Your energy buyers now open ChatGPT, Gemini and Google's AI Overviews before they open a browser tab, and they build vendor shortlists inside those answers. If your firm is not named, you are not in the running, and you will never see the loss in your analytics. This dossier sets out AI Share of Voice, the metric that measures whether an energy brand appears when an answer engine is asked who to trust, and a practical method to measure it.<\/p>\n
Share of Voice has always been a proxy for attention: your slice of the advertising, the search rankings or the press coverage in a category. AI Share of Voice applies the same idea to a new surface. It is the percentage of answers from AI engines, ChatGPT, Gemini, Perplexity, Microsoft Copilot and Google’s AI Overviews, across a defined set of buyer questions, in which your brand is mentioned, cited or recommended, measured against a named set of competitors.<\/p>\n
L'importance de cette question aujourd'hui tient au fait que les moteurs de recherche sont devenus le point de d\u00e9part, et non plus la destination finale. Forrester a indiqu\u00e9 que 89 % des acheteurs B2B avaient adopt\u00e9 l'IA g\u00e9n\u00e9rative comme principale source de recherche autonome, soit environ trois fois plus que les consommateurs, d'apr\u00e8s l'analyse de ses donn\u00e9es par [nom de l'entreprise ou de l'organisation]. Rapport sur la g\u00e9n\u00e9ration de la demande<\/a>. In 2026 G2 research found that 51 percent of B2B software buyers now begin their purchase research inside an AI chatbot rather than a search engine, with ChatGPT the dominant tool at 63 percent, as reported in G2’s communiqu\u00e9 de recherche<\/a>.<\/p>\n Pour les entreprises \u00e9nerg\u00e9tiques, il ne s'agit pas d'un changement abstrait. Lorsqu'un responsable des achats, un gestionnaire d'actifs ou un ing\u00e9nieur demande \u00e0 un assistant de recommander des fournisseurs fiables pour un projet, le mod\u00e8le g\u00e9n\u00e8re une liste restreinte. Si votre entreprise y figure, elle est prise en compte avant m\u00eame qu'un commercial n'ait entrepris la moindre d\u00e9marche. Si elle n'y figure pas, elle est \u00e9limin\u00e9e, et, contrairement \u00e0 un clic de recherche infructueux, vos analyses ne vous en informeront jamais. AI Share of Voice a pour vocation de rendre visible cette perte invisible, afin qu'elle puisse \u00eatre g\u00e9r\u00e9e plut\u00f4t que simplement estim\u00e9e.<\/p>\n Deux forces convergent. La premi\u00e8re concerne les comportements\u00a0: les acheteurs pr\u00e9f\u00e8rent effectuer leurs recherches seuls, et l\u2019IA g\u00e9n\u00e9rative est l\u2019outil de libre-service le plus efficace jamais mis \u00e0 leur disposition. Bien avant l\u2019existence des moteurs de r\u00e9ponse, les analystes avaient d\u00e9j\u00e0 constat\u00e9 que les acheteurs B2B passent la majeure partie de leur parcours d\u2019achat loin des \u00e9quipes commerciales\u00a0; l\u2019assistant ne fait qu\u2019acc\u00e9l\u00e9rer ce processus, en condensant la premi\u00e8re s\u00e9lection en une seule conversation. Une \u00e9tude Google, cit\u00e9e dans tout le secteur fin 2025, a r\u00e9v\u00e9l\u00e9 qu\u2019environ 60\u00a0% des acheteurs B2B utilisent d\u00e9sormais des outils comme ChatGPT ou Gemini pour enrichir leurs listes de fournisseurs, synth\u00e9tiser du contenu et identifier les concurrents.<\/p>\n The second force is the search page itself changing under buyers’ feet. Google’s AI Overviews now sit above the classic links on a large and contested share of queries. Trackers disagree on the exact figure because each samples a different keyword mix, but a defensible 2026 reading is that AI Overviews appear on roughly a fifth to a half of queries depending on the vertical, with BrightEdge data cited near 48 percent of tracked queries and Semrush measuring a rise from about 6.5 percent of queries in January 2025 to a peak near 25 percent before Google recalibrated. Treat any single number as an estimate; the trend is not in doubt.<\/p>\n Gartner a insist\u00e9 sur ce point en 2024 en pr\u00e9voyant que le volume des moteurs de recherche traditionnels chuterait d'environ 25 % d'ici 2026, les chatbots et les agents virtuels absorbant les requ\u00eates, et a conseill\u00e9 aux entreprises de\u2026 Mettre l'accent sur la production de contenu unique et utile qui d\u00e9montre expertise, exp\u00e9rience, autorit\u00e9 et fiabilit\u00e9.<\/a>. D'apr\u00e8s certaines analyses de 2026, la baisse des prix des titres n'a pas encore eu d'impact significatif. L'ampleur pr\u00e9cise importe moins que la direction\u00a0: l'attention se d\u00e9tourne des dix liens bleus pour se concentrer sur la r\u00e9ponse unique et synth\u00e9tis\u00e9e, et les sp\u00e9cialistes du marketing \u00e9nerg\u00e9tique qui ne mesurent que les premiers passent \u00e0 c\u00f4t\u00e9 d'une part croissante du march\u00e9.<\/p>\n La part de voix de l'IA ne se mesure pas \u00e0 un seul chiffre, mais \u00e0 un ensemble de valeurs, toutes calcul\u00e9es \u00e0 partir d'un m\u00eame ensemble de questions pr\u00e9d\u00e9finies afin de garantir la comparabilit\u00e9 des tendances d'une semaine \u00e0 l'autre. Les fournisseurs utilisent des formules diff\u00e9rentes\u00a0: HubSpot, Semrush et Profound publient chacun des m\u00e9thodes sensiblement distinctes. Il est donc essentiel d'en choisir une, de la documenter et de la maintenir. Trois indicateurs sont particuli\u00e8rement r\u00e9v\u00e9lateurs.<\/p>\n La m\u00e9thode est simple \u00e0 \u00e9noncer, mais exigeante \u00e0 mettre en \u0153uvre. Premi\u00e8rement, \u00e9laborez l'outil\u00a0: rassemblez 50 \u00e0 200 questions que se posent r\u00e9ellement les acheteurs de votre secteur, dans leur propre langage. Deuxi\u00e8mement, d\u00e9finissez votre panel de concurrents, soit les cinq \u00e0 dix entreprises auxquelles vous pr\u00e9voyez d'\u00eatre compar\u00e9. Troisi\u00e8mement, soumettez r\u00e9guli\u00e8rement le questionnaire \u00e0 chaque moteur de recherche, en enregistrant la r\u00e9ponse compl\u00e8te et ses sources cit\u00e9es, car les r\u00e9ponses varient selon le jour, la formulation et le moteur utilis\u00e9. Quatri\u00e8mement, \u00e9valuez chaque r\u00e9ponse en fonction de sa fr\u00e9quence de pr\u00e9sence, de sa position et de sa part de citation, agr\u00e9gez ces scores pour obtenir une part par rapport \u00e0 votre panel de concurrents et analysez la tendance plut\u00f4t qu'une seule mesure.<\/p>\n Deux pr\u00e9cautions garantissent la fiabilit\u00e9 des donn\u00e9es. Les r\u00e9ponses \u00e9tant probabilistes, une mesure ponctuelle est trompeuse\u00a0; seule une mesure r\u00e9p\u00e9t\u00e9e et planifi\u00e9e r\u00e9v\u00e8le une tendance. De plus, en l\u2019absence de norme \u00e9tablie, les donn\u00e9es issues de diff\u00e9rents outils sont indiscernables les unes des autres. La valeur de cette mesure est intrins\u00e8que et directionnelle\u00a0: notre pr\u00e9sence est-elle en hausse\u00a0? Sur quels moteurs de recherche\u00a0? Pour quelles questions\u00a0? Face \u00e0 qui\u00a0? Ces informations suffisent \u00e0 orienter le contenu et \u00e0 justifier un budget.<\/p>\n Le tableau ci-dessous pr\u00e9sente les indicateurs cl\u00e9s, ce que chacun permet de d\u00e9terminer, ainsi qu'un exemple \u00e9nerg\u00e9tique, afin qu'une \u00e9quipe puisse transformer cette id\u00e9e en un rapport hebdomadaire reproductible.<\/p>\n L'achat d'\u00e9nergie ne se r\u00e9sume pas \u00e0 un simple clic. Il s'agit d'une d\u00e9cision coll\u00e9giale, souvent longue et complexe, prise par les services achats, ing\u00e9nierie et finance qui m\u00e8nent chacun leurs propres recherches. C'est pr\u00e9cis\u00e9ment dans ce contexte qu'un outil de r\u00e9ponse est le plus utile \u00e0 l'acheteur et le plus d\u00e9terminant pour le vendeur, car il contribue \u00e0 \u00e9tablir la liste restreinte que le comit\u00e9 examine ensuite. Nous \u00e9tudions comment ce comit\u00e9 se constitue et prend sa d\u00e9cision. vente au nouveau comit\u00e9 d'achat d'\u00e9nergie<\/a>, et la couche d'IA se superpose d\u00e9sormais \u00e0 elle.<\/p>\n Les cat\u00e9gories \u00e9nerg\u00e9tiques sont \u00e9galement restreintes et techniques, ce qui modifie la m\u00e9thode de mesure. Lorsqu'un acheteur demande qui est responsable des inspections sous-marines, qui assure la surveillance du m\u00e9thane \u00e0 grande \u00e9chelle ou qui peut garantir les donn\u00e9es CSRD pour une cha\u00eene d'approvisionnement p\u00e9troli\u00e8re et gazi\u00e8re, le mod\u00e8le dispose de moins de sources cr\u00e9dibles que pour une cat\u00e9gorie grand public. Cela a un double impact\u00a0: chaque citation a plus de poids, si bien qu'une seule page faisant autorit\u00e9 peut consid\u00e9rablement am\u00e9liorer votre visibilit\u00e9, mais une simple erreur factuelle, comme un assistant affirmant que vous n'op\u00e9rez pas dans une r\u00e9gion, peut avoir des cons\u00e9quences d\u00e9sastreuses. C'est pourquoi la qualit\u00e9 et l'exactitude des informations doivent \u00eatre prises en compte dans la mesure, et non pas seulement le nombre de mentions.<\/p>\n Le questionnaire doit donc \u00eatre \u00e9labor\u00e9 \u00e0 partir du langage authentique des march\u00e9s publics, tir\u00e9 des questions pos\u00e9es dans les appels d'offres, des entretiens avec les acheteurs et des signaux d'intention que l'entreprise recueille d\u00e9j\u00e0. Il s'agit de la m\u00eame m\u00e9thode que nous d\u00e9crivons dans Analyse des donn\u00e9es d'intention B2B sur l'\u00e9nergie et des signaux d'achat<\/a>Mesurez ce que les acheteurs demandent r\u00e9ellement, et non ce que vous souhaiteriez qu'ils demandent. Un questionnaire bas\u00e9 sur le langage marketing ne permet pas de mesurer le bon march\u00e9.<\/p>\n La premi\u00e8re \u00e9tape consiste \u00e0 mesurer avant d'optimiser. L'optimisation des moteurs de r\u00e9ponse, discipline de l'optimisation des moteurs g\u00e9n\u00e9ratifs, n'est rentable que si l'on peut v\u00e9rifier son efficacit\u00e9, ce qui est actuellement impossible pour la plupart des \u00e9quipes. Nous avons pr\u00e9sent\u00e9 le plan d'optimisation dans optimisation des moteurs g\u00e9n\u00e9ratifs pour le B2B \u00e9nerg\u00e9tique<\/a>; L'indicateur AI Share of Voice vous permet de savoir si vos efforts portent leurs fruits. Optimiser sans mesurer, c'est d\u00e9penser sans retour d'information.<\/p>\n La deuxi\u00e8me \u00e9tape consiste \u00e0 consid\u00e9rer la part de voix de l'IA comme un indicateur avanc\u00e9, et non comme un simple indicateur de vanit\u00e9. \u00c9tant donn\u00e9 que les acheteurs pr\u00e9s\u00e9lectionnent les options dans la r\u00e9ponse avant m\u00eame de contacter les \u00e9quipes commerciales, une pr\u00e9sence accrue de l'IA aujourd'hui se traduit par un pipeline commercial plus important demain, de la m\u00eame mani\u00e8re que l'intention et l'attribution alimentent les pr\u00e9visions \u00e0 long terme. Cet indicateur a toute sa place dans le rapport final, au m\u00eame titre que les indicateurs que nous d\u00e9crivons dans\u2026 attribution tout au long du cycle de vente d'\u00e9nergie<\/a>, et contre les cibles du compte dans Marketing bas\u00e9 sur les comptes pour le secteur de l'\u00e9nergie B2B<\/a>. Pr\u00e9sent\u00e9 trimestriellement par rapport \u00e0 un ensemble fixe de concurrents, il transforme une anxi\u00e9t\u00e9 diffuse concernant l'IA en un chiffre sur lequel une \u00e9quipe dirigeante peut s'appuyer.<\/p>\n La troisi\u00e8me \u00e9tape concerne le positionnement. Une marque \u00e9nerg\u00e9tique mesur\u00e9e, cit\u00e9e et correctement d\u00e9crite par les mod\u00e8les est une marque pr\u00e9sente au moment pr\u00e9cis o\u00f9 un comit\u00e9 se forge son opinion, et une marque capable de le prouver. C'est la position de partenaire de croissance que nous adoptons dans l'ensemble de notre travail\u00a0: une visibilit\u00e9, et les revenus qu'elle g\u00e9n\u00e8re, sont construits, non pr\u00e9sum\u00e9s.<\/p>","protected":false},"excerpt":{"rendered":" Your energy buyers now open ChatGPT, Gemini and Google’s AI Overviews before they open a browser tab, and they build vendor shortlists inside those answers. If your firm is not named, you are not in the running, and you will never see the loss in your analytics. This dossier sets out AI Share of Voi<\/p>","protected":false},"author":12,"featured_media":0,"comment_status":"open","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"p54_article_data":"{\"meta\": {\"kicker\": \"Insight \u00b7 AI Visibility\", \"topics\": [\"Marketing & Growth\", \"AI Search\"], \"title\": \"AI Share of Voice: Energy B2B's New Visibility Metric\", \"dek\": \"Your energy buyers now open ChatGPT, Gemini and Google's AI Overviews before they open a browser tab, and they build vendor shortlists inside those answers. If your firm is not named, you are not in the running, and you will never see the loss in your analytics. This dossier sets out AI Share of Voice, the metric that measures whether an energy brand appears when an answer engine is asked who to trust, and a practical method to measure it.\", \"date\": \"2026-07-23\", \"readTime\": \"10 min read\", \"author\": \"Project 54\"}, \"quickAnswer\": {\"q\": \"What is AI Share of Voice and how do energy B2B marketers measure it?\", \"a\": \"AI Share of Voice is the share of answers from AI engines such as ChatGPT, Gemini, Perplexity and Google's AI Overviews, across a fixed set of buyer questions, in which your brand is mentioned, cited or recommended, measured against named competitors. You measure it by defining a prompt set of 50 to 200 real questions your buyers ask, running those prompts across each engine on a regular schedule, and scoring three things: presence, whether you appear at all, position, where you appear in the answer, and citation share, how often your own pages are the source. There is no single industry standard formula yet, so the discipline is to fix one consistent method and track the trend over time and by engine.\"}, \"takeaways\": [\"Buyers have moved into the answer engine. Forrester found that 89 percent of B2B buyers had adopted generative AI as a top source of self-guided research, about three times the consumer rate, and G2 research in 2026 reported that 51 percent of B2B software buyers now begin their research inside an AI chatbot rather than a search engine. When the answer is the first touch, being named in it is the new front of the funnel.\", \"Invisibility in AI answers is silent. Unlike a lost search ranking, a missing mention leaves no impression, no click and no line in your analytics, so the loss is real but unrecorded. G2 found that 69 percent of buyers chose a different vendor than they first intended after AI guidance, and about a third bought from a vendor they had not previously heard of.\", \"AI Share of Voice is measurable, not mystical. It rests on three numbers on a fixed prompt set: presence rate, the share of answers that mention you, position, where in the answer you land, and citation share, how often your pages are the cited source. AthenaHQ's State of AI Search 2026 put the average brand mention rate at about 17.2 percent, a low base that rewards early movers.\", \"The prompt set is the instrument, and in energy it must be built from real procurement language. Questions like which firms lead subsea inspection, who provides methane monitoring at scale, or who assures CSRD data for oil and gas are where committees actually start. Niche, technical categories surface fewer sources, so each citation carries more weight and each factual error costs more trust.\", \"Measurement is the prerequisite for managing a new channel. Adobe reported that AI referrals converted about 31 percent higher than other online sources over the 2025 holiday period, and Gartner predicted in 2024 that traditional search volume would fall by around a quarter by 2026 as answer engines absorb queries. Whether or not that exact figure lands, the direction is set, and what is not measured cannot be defended in a budget review.\"], \"sections\": [{\"id\": \"what\", \"q\": \"What is AI Share of Voice, and why does it matter now?\", \"h\": \"The metric for a market that has moved into the answer\", \"p\": [\"Share of Voice has always been a proxy for attention: your slice of the advertising, the search rankings or the press coverage in a category. AI Share of Voice applies the same idea to a new surface. It is the percentage of answers from AI engines, ChatGPT, Gemini, Perplexity, Microsoft Copilot and Google's AI Overviews, across a defined set of buyer questions, in which your brand is mentioned, cited or recommended, measured against a named set of competitors.\", \"The reason it matters now is that the answer engine has become the first stop, not the last. Forrester reported that 89 percent of B2B buyers had adopted generative AI as a top source of self-guided research, roughly three times the consumer rate, according to coverage of its data by Demand Gen Report<\/a>. In 2026 G2 research found that 51 percent of B2B software buyers now begin their purchase research inside an AI chatbot rather than a search engine, with ChatGPT the dominant tool at 63 percent, as reported in G2's research release<\/a>.\", \"For energy firms this is not an abstract shift. When a procurement lead, an asset manager or an engineer asks an assistant to name credible suppliers for a scope of work, the model returns a shortlist. If your firm is on it, you are in consideration before a salesperson has done anything. If you are absent, you are out, and, unlike a lost search click, nothing in your analytics will ever tell you. AI Share of Voice exists to make that invisible loss visible, so it can be managed rather than guessed at.\"]}, {\"id\": \"why-now\", \"q\": \"Why has the buyer's first touch shifted into AI, and how fast?\", \"h\": \"The research journey now starts with a question, not a query\", \"p\": [\"Two forces are compounding. The first is behaviour: buyers prefer to research alone, and generative AI is the most efficient self-service tool ever handed to them. Long before an answer engine existed, analysts noted that B2B buyers spend the majority of the journey away from sales; the assistant simply accelerates that, compressing the early shortlist into a single conversation. Google research cited across the industry in late 2025 found that around 60 percent of B2B buyers now use tools like ChatGPT or Gemini to augment vendor lists, summarise content and surface competitors.\", \"The second force is the search page itself changing under buyers' feet. Google's AI Overviews now sit above the classic links on a large and contested share of queries. Trackers disagree on the exact figure because each samples a different keyword mix, but a defensible 2026 reading is that AI Overviews appear on roughly a fifth to a half of queries depending on the vertical, with BrightEdge data cited near 48 percent of tracked queries and Semrush measuring a rise from about 6.5 percent of queries in January 2025 to a peak near 25 percent before Google recalibrated. Treat any single number as an estimate; the trend is not in doubt.\", \"Gartner sharpened the point in 2024 when it predicted that traditional search engine volume would fall by around 25 percent by 2026 as chatbots and virtual agents absorb queries, advising that companies would need to focus on producing unique, useful content that demonstrates expertise, experience, authoritativeness and trustworthiness<\/a>. Some 2026 reviews argue that headline drop has not fully landed. The precise magnitude matters less than the direction: attention is migrating from the ten blue links to the single synthesised answer, and energy marketers who only measure the former are now blind to a growing part of the market.\"]}, {\"id\": \"metrics\", \"q\": \"What do you actually measure?\", \"h\": \"Three numbers on a fixed set of questions\", \"p\": [\"AI Share of Voice is not one number but a small family of them, all computed on the same fixed prompt set so the trend is comparable week to week. Vendors differ on the exact formula, HubSpot, Semrush and Profound each publish materially different methods, so the discipline is to choose one, document it, and hold it steady. Three measures carry most of the signal.\"], \"pillars\": [{\"n\": \"01\", \"t\": \"Presence rate\", \"d\": \"The share of answers, across your prompt set and chosen engines, in which your brand is mentioned at all. This is the base metric, the answer-engine equivalent of appearing on page one. AthenaHQ's State of AI Search 2026 put the average brand mention rate near 17.2 percent, which means most categories are wide open and a deliberate programme can move the needle quickly.\"}, {\"n\": \"02\", \"t\": \"Position and framing\", \"d\": \"Being named is not the same as being recommended. Track where you appear, first in a list or a footnote, and how you are framed, the category leader or a niche option. A model that lists you third behind two rivals is telling you something a raw mention count hides.\"}, {\"n\": \"03\", \"t\": \"Citation share\", \"d\": \"How often the answer's cited sources are your own pages rather than a directory, a competitor or a review site. Citation share is the most actionable metric because it links directly to content you control, and it is the clearest early signal that your authored material is being read and trusted by the models.\"}]}, {\"id\": \"method\", \"q\": \"How do you build the measurement in practice?\", \"h\": \"A four step method any energy marketing team can run\", \"p\": [\"The method is simple to state and demanding to do well. First, build the instrument: assemble 50 to 200 questions that real buyers in your category ask, in their language, not yours. Second, fix your competitor set, the five to ten firms you expect to be compared against. Third, run the prompt set across each engine on a regular schedule, capturing the full answer and its cited sources, because answers vary by day, by phrasing and by engine. Fourth, score each answer for presence, position and citation share, aggregate to a share against your competitor set, and watch the trend rather than any single reading.\", \"Two cautions keep the number honest. Answers are probabilistic, so a one-off snapshot is noise; only a repeated, scheduled measurement reveals a trend. And there is no agreed standard, so a figure from one tool cannot be compared with a figure from another. The value is internal and directional: is our presence rising, on which engines, for which questions, against whom. That is enough to steer content and to defend a budget.\", \"The table below sets out the core measures, what each answers, and an energy example, so a team can turn the idea into a repeatable weekly report.\"], \"table\": {\"cols\": [\"Metric\", \"The question it answers\", \"How to compute it\", \"Energy example\"], \"rows\": [[\"Presence rate\", \"Do we appear at all?\", \"Answers mentioning you divided by total answers in the prompt set\", \"You are named in 22 of 120 answers about EPC and inspection suppliers, a 18 percent presence rate\"], [\"Share of voice\", \"How do we compare with rivals?\", \"Your mentions divided by the total mentions of your named competitor set\", \"You hold 15 percent of mentions across a five firm methane monitoring peer set\"], [\"Position\", \"Are we recommended or just listed?\", \"Average rank of your mention within the answer, plus a note on framing\", \"You appear on average third, usually as a regional specialist rather than the default\"], [\"Citation share\", \"Is our own content the source?\", \"Answers citing your pages divided by all answers with citations\", \"Your site is cited in 9 of 40 sourced answers, a 22.5 percent citation share\"], [\"Sentiment and accuracy\", \"Are we described correctly?\", \"Manual review of tone and factual errors in answers that mention you\", \"Two answers wrongly state you do not operate offshore, a correction and content priority\"]]}}, {\"id\": \"energy\", \"q\": \"What makes AI visibility different in energy?\", \"h\": \"Committee buying, long cycles and thin, technical categories\", \"p\": [\"Energy buying is not a single click. It is a committee decision, often long and high value, made by procurement, engineering and finance functions that each research independently. That is precisely the environment in which an answer engine is most useful to the buyer and most consequential for the seller, because the assistant helps assemble the shortlist the committee then debates. We examine how that committee actually forms and decides in selling into the new energy buying committee<\/a>, and the AI layer now sits on top of it.\", \"Energy categories are also thin and technical, and that changes the measurement. When a buyer asks who leads subsea inspection, who provides methane monitoring at scale, or who can assure CSRD data for an oil and gas supply chain, the model has fewer credible sources to draw on than it would in a mass consumer category. That cuts both ways: each citation carries more weight, so a single authoritative page can lift your presence sharply, but a single factual error, an assistant claiming you do not operate in a region, does disproportionate damage. This is why sentiment and accuracy belong in the measurement, not just raw mentions.\", \"The prompt set, then, has to be engineered from genuine procurement language, drawn from tender questions, buyer interviews and the intent signals a firm already collects. It is the same discipline we describe in reading energy B2B intent data and buying signals<\/a>: measure what buyers actually ask, not what you wish they asked. A prompt set built from marketing language measures the wrong market.\"]}, {\"id\": \"b2b\", \"q\": \"What should energy marketing and sales leaders do with this?\", \"h\": \"Measure first, then optimise, and make it a standing KPI\", \"p\": [\"The first move is to measure before optimising. Optimising for answer engines, the discipline of generative engine optimisation, only pays off if you can see whether it is working, and most teams currently cannot. We set out the optimisation playbook in generative engine optimisation for energy B2B<\/a>; AI Share of Voice is the scoreboard that tells you whether that work is landing. Doing the optimisation without the measurement is spending without a feedback loop.\", \"The second move is to treat AI Share of Voice as a leading indicator, not a vanity metric. Because buyers shortlist inside the answer before they ever contact sales, a rising presence rate today is a rising pipeline tomorrow, in the same way that intent and attribution feed a long cycle forecast. It belongs on the same board report as the metrics we describe in attribution across the long energy sales cycle<\/a>, and against the account targets in account based marketing for energy B2B<\/a>. Reported quarterly against a fixed competitor set, it turns a diffuse anxiety about AI into a number a leadership team can act on.\", \"The third move is posture. An energy brand that is measured, cited and correctly described by the models is a brand that shows up at the exact moment a committee is forming its view, and one that can prove it. That is the growth partner stance we take across our work: visibility, and the revenue it protects, engineered, not assumed.\"]}], \"media\": {\"image\": {\"src\": \"\/wp-content\/uploads\/2026\/07\/illuminated-refinery-complex-at-night.jpg\", \"label\": \"An energy complex lit against the dark. In the answer engine era, being visible when a buyer asks who to trust is the new front of the funnel.\", \"credit\": \"Project 54\"}, \"infographicLabel\": \"AI Share of Voice: presence, position and citation share, measured on a fixed set of buyer questions.\"}, \"poll\": {\"q\": \"Where is your energy brand's biggest AI visibility gap right now?\", \"options\": [{\"id\": \"a\", \"label\": \"We are not measuring it at all\", \"insight\": \"The most common starting point. You cannot manage what you cannot see, and a missing AI mention leaves no trace in analytics, so the first win is simply standing up a repeatable measurement against a named competitor set.\"}, {\"id\": \"b\", \"label\": \"We appear, but behind competitors\", \"insight\": \"A position problem, not a presence problem. The lever is authoritative, structured content on the specific questions where rivals are cited and you are not, then re-measuring to confirm the shift.\"}, {\"id\": \"c\", \"label\": \"The models describe us inaccurately\", \"insight\": \"The most urgent case in a technical category. Wrong claims about your capabilities or geography actively lose deals, so accuracy and clear, citable source pages become the priority over raw reach.\"}], \"note\": \"One read on where to start. No tallies.\"}, \"faq\": [{\"q\": \"What is AI Share of Voice?\", \"a\": \"AI Share of Voice is the share of answers from AI engines such as ChatGPT, Gemini, Perplexity and Google's AI Overviews, measured across a fixed set of buyer questions, in which your brand is mentioned, cited or recommended relative to named competitors. It is the answer-engine equivalent of traditional share of voice in advertising or search, and it measures whether you appear when a buyer asks an assistant who to trust.\"}, {\"q\": \"How do you measure AI visibility for a B2B brand?\", \"a\": \"Define a prompt set of 50 to 200 real questions your buyers ask, fix a competitor set of five to ten rivals, run the prompts across each AI engine on a regular schedule, and score every answer for presence (do you appear), position (where and how you are framed) and citation share (whether your own pages are the source). Because answers vary by day and phrasing, the value is in the repeated trend, not a single snapshot.\"}, {\"q\": \"Is AI Share of Voice the same as SEO?\", \"a\": \"No. SEO measures ranking in a list of links, while AI Share of Voice measures whether you are named inside a synthesised answer. The two overlap, since AI engines often draw on well ranked, authoritative content, but a page can rank well and still be absent from AI answers, and a brand can be widely cited by models while its classic rankings lag. They are complementary metrics and should both be tracked.\"}, {\"q\": \"Why does AI visibility matter specifically for energy companies?\", \"a\": \"Energy purchases are committee decisions that are long, high value and heavily researched before any contact with sales, so the AI generated shortlist strongly shapes who gets considered. Energy categories are also technical and have fewer online sources, which means each citation carries more weight and each factual error does more damage, making accurate, authoritative presence in AI answers unusually important.\"}, {\"q\": \"Which AI engines should energy marketers track?\", \"a\": \"At a minimum, track ChatGPT, which dominates B2B research use, alongside Google's AI Overviews, Google Gemini, Perplexity and Microsoft Copilot, because buyers use several and each draws on different sources. Weight your effort toward the engines your specific buyers use, but measure across all of the major ones, since presence on one does not guarantee presence on another.\"}], \"newsletter\": {\"kicker\": \"The Energy Growth Brief\", \"title\": [\"Intelligence,\", \"to your inbox\"], \"body\": \"Join energy and industrial leaders getting our marketing, AI-growth and revenue-architecture intelligence, direct, no filler.\", \"placeholder\": \"you@company.com\", \"cta\": \"Subscribe\", \"note\": \"No spam. Unsubscribe anytime. We read every reply.\"}, \"related\": [{\"title\": \"Generative Engine Optimisation for Energy B2B\", \"topic\": \"Marketing & Growth\", \"href\": \"https:\/\/projectfifty4.com\/generative-engine-optimization-energy-b2b\/\"}, {\"title\": \"Selling Into the New Energy Buying Committee\", \"topic\": \"Specialism\", \"href\": \"https:\/\/projectfifty4.com\/selling-new-energy-buying-committee\/\"}, {\"title\": \"Energy B2B Marketing Attribution Across the Long Cycle\", \"topic\": \"Revenue Measurement\", \"href\": \"https:\/\/projectfifty4.com\/energy-b2b-marketing-attribution-long-cycle\/\"}, {\"title\": \"Account Based Marketing for Energy B2B\", \"topic\": \"Demand Architecture\", \"href\": \"https:\/\/projectfifty4.com\/account-based-marketing-energy-b2b\/\"}]}","p54_faq":"[{\"q\": \"What is AI Share of Voice?\", \"a\": \"AI Share of Voice is the share of answers from AI engines such as ChatGPT, Gemini, Perplexity and Google's AI Overviews, measured across a fixed set of buyer questions, in which your brand is mentioned, cited or recommended relative to named competitors. It is the answer-engine equivalent of traditional share of voice in advertising or search, and it measures whether you appear when a buyer asks an assistant who to trust.\"}, {\"q\": \"How do you measure AI visibility for a B2B brand?\", \"a\": \"Define a prompt set of 50 to 200 real questions your buyers ask, fix a competitor set of five to ten rivals, run the prompts across each AI engine on a regular schedule, and score every answer for presence (do you appear), position (where and how you are framed) and citation share (whether your own pages are the source). Because answers vary by day and phrasing, the value is in the repeated trend, not a single snapshot.\"}, {\"q\": \"Is AI Share of Voice the same as SEO?\", \"a\": \"No. SEO measures ranking in a list of links, while AI Share of Voice measures whether you are named inside a synthesised answer. The two overlap, since AI engines often draw on well ranked, authoritative content, but a page can rank well and still be absent from AI answers, and a brand can be widely cited by models while its classic rankings lag. They are complementary metrics and should both be tracked.\"}, {\"q\": \"Why does AI visibility matter specifically for energy companies?\", \"a\": \"Energy purchases are committee decisions that are long, high value and heavily researched before any contact with sales, so the AI generated shortlist strongly shapes who gets considered. Energy categories are also technical and have fewer online sources, which means each citation carries more weight and each factual error does more damage, making accurate, authoritative presence in AI answers unusually important.\"}, {\"q\": \"Which AI engines should energy marketers track?\", \"a\": \"At a minimum, track ChatGPT, which dominates B2B research use, alongside Google's AI Overviews, Google Gemini, Perplexity and Microsoft Copilot, because buyers use several and each draws on different sources. Weight your effort toward the engines your specific buyers use, but measure across all of the major ones, since presence on one does not guarantee presence on another.\"}]","p54_media":"","p54_comments_enabled":"","footnotes":""},"categories":[92,125],"tags":[],"class_list":["post-3900","post","type-post","status-publish","format-standard","hentry","category-analysis","category-strategy"],"acf":[],"_links":{"self":[{"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/posts\/3900","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/comments?post=3900"}],"version-history":[{"count":1,"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/posts\/3900\/revisions"}],"predecessor-version":[{"id":3901,"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/posts\/3900\/revisions\/3901"}],"wp:attachment":[{"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/media?parent=3900"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/categories?post=3900"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/projectfifty4.com\/fr\/wp-json\/wp\/v2\/tags?post=3900"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Le parcours de recherche commence d\u00e9sormais par une question, et non plus par une requ\u00eate.<\/h2>\n
Trois nombres sur un ensemble fixe de questions<\/h2>\n
\n
Une m\u00e9thode en quatre \u00e9tapes que toute \u00e9quipe de marketing \u00e9nerg\u00e9tique peut mettre en \u0153uvre<\/h2>\n
Achats par comit\u00e9, cycles longs et cat\u00e9gories techniques restreintes<\/h2>\n
Mesurez d'abord, puis optimisez, et faites-en un indicateur de performance cl\u00e9 permanent.<\/h2>\n