessible and structured for interpretation by search engines and AI systems; and downstream AI outputs, including how an organisation appears in AI-generated answers, citations, framing, omissions and potential reputational risk.
The principles also introduce baseline evidence requirements, including repeatable prompts, documented methods, transparent assumptions and clear limitations. They reinforce that AI outputs should be treated as directional evidence rather than absolute truth, and caution against relying on any single score, platform or tool.
James Crawford, managing director of PR Agency One and AMEC Board Director, said:
“Anyone working in PR or communication will know how quickly clients and boards have started asking how GEO and LLM outputs should be measured. There is excellent innovation taking place, but there are also uneven standards, overclaiming, vanity metrics and methodologies that are not always transparent enough.
“AMEC has a responsibility to bring discipline to that conversation. These principles give the industry a more rigorous way of looking at AI-led discovery: one that recognises its importance, but also its limits. The most useful measurement will come from triangulating evidence: the reputation signals that feed the information environment, whether organisations are technically and editorially discoverable, and what AI systems then present to users.”
Johna Burke, CEO and Global Managing Director of AMEC, said:
“As AI increasingly shapes what people see, trust and act upon, the communication industry must hold itself to higher levels of transparency, evidence and accountability.
“The AMEC GEO Principles were built through global collaboration across agencies, practitioners, academics, technology leaders and AMEC’s international community because no single organisation, platform or perspective can fully define or measure AI-driven discovery alone.
“This initiative reflects the collective expertise, scrutiny and commitment of professionals across regions who understand that rigorous, transparent and ethical evaluation is essential to maintaining trust in the AI era.”