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
Der Grund, warum dies jetzt relevant ist, liegt darin, dass die Antwortmaschine nicht mehr die letzte, sondern der erste Anlaufpunkt ist. Forrester berichtete, dass 89 Prozent der B2B-Eink\u00e4ufer generative KI als wichtigste Quelle f\u00fcr selbstgesteuerte Recherchen nutzen \u2013 etwa dreimal so viele wie bei Endverbrauchern, wie aus den Daten hervorgeht. Nachfragegenerierungsbericht<\/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 Forschungsver\u00f6ffentlichung<\/a>.<\/p>\n F\u00fcr Energieunternehmen ist dies keine abstrakte Ver\u00e4nderung. Wenn ein Einkaufsleiter, ein Anlagenmanager oder ein Ingenieur einen Assistenten bittet, zuverl\u00e4ssige Lieferanten f\u00fcr einen bestimmten Auftrag zu nennen, liefert das Modell eine Vorauswahlliste. Ist Ihr Unternehmen darauf vertreten, werden Sie ber\u00fccksichtigt, noch bevor ein Vertriebsmitarbeiter aktiv geworden ist. Fehlt Ihr Unternehmen, scheiden Sie aus \u2013 und im Gegensatz zu einem verlorenen Suchklick werden Sie davon in Ihren Analysen nichts erfahren. AI Share of Voice macht diesen unsichtbaren Verlust sichtbar, sodass er gesteuert statt nur erahnt werden kann.<\/p>\n Zwei Faktoren wirken zusammen. Erstens das Kaufverhalten: K\u00e4ufer recherchieren bevorzugt selbstst\u00e4ndig, und generative KI ist das effizienteste Self-Service-Tool, das ihnen je zur Verf\u00fcgung stand. Schon lange vor der Existenz von Antwort-Engines stellten Analysten fest, dass B2B-K\u00e4ufer den Gro\u00dfteil ihrer Kaufentscheidung abseits des eigentlichen Verkaufsgespr\u00e4chs verbringen; der Assistent beschleunigt diesen Prozess lediglich, indem er die erste Vorauswahl in einem einzigen Gespr\u00e4ch zusammenfasst. Eine branchenweit zitierte Google-Studie aus dem Jahr 2025 ergab, dass rund 60 Prozent der B2B-K\u00e4ufer bereits Tools wie ChatGPT oder Gemini nutzen, um Anbieterlisten zu erg\u00e4nzen, Inhalte zusammenzufassen und Wettbewerber aufzusp\u00fcren.<\/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 unterstrich diese These 2024 mit der Prognose, dass das Suchvolumen traditioneller Suchmaschinen bis 2026 um rund 25 Prozent sinken w\u00fcrde, da Chatbots und virtuelle Assistenten die Anfragen \u00fcbernehmen w\u00fcrden. Gartner riet Unternehmen daher, \u2026 Fokus auf die Erstellung einzigartiger, n\u00fctzlicher Inhalte, die Fachkompetenz, Erfahrung, Autorit\u00e4t und Vertrauensw\u00fcrdigkeit demonstrieren<\/a>. Einige Analysen aus dem Jahr 2026 argumentieren, dass der erwartete Preisverfall noch nicht vollst\u00e4ndig eingetreten ist. Die genaue St\u00e4rke ist weniger wichtig als die Richtung: Die Aufmerksamkeit verlagert sich von den zehn blauen Links hin zu einer einzigen, zusammengefassten Antwort, und Energieunternehmen, die nur die zehn blauen Links messen, \u00fcbersehen nun einen wachsenden Teil des Marktes.<\/p>\n Der Marktanteil von KI-Nutzern ist nicht nur eine Kennzahl, sondern setzt sich aus mehreren Werten zusammen, die alle auf Basis desselben festen Fragensatzes berechnet werden, sodass die Entwicklung von Woche zu Woche vergleichbar ist. Die Anbieter verwenden unterschiedliche Formeln; HubSpot, Semrush und Profound ver\u00f6ffentlichen jeweils deutlich abweichende Methoden. Daher ist es ratsam, eine Methode auszuw\u00e4hlen, diese zu dokumentieren und beizubehalten. Drei dieser Kennzahlen liefern die wichtigsten Informationen.<\/p>\n Die Methode ist einfach zu beschreiben, aber anspruchsvoll in der Anwendung. Zuerst erstellen Sie das Instrument: Stellen Sie 50 bis 200 Fragen zusammen, die echte K\u00e4ufer in Ihrer Branche stellen \u2013 in ihrer Sprache, nicht in Ihrer. Zweitens: Legen Sie Ihre Wettbewerber fest \u2013 die f\u00fcnf bis zehn Unternehmen, mit denen Sie verglichen werden m\u00f6chten. Drittens: F\u00fchren Sie die Fragen regelm\u00e4\u00dfig auf jeder Suchmaschine durch und erfassen Sie die vollst\u00e4ndigen Antworten inklusive der zitierten Quellen, da die Antworten je nach Tag, Formulierung und Suchmaschine variieren. Viertens: Bewerten Sie jede Antwort hinsichtlich H\u00e4ufigkeit, Position und Zitationsanteil, aggregieren Sie die Ergebnisse im Vergleich zu Ihren Wettbewerbern und beobachten Sie den Trend anstatt einzelner Werte.<\/p>\n Zwei wichtige Punkte sorgen f\u00fcr aussagekr\u00e4ftige Ergebnisse. Die Antworten sind Wahrscheinlichkeitsverteilungen, daher liefert eine Momentaufnahme nur ungenaue Daten; erst regelm\u00e4\u00dfige, planm\u00e4\u00dfige Messungen zeigen einen Trend. Und es gibt keinen einheitlichen Standard, sodass die Ergebnisse verschiedener Messinstrumente nicht miteinander vergleichbar sind. Der Wert ist intern und richtungsweisend: Steigt unsere Pr\u00e4senz, auf welchen Plattformen, zu welchen Fragen und gegen wen? Das gen\u00fcgt, um Inhalte zu steuern und ein Budget zu rechtfertigen.<\/p>\n Die Tabelle unten zeigt die wichtigsten Kennzahlen, was diese jeweils beantworten, und ein Beispiel aus dem Energiebereich, damit ein Team die Idee in einen wiederholbaren w\u00f6chentlichen Bericht umsetzen kann.<\/p>\n Der Energieeinkauf ist keine Sache von einem Klick. Es handelt sich um eine oft langwierige und kostspielige Entscheidung eines Gremiums, bestehend aus den Abteilungen Einkauf, Technik und Finanzen, die jeweils unabh\u00e4ngig voneinander recherchieren. Genau in diesem Umfeld ist ein Antwortsystem f\u00fcr den K\u00e4ufer am n\u00fctzlichsten und f\u00fcr den Verk\u00e4ufer am folgenreichsten, da es die Erstellung der Vorauswahl unterst\u00fctzt, \u00fcber die das Gremium anschlie\u00dfend ber\u00e4t. Wir untersuchen, wie dieses Gremium tats\u00e4chlich gebildet wird und Entscheidungen trifft. Verkauf an den neuen Energieeinkaufsausschuss<\/a>, Und die KI-Schicht befindet sich nun dar\u00fcber.<\/p>\n Energiekategorien sind ebenfalls komplex und technisch, was die Bewertung beeinflusst. Fragt ein K\u00e4ufer beispielsweise nach Anbietern von Unterwasserinspektionen, gro\u00dffl\u00e4chigen Methan\u00fcberwachungen oder CSRD-Daten f\u00fcr die \u00d6l- und Gaslieferkette, stehen dem Modell weniger glaubw\u00fcrdige Quellen zur Verf\u00fcgung als in einer Konsumg\u00fcterkategorie. Das hat Vor- und Nachteile: Jede einzelne Erw\u00e4hnung hat mehr Gewicht, sodass eine einzige ma\u00dfgebliche Webseite die Pr\u00e4senz deutlich steigern kann. Ein einziger sachlicher Fehler hingegen, etwa die Behauptung eines Mitarbeiters, das Unternehmen sei in einer bestimmten Region nicht t\u00e4tig, richtet unverh\u00e4ltnism\u00e4\u00dfig gro\u00dfen Schaden an. Daher sollten neben reinen Erw\u00e4hnungen auch Sentimentalit\u00e4t und Genauigkeit in die Bewertung einflie\u00dfen.<\/p>\n Die vorgegebenen Fragen m\u00fcssen daher aus authentischer Beschaffungssprache abgeleitet werden, die sich aus Ausschreibungsfragen, K\u00e4uferinterviews und den bereits vom Unternehmen erfassten Absichtssignalen speist. Es handelt sich um dieselbe Vorgehensweise, die wir in [Referenz einf\u00fcgen] beschreiben. Lesen von B2B-Absichtsdaten und Kaufsignalen im Energiesektor<\/a>Messen Sie, was K\u00e4ufer tats\u00e4chlich fragen, nicht, was Sie sich w\u00fcnschen, dass sie fragen. Ein aus Marketingsprache erstellter Fragenkatalog misst den falschen Markt.<\/p>\n Der erste Schritt besteht darin, vor der Optimierung zu messen. Die Optimierung von Antwortsystemen, also die Optimierung generativer Systeme, lohnt sich nur, wenn man ihre Funktionsweise \u00fcberpr\u00fcfen kann, was den meisten Teams derzeit nicht m\u00f6glich ist. Wir haben den Optimierungsleitfaden in [Link einf\u00fcgen] dargelegt. Generative Engine-Optimierung f\u00fcr Energie B2B<\/a>; Der KI-Share of Voice ist die Messlatte, die Ihnen anzeigt, ob Ihre Ma\u00dfnahmen Wirkung zeigen. Optimierung ohne Messung ist Geldverschwendung ohne Feedbackschleife.<\/p>\n Der zweite Schritt besteht darin, den KI-Share of Voice als Fr\u00fchindikator und nicht als blo\u00dfe Kennzahl zu betrachten. Da K\u00e4ufer bereits vor der Kontaktaufnahme mit dem Vertrieb eine Vorauswahl treffen, bedeutet eine steigende Pr\u00e4senzrate heute eine wachsende Pipeline morgen \u2013 \u00e4hnlich wie Intention und Attribution eine langfristige Prognose beeinflussen. Er geh\u00f6rt in denselben Lagebericht wie die Kennzahlen, die wir in Abschnitt [Abschnittsnummer einf\u00fcgen] beschreiben. Attribution \u00fcber den langen Energieverkaufszyklus hinweg<\/a>, und gegen die Kontoziele in Account-basiertes Marketing f\u00fcr B2B-Energieunternehmen<\/a>. Die viertelj\u00e4hrliche Berichterstattung im Vergleich zu einem festen Wettbewerbsumfeld wandelt eine diffuse Besorgnis \u00fcber KI in eine Zahl um, auf deren Grundlage ein F\u00fchrungsteam handeln kann.<\/p>\n Der dritte Schritt ist die Positionierung. Eine Energiemarke, die gemessen, zitiert und von den Modellen korrekt beschrieben wird, ist genau dann pr\u00e4sent, wenn sich ein Gremium eine Meinung bildet \u2013 und sie kann diese auch beweisen. Das ist die Haltung, die wir als Wachstumspartner in unserer gesamten Arbeit einnehmen: Sichtbarkeit und die damit verbundenen, gesch\u00fctzten Ums\u00e4tze \u2013 gezielt geschaffen, nicht vorausgesetzt.<\/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\/de\/wp-json\/wp\/v2\/posts\/3900","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/users\/12"}],"replies":[{"embeddable":true,"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/comments?post=3900"}],"version-history":[{"count":1,"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/posts\/3900\/revisions"}],"predecessor-version":[{"id":3901,"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/posts\/3900\/revisions\/3901"}],"wp:attachment":[{"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/media?parent=3900"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/categories?post=3900"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/projectfifty4.com\/de\/wp-json\/wp\/v2\/tags?post=3900"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Der Forschungsprozess beginnt nun mit einer Frage, nicht mit einer Anfrage.<\/h2>\n
Drei Zahlen zu einem festgelegten Fragenkatalog<\/h2>\n
\n
Eine Vier-Schritte-Methode, die jedes Energiemarketingteam anwenden kann<\/h2>\n
Ausschussk\u00e4ufe, lange Zyklen und d\u00fcnne, technische Kategorien<\/h2>\n
Erst messen, dann optimieren und zu einem festen KPI machen.<\/h2>\n