{"id":2434,"date":"2026-06-01T03:03:54","date_gmt":"2026-06-01T03:03:54","guid":{"rendered":"https:\/\/blogs.consentplace.com\/en\/?page_id=2434"},"modified":"2026-05-28T15:21:19","modified_gmt":"2026-05-28T15:21:19","slug":"95-5-2","status":"publish","type":"post","link":"https:\/\/blogs.consentplace.com\/en\/tech\/95-5-2\/","title":{"rendered":"95% of enterprise AI fails. The 5% that doesn’t has one thing the rest is missing."},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n\n\nWhy 95% of Enterprise AI Fails \u2014 and the Missing Layer the Other 5% Have \u2013 Official Blog<\/title>\n<link href=\"https:\/\/fonts.googleapis.com\/css2?family=DM+Sans:ital,opsz,wght@0,9..40,300;0,9..40,400;0,9..40,500;0,9..40,600;0,9..40,700;1,9..40,400&family=DM+Serif+Display:ital@0;1&display=swap\" rel=\"stylesheet\">\n<style>\n :root {\n --orange: #FF6B35;\n --orange-dark: #E8521A;\n --orange-light: #fff3ee;\n --dark: #1a1a1a;\n --mid: #555;\n --light: #888;\n --border: #e8e8e8;\n --bg: #ffffff;\n --cream: #FAF8F5;\n }\n * { box-sizing: border-box; margin: 0; padding: 0; }\n body { font-family: 'DM Sans', sans-serif; color: var(--dark); background: var(--bg); line-height: 1.75; font-size: 17px; }\n .post-container { max-width: 760px; margin: 0 auto; padding: 60px 24px 100px; }\n .label { display: inline-block; font-size: 11px; font-weight: 600; letter-spacing: 0.12em; text-transform: uppercase; color: var(--orange); border: 1px solid var(--orange); border-radius: 3px; 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}\n .exec-summary strong { color: #fff; }\n\n h2 { font-family: 'DM Serif Display', serif; font-size: 28px; color: var(--dark); margin-bottom: 20px; margin-top: 56px; line-height: 1.25; }\n h3 { font-family: 'DM Serif Display', serif; font-size: 22px; color: var(--dark); margin-bottom: 16px; margin-top: 40px; line-height: 1.3; }\n p { margin-bottom: 22px; color: var(--mid); }\n p strong { color: var(--dark); }\n a { color: var(--orange); }\n em.agent { font-style: italic; color: var(--orange); }\n\n \/* STAT STRIP *\/\n .stat-strip { display: grid; grid-template-columns: 1fr 1fr 1fr; gap: 14px; margin: 44px 0; }\n @media (max-width: 560px) { .stat-strip { grid-template-columns: 1fr; } }\n .stat-cell { background: var(--cream); border-radius: 8px; padding: 24px 18px; text-align: center; border-left: 3px solid var(--orange); }\n .stat-cell .sv { font-family: 'DM Serif Display', serif; font-size: 44px; color: var(--orange); line-height: 1; margin-bottom: 10px; }\n .stat-cell .sl { font-size: 13px; color: var(--mid); line-height: 1.45; }\n\n \/* PULL QUOTE *\/\n blockquote { border-left: 3px solid var(--orange); padding: 18px 28px; margin: 36px 0; font-size: 20px; font-family: 'DM Serif Display', serif; font-style: italic; color: var(--dark); line-height: 1.5; background: var(--cream); border-radius: 0 8px 8px 0; }\n\n \/* FOUR-FUNCTION GRID *\/\n .func-grid {\n display: grid;\n grid-template-columns: 1fr 1fr;\n gap: 16px;\n margin: 36px 0;\n }\n @media (max-width: 600px) { .func-grid { grid-template-columns: 1fr; } }\n .func-card {\n background: var(--cream);\n border: 1px solid var(--border);\n border-radius: 10px;\n padding: 22px 24px;\n border-top: 3px solid var(--orange);\n }\n .func-num {\n font-family: 'DM Serif Display', serif;\n font-style: italic;\n font-size: 22px;\n color: var(--orange);\n margin-bottom: 6px;\n }\n .func-title {\n font-size: 17px;\n font-weight: 700;\n color: var(--dark);\n margin-bottom: 10px;\n }\n .func-body {\n font-size: 15px;\n line-height: 1.65;\n color: var(--mid);\n }\n\n \/* HIGHLIGHT BOX *\/\n .highlight {\n background: var(--orange-light);\n border-radius: 8px;\n padding: 24px 28px;\n margin: 36px 0;\n border-left: 3px solid var(--orange);\n }\n .highlight p {\n color: var(--dark);\n font-size: 16px;\n line-height: 1.7;\n margin-bottom: 12px;\n }\n .highlight p:last-child { margin-bottom: 0; }\n .highlight strong { color: var(--orange-dark); }\n\n \/* CTA *\/\n .cta-box { background: var(--dark); border-radius: 10px; padding: 40px; margin-top: 56px; text-align: center; }\n .cta-box h3 { font-family: 'DM Serif Display', serif; font-size: 26px; color: #fff; font-weight: 400; margin-bottom: 12px; margin-top: 0; }\n .cta-box p { color: #888; font-size: 14px; margin-bottom: 24px; }\n .cta-box a.btn { display: inline-block; background: var(--orange); color: #fff; text-decoration: none; font-weight: 700; font-size: 14px; letter-spacing: 0.04em; padding: 13px 28px; border-radius: 6px; transition: opacity 0.15s; }\n .cta-box a.btn:hover { opacity: 0.85; }\n\n \/* REFERENCES *\/\n .references { margin-top: 56px; padding-top: 28px; border-top: 1px solid var(--border); font-size: 13px; color: var(--light); }\n .references h3 { font-size: 11px; letter-spacing: 0.1em; text-transform: uppercase; color: var(--light); margin-bottom: 14px; margin-top: 0; font-family: 'DM Sans', sans-serif; font-weight: 700; }\n .references ol { padding-left: 20px; }\n .references li { margin-bottom: 10px; line-height: 1.55; }\n .references a { color: var(--orange); text-decoration: none; }\n<\/style>\n\n\n<div class=\"post-container\">\n\n <div class=\"label\">AI Enterprise \u00b7 Emotional Dynamics \u00b7 Research<\/div>\n\n <h1>Why <em>95%<\/em> of enterprise AI fails \u2014 and the missing layer the other 5% have.<\/h1>\n\n <p class=\"deck\">Personalization is 30 years old. The Why behind every interaction is not. Neither is the How. And neither is the Reception \u2014 the layer that decides whether AI lands or quietly fails.<\/p>\n\n <!-- EXEC SUMMARY -->\n <div class=\"exec-summary\">\n <div class=\"es-label\">Executive Summary<\/div>\n <p>In 2025, MIT found that <strong>95% of enterprise generative AI deployments fail to deliver measurable P&L value<\/strong>. Gartner predicts <strong>over 40% of agentic AI projects will be canceled by 2027<\/strong>. The failure mode is not model capability. The models work. The infrastructure scales. The investment is there.<\/p>\n <p>What’s failing is <strong>reception<\/strong> \u2014 the layer between technically correct AI output and the human receiving it. The 5% that succeed have built an emotional infrastructure layer, whether they call it that or not. The 95% have not.<\/p>\n <p>This post lays out the four functions that layer has to perform \u2014 and why ConsentPlace<em class=\"agent\">Agent<\/em> is the only Emotional Dynamics infrastructure layer purpose-built for it.<\/p>\n <\/div>\n\n <!-- THE NUMBERS -->\n <h2>The numbers most enterprise AI conversations refuse to start with<\/h2>\n\n <p>In August 2025, MIT’s Project NANDA published <em>The GenAI Divide: State of AI in Business 2025<\/em>. The headline finding, drawn from interviews with 150 leaders, a survey of 350 employees, and an analysis of 300 enterprise AI deployments, was this: <strong>95% of enterprise AI deployments fail to deliver measurable P&L value.<\/strong><\/p>\n\n <p>Despite $30\u201340B in enterprise generative AI investment, only 5% of pilots achieve rapid revenue acceleration. The vast majority stall \u2014 delivering little to no measurable impact on the business.<\/p>\n\n <p>Two months earlier, Gartner had published a forecast for the next category up the stack: <strong>more than 40% of agentic AI projects will be canceled by the end of 2027<\/strong> due to escalating costs, unclear business value, or inadequate risk controls.<\/p>\n\n <div class=\"stat-strip\">\n <div class=\"stat-cell\">\n <div class=\"sv\">95%<\/div>\n <div class=\"sl\">of enterprise GenAI pilots fail to deliver P&L value (MIT Project NANDA, 2025)<\/div>\n <\/div>\n <div class=\"stat-cell\">\n <div class=\"sv\">40%+<\/div>\n <div class=\"sl\">of agentic AI projects forecast to be canceled by 2027 (Gartner)<\/div>\n <\/div>\n <div class=\"stat-cell\">\n <div class=\"sv\">$30\u201340B<\/div>\n <div class=\"sl\">of enterprise GenAI investment, most of it producing zero measurable return (MIT)<\/div>\n <\/div>\n <\/div>\n\n <p>Neither study is about model capability. The models work. The infrastructure scales. The investment is there. What’s failing is execution.<\/p>\n\n <p>The question worth asking, before any enterprise commits the next dollar to AI infrastructure: <strong>what does the 5% have that the 95% doesn’t?<\/strong><\/p>\n\n <!-- MISSING LAYER -->\n <h2>The missing layer in plain language<\/h2>\n\n <p>Generative AI deployments at enterprise scale fail for one structural reason. They are built to be <em>technically correct<\/em>. They are not built to be <em>emotionally received<\/em>.<\/p>\n\n <p>A customer support AI that gives the factually right answer to a customer in <strong>Remorse<\/strong> closes the relationship. A sales AI that pushes the next-best-action to a customer in <strong>hesitation<\/strong> converts the hesitation into resistance. A compliance bot that delivers a privacy notice to a user in <strong>Contempt<\/strong> confirms their suspicion that the brand is hiding something. The output is technically correct. The reception is wrong. And the reception is what drives the P&L.<\/p>\n\n <p>This is not a problem any model upgrade fixes. MIT’s research points to flawed enterprise integration \u2014 generic tools excel for individuals because of their flexibility, but stall in enterprise use because they don’t adapt to the workflows or the humans they serve. The capability gap MIT identifies is not what the model can <em>do<\/em>. It is what the deployment can <em>sense<\/em>, what it can <em>respond to<\/em>, and how it can <em>adapt<\/em> to the human on the other side of the conversation.<\/p>\n\n <blockquote>\n The 95% deliver technically correct AI into emotional moments that cannot receive it.<br>\n The 5% adapt to who’s on the other side.\n <\/blockquote>\n\n <p>That is an emotional context gap. It is also a measurement gap, a routing gap, and a consent gap. Together, they define the missing layer.<\/p>\n\n <!-- ANTHROPIC + URGENCY -->\n <h2>Why this becomes more urgent, not less, as AI gets better<\/h2>\n\n <p>In April 2026, Anthropic’s interpretability team published <em>Emotion concepts and their function in a large language model<\/em>. The paper analyzed how large language models internally represent emotional concepts and found that the functional emotional representations inside the model are organized in a structure that echoes the kind of structured emotional taxonomy Plutchik’s psychoevolutionary model describes. More importantly, the paper found that these emotion representations are <strong>causal<\/strong> \u2014 they drive model behavior in measurable, consequential ways, including behaviors relevant to AI safety.<\/p>\n\n <p>Read this finding alongside MIT and Gartner, and a pattern emerges:<\/p>\n\n <div class=\"highlight\">\n <p>The MIT study tells us that <strong>95% of enterprise AI deployments fail to capture value<\/strong>.<\/p>\n <p>The Gartner study tells us that <strong>40%+ of the next category \u2014 agentic AI \u2014 will be canceled before reaching production<\/strong>.<\/p>\n <p>The Anthropic study tells us that <strong>emotional context is a real, functional, causal substrate inside the AI systems being deployed<\/strong>.<\/p>\n <\/div>\n\n <p>The reason enterprise AI fails is not that emotion is irrelevant to AI. The reason is that emotion is <em>operative inside the AI<\/em> and <em>invisible in the deployment<\/em>. Models have emotional context. Deployments don’t.<\/p>\n\n <p>That is the layer that has to be built.<\/p>\n\n <!-- FOUR FUNCTIONS -->\n <h2>The four functions of an Emotional Dynamics infrastructure layer<\/h2>\n\n <p>Any enterprise AI deployment that intends to be in the 5% rather than the 95% will require an Emotional Dynamics infrastructure layer that performs four functions:<\/p>\n\n <div class=\"func-grid\">\n <div class=\"func-card\">\n <div class=\"func-num\">01<\/div>\n <div class=\"func-title\">Detection<\/div>\n <div class=\"func-body\">Per-interaction emotional state, at a resolution sufficient to distinguish between states that require opposite responses. A customer in Remorse and a customer in Contempt both register as “negative” in three-state sentiment analysis. They require opposite responses. The system that cannot tell them apart is operating without enough information to choose correctly.<\/div>\n <\/div>\n <div class=\"func-card\">\n <div class=\"func-num\">02<\/div>\n <div class=\"func-title\">Reception<\/div>\n <div class=\"func-body\">Adaptation of the AI’s output to the emotional state of the human, not just to the content of the message. A factually correct response delivered at the wrong emotional moment closes the relationship; the same content at the right moment builds it.<\/div>\n <\/div>\n <div class=\"func-card\">\n <div class=\"func-num\">03<\/div>\n <div class=\"func-title\">Consent<\/div>\n <div class=\"func-body\">Architectural treatment of consent as a property of every interaction, not a one-time checkbox at signup. The emotional moment determines whether a privacy disclosure builds credibility or destroys it. At industrial scale, consent has to be operational, not procedural.<\/div>\n <\/div>\n <div class=\"func-card\">\n <div class=\"func-num\">04<\/div>\n <div class=\"func-title\">Audit<\/div>\n <div class=\"func-body\">Every emotional metadata record consent-stamped, versioned, and reviewable, so the deployment survives the regulatory and compliance review that any production enterprise AI now requires under the EU AI Act, GDPR, and equivalent frameworks.<\/div>\n <\/div>\n <\/div>\n\n <p>These four functions are not independent. They have to compose into a single architectural layer that sits between the model and the human, in real time, without breaking enterprise governance constraints. That is what an Emotional Dynamics infrastructure layer is.<\/p>\n\n <!-- OPENAI DEPLOYMENT -->\n <h2>How this connects to OpenAI’s $4B move on the deployment layer<\/h2>\n\n <p>On May 11, 2026, OpenAI announced the OpenAI Deployment Company, backed by $4B+ in investment from TPG, Bain Capital, Goldman Sachs, SoftBank, Warburg Pincus, and the major consultancies. The founding acquisition was Tomoro, a London-based AI consultancy with ~150 Forward Deployed Engineers and enterprise clients including Tesco, Virgin Atlantic, Mattel, and Red Bull.<\/p>\n\n <p>The stated purpose: accelerate enterprise AI deployment in the same Palantir-style “embed engineers inside the customer” model that has historically separated successful deployments from stalled ones.<\/p>\n\n <p>The Deployment Company news matters here for one reason: <strong>OpenAI itself just put $4B behind the diagnosis that deployment, not model capability, is the next enterprise battleground<\/strong>. The MIT and Gartner numbers are not pessimism \u2014 they are the market opportunity that an entire industry is now repositioning to address.<\/p>\n\n <p>But deployment alone closes only part of the gap. Forward Deployed Engineers can integrate the model into the workflow. They cannot, by themselves, give the model emotional context about the human on the other side of the conversation. That requires a different layer, built on different scientific foundations, with consent as architecture rather than as compliance.<\/p>\n\n <p><b>That layer is what ConsentPlace builds.<\/b><\/p>\n\n <!-- CONSENTPLACE IN ONE SENTENCE -->\n <h2>What ConsentPlace is, in one sentence<\/h2>\n\n <p>ConsentPlace<em class=\"agent\">Agent<\/em> is the only Emotional Dynamics infrastructure layer for enterprise AI \u2014 a real-time, consent-native API that detects, guides, and anticipates the full spectrum of human emotion across Plutchik’s 24 dyads, so that AI systems can respond to what people feel, not just what they say. <strong>Three lines of code on the existing stack. No rip-and-replace. GDPR-native architecture.<\/strong><\/p>\n\n <p>An Emotional Dynamics infrastructure layer with these four functions \u2014 detection, reception, consent, audit \u2014 is becoming a structural requirement for any enterprise AI deployment that intends to capture P&L value rather than join the 95% that don’t.<\/p>\n\n <!-- THREE QUESTIONS -->\n <h2>Where this leaves the enterprise AI buyer<\/h2>\n\n <p>Three questions worth asking before the next AI deployment goes to production:<\/p>\n\n <div class=\"func-grid\">\n <div class=\"func-card\">\n <div class=\"func-num\">01<\/div>\n <div class=\"func-title\">Detection<\/div>\n <div class=\"func-body\">Does the system distinguish between emotional states that require opposite responses \u2014 or does it collapse them into three sentiment categories?<\/div>\n <\/div>\n <div class=\"func-card\">\n <div class=\"func-num\">02<\/div>\n <div class=\"func-title\">Reception<\/div>\n <div class=\"func-body\">Does the system adapt its output to the emotional state of the human receiving it \u2014 or does it deliver technically correct content into emotional moments that cannot receive it?<\/div>\n <\/div>\n <div class=\"func-card\">\n <div class=\"func-num\">03<\/div>\n <div class=\"func-title\">Consent<\/div>\n <div class=\"func-body\">Is consent architectural in the deployment \u2014 or is it a checkbox that was solved at signup and then forgotten?<\/div>\n <\/div>\n <\/div>\n\n <p>If the answer to any of these is “no” or “we haven’t thought about it,” the deployment is structurally exposed to the same failure mode that has claimed 95% of generative AI pilots and is on track to claim 40% of agentic AI projects.<\/p>\n\n <p>The 5% have an Emotional Dynamics infrastructure layer, whether they call it that or not. The 95% don’t.<\/p>\n\n <blockquote>\n Personalization was the bet of the last 30 years.<br>\n Emotional Dynamics is the bet of the next 30.\n <\/blockquote>\n\n <p>The companies that build it into their AI stack now \u2014 and the companies that acquire it instead of attempting to build it from scratch \u2014 will be the ones whose AI investments produce ROI instead of headlines about cancellation.<\/p>\n\n <!-- CTA -->\n <div class=\"cta-box\">\n <h3>The only Emotional Dynamics infrastructure layer. Live now.<\/h3>\n <p>Three lines of code. No rip-and-replace. GDPR-native by design.<\/p>\n <a target=\"_blank\" href=\"mailto:info@consentplace.com\" class=\"btn\">Contact Us \u2192<\/a>\n <\/div>\n\n <!-- REFERENCES -->\n <div class=\"references\">\n <h3>References & Sources<\/h3>\n <ol>\n <li>MIT Project NANDA. <em>The GenAI Divide: State of AI in Business 2025.<\/em> Published August 2025. Lead researcher: Aditya Challapally. Coverage: <a target=\"_blank\" href=\"https:\/\/fortune.com\/2025\/08\/18\/mit-report-95-percent-generative-ai-pilots-at-companies-failing-cfo\/\">Fortune CFO Daily<\/a>.<\/li>\n <li>Gartner, Inc. <a target=\"_blank\" href=\"https:\/\/www.gartner.com\/en\/newsroom\/press-releases\/2025-06-25-gartner-predicts-over-40-percent-of-agentic-ai-projects-will-be-canceled-by-end-of-2027\"><em>Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027.<\/em><\/a> Press release, June 25, 2025. Quoted: Anushree Verma, Senior Director Analyst.<\/li>\n <li>Anthropic Interpretability Team. <a target=\"_blank\" href=\"https:\/\/www.anthropic.com\/research\/emotion-concepts-function\"><em>Emotion concepts and their function in a large language model.<\/em><\/a> Published April 2, 2026.<\/li>\n <li>OpenAI \/ Tomoro \/ OpenAI Deployment Company announcement, May 11, 2026.<\/li>\n <li>Plutchik, R. <em>A General Psychoevolutionary Theory of Emotion<\/em> (1980); <em>The Nature of Emotions,<\/em> American Scientist 89(4), 344\u2013350 (2001).<\/li>\n <\/ol>\n <\/div>\n\n<\/div>\n\n\n<style id=\"wpforms-css-vars-198-block-f85f49dc-55f8-4697-a842-cbc83ede254c\">\n\t\t\t\t#wpforms-198.wpforms-block-f85f49dc-55f8-4697-a842-cbc83ede254c {\n\t\t\t\t--wpforms-field-size-input-height: 43px;\n--wpforms-field-size-input-spacing: 15px;\n--wpforms-field-size-font-size: 16px;\n--wpforms-field-size-line-height: 19px;\n--wpforms-field-size-padding-h: 14px;\n--wpforms-field-size-checkbox-size: 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.wpforms-field-container --><div class=\"wpforms-submit-container\" ><input type=\"hidden\" name=\"wpforms[id]\" value=\"198\"><input type=\"hidden\" name=\"page_title\" value=\"\"><input type=\"hidden\" name=\"page_url\" value=\"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/posts\/2434\"><input type=\"hidden\" name=\"url_referer\" value=\"\"><button type=\"submit\" name=\"wpforms[submit]\" id=\"wpforms-submit-198\" class=\"wpforms-submit\" data-alt-text=\"Sending...\" data-submit-text=\"Submit\" aria-live=\"assertive\" value=\"wpforms-submit\">Submit<\/button><img loading=\"lazy\" decoding=\"async\" src=\"https:\/\/blogs.consentplace.com\/en\/wp-content\/plugins\/wpforms-lite\/assets\/images\/submit-spin.svg\" class=\"wpforms-submit-spinner\" style=\"display: none;\" width=\"26\" height=\"26\" alt=\"Loading\"><\/div><\/form><\/div> <!-- .wpforms-container -->","protected":false},"excerpt":{"rendered":"<p>Why 95% of Enterprise AI Fails \u2014 and the Missing Layer the Other 5% Have \u2013 Official Blog AI Enterprise \u00b7 Emotional Dynamics \u00b7 Research Why 95% of enterprise AI fails \u2014 and the missing layer the other 5% have. Personalization is 30 years old. The Why behind every interaction is not. Neither is the… <a class=\"more-link\" href=\"https:\/\/blogs.consentplace.com\/en\/tech\/95-5-2\/\">Continue reading <span class=\"screen-reader-text\">95% of enterprise AI fails. The 5% that doesn’t has one thing the rest is missing.<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[65,67,64,70,69,21,6],"tags":[7,66,17,18,25],"class_list":["post-2434","post","type-post","status-publish","format-standard","hentry","category-ai-agent","category-business-intelligence","category-consentboxes","category-conversational-intelligence","category-dashboard","category-marketing","category-tech","tag-consent","tag-consentboxes","tag-customer-technology","tag-custtech","tag-explicit-consent","entry"],"_links":{"self":[{"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/posts\/2434","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/comments?post=2434"}],"version-history":[{"count":1,"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/posts\/2434\/revisions"}],"predecessor-version":[{"id":2435,"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/posts\/2434\/revisions\/2435"}],"wp:attachment":[{"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/media?parent=2434"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/categories?post=2434"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blogs.consentplace.com\/en\/wp-json\/wp\/v2\/tags?post=2434"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}