AMEC Launches GEO Principles For AI-Era Measurement
Developed in response to the rapid rise of AI-generated summaries, conversational search, large language models and zero-click discovery, the initiative reflects growing concern over how organisations, brands and reputations are being interpreted online.
The principles and guide were shaped through AMEC Agency Group collaboration, consultation and academic scrutiny, AMEC board review, vendor and practitioner feedback, and iterative testing and refinement over more than six months.
Primary contributors to the project included 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. The work was also informed by practitioners, measurement specialists and AMEC's Academic Advisory Group, bringing together agency experience with academic expertise in communications measurement, evaluation and public relations research.
Launching today at the AMEC Global Summit in Dublin, the resources will be introduced during a panel chaired by Rayna Grudova-de Lange, founder and CEO of InsightHQ.
GEO, or Generative Engine Optimisation, is increasingly being used to describe how organisations appear in AI-generated answers and discovery environments. AMEC's new principles are designed to help practitioners assess this area responsibly and ethically, without reducing measurement to simplistic rankings, vanity metrics or opaque scores from individual tools.
Building on AMEC's wider legacy in communication measurement and evaluation – including the Barcelona Principles, the Integrated Evaluation Framework (IEF) and the Data Quality Initiative – the GEO Principles encourage practitioners to connect AI discovery measurement to communication objectives, reputation, trust and organisational outcomes.
At the centre of the framework are three connected areas of AI-led discovery measurement:
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Upstream reputation – the earned, shared and owned signals that shape how an organisation is understood, including media coverage, third-party commentary, reviews, expert content and public reputation signals
Search and content readiness – the extent to which an organisation's digital presence is structured, credible, accessible and interpretable by search engines and AI systems
Downstream AI outputs – how an organisation appears in AI-generated answers, including accuracy, prominence, framing, citations, omissions and potential reputational risk.
Alongside the framework, AMEC is also introducing baseline evidence requirements for GEO measurement, including repeatable prompts, documented methods, transparent assumptions and clear limitations. The principles 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 to measure AI discovery or organisational representation.
A separate practitioner guide is intended to help communication teams apply the principles in day-to-day measurement programmes, including how to interpret evidence from multiple sources, report findings responsibly and avoid false precision when working with AI-generated outputs.
Rather than treating AI outputs as a standalone data source, the guide encourages practitioners to triangulate evidence across reputation signals, search and content readiness, and downstream AI-generated outputs to build a more complete understanding of organisational visibility and representation.
Communications teams are meanwhile facing growing pressure to understand how brands, organisations and issues are being interpreted by AI systems. While AI-led discovery introduces new measurement challenges, AMEC argues that practitioners should continue to apply established evaluation disciplines: clear objectives, relevant evidence, transparent methodology and a connection to meaningful outcomes.
Ethical considerations are also built into the framework, with the principles calling on practitioners to consider bias, transparency, data provenance, user intent, privacy, accuracy and the risk of overclaiming. They also caution against treating AI-generated answers as a fixed or universal view of reputation, given outputs can vary by model, prompt, location, language, timing and user context.
James Crawford, managing director of PR Agency One and AMEC board director (pictured), 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 some 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 help bring discipline to that conversation.
“These principles were created because the industry needs 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. We need to understand the reputation signals that feed the information environment, whether organisations are technically and editorially discoverable, and what AI systems then present to users.
“This has taken more than six months of detailed discussion, research, scrutiny and refinement. It has been a genuinely collaborative piece of work, shaped by agency practitioners, analysts, vendors, academics and AMEC colleagues. The aim is to give the industry a common starting point, not to pretend that all the answers are settled.”
Johna Burke, CEO and global managing director of AMEC, added:“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.
“At a time when technology is evolving at extraordinary speed, professional associations play a critical role in helping industries come together to challenge assumptions, reduce bias, strengthen methodologies and encourage more responsible and credible approaches to measurement.
“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.”
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