AI-led discovery is changing how organisations are found, interpreted and trusted. Search is no longer only a list of links. Increasingly, stakeholders encounter information through AI summaries, conversational search, large language models and zero-click discovery environments.
For communications teams, that creates a new measurement challenge. It is no longer enough to ask whether a brand ranks on Google or has secured media coverage. We also need to understand how the wider world online content shapes what AI systems present to users and whether those outputs are accurate, useful and credible.
That is why the launch of the AMEC GEO Principles is important to PRs, analysts, and tech vendors
The principles, launched at the AMEC Global Summit in Dublin alongside A Practitioner’s Guide to GEO Measurement, provide a much-needed framework for measuring generative engine optimisation, or GEO. In simple terms, GEO describes how organisations appear in AI-generated answers and discovery environments.
PR Agency One Managing Director James Crawford helped lead the initiative, along with 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 informed by contributions from practitioners, measurement specialists and AMEC’s Academic Advisory Group, bringing together applied agency experience with academic expertise in communications measurement, evaluation and public relations research.
The aim is straightforward: to bring more discipline, transparency and accountability to a fast-moving area of communications measurement.
Why GEO measurement needs standards
The communications industry has moved quickly from asking what GEO is to asking how it should be measured. That speed has created useful innovation, but also risk.
Some measurement approaches rely too heavily on single visibility scores, limited prompt sets or opaque dashboards. Others treat AI-generated answers as fixed evidence, when in reality outputs can vary by tool, prompt, market, language, location, timing and user context.
The AMEC GEO Principles are designed to stop the industry repeating old measurement mistakes in a new environment. Avoid AI visibility vanity metrics. It should, instead, be connected to communications objectives, stakeholder information needs and meaningful outcomes.
That means applying the same discipline that underpins the AMEC Barcelona Principles and the AMEC Integrated Evaluation Framework: clear objectives, relevant evidence, transparent methodology and a separation between outputs, out-takes, outcomes and impact.
The seven AMEC GEO Principles
The AMEC GEO Principles set out the standards that should guide responsible measurement of AI-led discovery:
- AI-led discovery should be measured against communication objectives and stakeholder information needs.
- GEO measurement must assess the upstream brand reputation information environment before interpreting AI-generated answers.
- Search and content readiness should be evaluated as evidence of whether reliable information can be found, understood and cited.
- Observed AI outputs are directional indicators and should be tested transparently across tools, prompts, markets, languages and time.
- GEO measurement should distinguish visibility from outcomes and connect AI discovery to awareness, trust, behaviour and impact.
- Reliable, trustworthy and current sources matter more than volume, promotion or short-term visibility.
- Ethical GEO improves the public information environment and should not manipulate, disguise or flood it.
The three evidence areas that count
A central idea in the principles and Practitioner’s Guide is that GEO measurement should be triangulated across three connected evidence areas.
The first is upstream reputation. This includes the earned, shared and owned signals that shape how an organisation is understood: media coverage, expert commentary, reviews, stakeholder discussion, public records, corporate content, social media and other credible sources.
The second is search and content readiness. This looks at whether reliable information is discoverable, structured, current and accessible. For organisations, that includes the quality of owned content, website structure, search visibility, authority signals, clear headings, question-led content and credible inbound links.
The third is downstream AI outputs. This is what users may actually see in AI-generated answers: whether the brand appears, how prominently it appears, how it is framed, which sources are cited, whether core messages are accurate and whether there are omissions or reputational risks.
Taken together, these areas give a more complete view of AI-led discovery. Taken in isolation, they can mislead.
Why PR has a central role in AI visibility
The rise of AI-led discovery makes PR more important than ever.
Large language models and AI search systems draw on the public information environment. That environment is shaped heavily by earned media, shared media and structured owned content – all areas where communications teams have influence.
Strong media coverage, credible third-party commentary, useful expert content, accurate corporate information and visible public reputation signals all affect how organisations are understood online. In turn, they may influence how AI systems retrieve, summarise and present information.
This is why PR Agency One has been investing in AI visibility, OneEval and measurement-led communications. Our view is that AI discovery should be assessed as part of a broader communications effectiveness model, rather than treated as a standalone technical exercise.
Visibility alone is only one part of picture. The real question is whether stakeholders encounter information that is accurate, useful, current, credible and trustworthy – and whether that contributes to awareness, reputation, trust, behaviour and commercial impact.
What communications teams should do next
The AMEC GEO Principles give communications teams a practical starting point.
First, define the stakeholder questions that matter. GEO measurement should begin with real information needs.
Second, audit the upstream information environment. Look at what credible sources say about the organisation, where gaps exist and whether reputation signals are strong enough to support accurate AI outputs.
Third, assess search and content readiness. Reliable information needs to be findable, structured and current if it is to be understood and cited.
Fourth, monitor downstream AI outputs responsibly. Test across tools, prompts, markets, languages and time. Save outputs as evidence. Treat findings as directional and do not take the findings as gospel!
Finally, connect AI visibility to outcomes. That means linking discovery to awareness, trust, behaviour, web analytics, conversion data and wider organisational goals where evidence allows.
The launch of the AMEC GEO Principles is an important step for the industry. It gives communicators a common language and a more credible way to approach AI-led discovery.
In a market where the temptation is to sell certainty, the principles make a more useful argument: measurement is strongest when it is transparent, triangulated and honest about its limits.
Download the AMEC Practitioner’s Guide to GEO Measurement
AMEC Practitioner’s Guide to GEO MeasurementDownload AMEC GEO Principles
AMEC GEO Principles
