{"id":1891,"date":"2019-08-19T03:50:56","date_gmt":"2019-08-19T03:50:56","guid":{"rendered":"http:\/\/kusuaks7\/?p=1496"},"modified":"2024-04-30T10:35:35","modified_gmt":"2024-04-30T10:35:35","slug":"the-ai-roles-some-companies-forget-to-fill","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/the-ai-roles-some-companies-forget-to-fill\/","title":{"rendered":"The AI Roles Some Companies Forget to Fill"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"1891\" class=\"elementor elementor-1891\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t<section class=\"has_eae_slider elementor-section elementor-top-section elementor-element elementor-element-6922c6e1 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6922c6e1\" data-element_type=\"section\" data-e-type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"has_eae_slider elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-1f1a2f38\" data-id=\"1f1a2f38\" data-element_type=\"column\" data-e-type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-75f5caac elementor-widget elementor-widget-text-editor\" data-id=\"75f5caac\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAI is almost everywhere in the news today, and the drive to create and implement AI solutions is creating an enormous talent gap.\u00a0 An\u00a0<a href=\"https:\/\/www.multivu.com\/players\/English\/8075951-teradata-state-of-artificial-intelligence-ai-for-enterprises\/\" rel=\"noopener\">estimated 80%<\/a>\u00a0of companies are already investing in AI and most are facing challenges hiring the capabilities they need to implement a useful AI application or product.\u00a0 It\u2019s clear that there is an\u00a0<a href=\"https:\/\/www.nytimes.com\/2017\/10\/22\/technology\/artificial-intelligence-experts-salaries.html\" class=\"broken_link\" rel=\"noopener\">intensively competitive market<\/a>\u00a0for artificial intelligence and machine learning specialists.\u00a0 Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and \u201cfeature engineering.\u201d\u00a0<a href=\"http:\/\/www.jfgagne.ai\/talent\" rel=\"noopener\">Some analysts<\/a>\u00a0have even equated \u201cAI talent\u201d with such researchers.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b54fc8 elementor-widget elementor-widget-image\" data-id=\"7b54fc8\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/hbr.org\/resources\/images\/article_assets\/2019\/03\/Mar19_14_965514096.jpg\" alt=\"\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bcccd8c elementor-widget elementor-widget-text-editor\" data-id=\"bcccd8c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p style=\"text-align: center;\"><span style=\"font-size: 11px;\">HIROSHI WATANABE\/GETTY IMAGES<\/span><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4e5212f elementor-widget elementor-widget-text-editor\" data-id=\"4e5212f\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHowever, AI talent goes far beyond machine learning Ph.D\u2019s. \u00a0Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6920753 elementor-widget elementor-widget-heading\" data-id=\"6920753\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h3><strong>The AI Engineer Role<\/strong><\/h3><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f80865b elementor-widget elementor-widget-text-editor\" data-id=\"f80865b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tBecause some others have already realized their importance, let\u2019s focus first on engineering skills. A\u00a0<a href=\"https:\/\/towardsdatascience.com\/dont-make-this-big-machine-learning-mistake-research-vs-application-bd52d5a9a8b9\" class=\"broken_link\" rel=\"noopener\">very useful article<\/a>\u00a0recently pointed out the difference between machine learning researchers and machine learning engineers.\u00a0 The key takeaway is that most companies need engineers to help develop products and production applications, rather than a researcher to help push the boundaries of AI technique and technology.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f9a0bf elementor-widget elementor-widget-text-editor\" data-id=\"6f9a0bf\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\nThese engineering skills include creating technology architectures that scale, writing and deploying bulletproof software, and integrating AI capabilities with existing systems. The people in AI engineering roles need to know something about AI, but just as much about programming, computing, and corporate IT environments. Such skills are becoming increasingly important over time as AI knowledge and tools mature, and as algorithms and techniques become commoditized.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cbfbc2b elementor-widget elementor-widget-heading\" data-id=\"cbfbc2b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\"><h3><strong>The AI Data Czar Role<\/strong><\/h3><\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a713c60 elementor-widget elementor-widget-text-editor\" data-id=\"a713c60\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAI initiatives also need data experts. We\u2019ve also argued\u00a0<a href=\"https:\/\/sloanreview.mit.edu\/article\/the-machine-learning-race-is-really-a-data-race\/\" rel=\"noopener\">elsewhere<\/a>\u00a0that the machine learning race is increasingly a data race in which unique data, rather than cutting-edge modeling, is what creates a valuable AI solution.\u00a0 Unfortunately, sourcing and managing data is a skill set that does not often overlap with algorithm development. The AI data czar is typically a position that is created over time through experience, rather than hired out of school, although education in computer science or statistics can be very helpful. The role encompasses such capabilities as:\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-344648b elementor-widget elementor-widget-text-editor\" data-id=\"344648b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<ul>\n \t<li>Knowing what data sources are useful to address an AI question or problem;<\/li>\n \t<li>Being aware of how data is used in algorithms;<\/li>\n \t<li>Assessing data quality;<\/li>\n \t<li>Cleaning and treating data;<\/li>\n \t<li>Having a focus on detail (and being a stickler for data quality);<\/li>\n \t<li>Possessing the strength to push back at technical teams;<\/li>\n \t<li>Knowing the typical ways to transform data.<\/li>\n<\/ul>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-10c4ed0 elementor-widget elementor-widget-text-editor\" data-id=\"10c4ed0\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tData management also requires business knowledge.\u00a0 Let\u2019s discuss a simple example.\u00a0 Our\u00a0<a href=\"https:\/\/aimatters.net\/\" rel=\"noopener\">startup<\/a>\u00a0uses machine learning to bring\u00a0<a href=\"https:\/\/hbr.org\/2018\/01\/robo-advisers-are-coming-to-consulting-and-corporate-strategy\" rel=\"noopener\">automation to strategy consulting<\/a>, and one of the key inputs we use is the financial information included in annual reports.\u00a0 This data is inherently full of gaps.\u00a0 Not every company reports the same set of metrics, and the reason for failing to report is most often that there is nothing to report in the category.\u00a0 For example, many companies do not bother reporting their research and development spending because they have none!\u00a0 This means that the best course of action for filling most of these gaps (which must be filled for the algorithms to work their magic) is to fill them with zeros, representing no spending.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4d19c6d elementor-widget elementor-widget-text-editor\" data-id=\"4d19c6d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHowever, in the world of data science and machine learning, zero-filling data is extremely uncommon and filling with the median value is a generally accepted best practice.\u00a0 Our application is the rare case where median filling actually introduces errors into the data set\u2014for example by assigning an average amount of research and development investment to every company, when 70% of the market actually spends nothing on R&amp;D.\u00a0 If we had handed off data management to the tech team, as many companies do, we would have headed down the wrong path.\u00a0 Instead, by having an informed business team deeply involved in the AI development process, we are able to catch potential problems.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-05a96cb elementor-widget elementor-widget-heading\" data-id=\"05a96cb\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><h3><strong>The Business Leader and AI Translator Roles<\/strong><\/h3>\n<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-9c2475a elementor-widget elementor-widget-text-editor\" data-id=\"9c2475a\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tAI groups also need a role at the intersection of business strategy and AI methods. Such a person, usually a somewhat senior executive, is able to translate strategic objectives and business models into the types of AI that can advance them. Unfortunately, the role of a business leader with some understanding of AI techniques is rarely discussed, and even less often filled.\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b79bcf2 elementor-widget elementor-widget-text-editor\" data-id=\"b79bcf2\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe result is that AI is often used to create either\u00a0<a href=\"https:\/\/www.healthnewsreview.org\/2017\/02\/md-anderson-cancer-centers-ibm-watson-project-fails-journalism-related\/\" class=\"broken_link\" rel=\"noopener\">off target or sunk cost projects<\/a>\u00a0where the technology investment does not yield the ROI anticipated by the board or the leadership team.\u00a0 Our experience working with boards and leaders is that creating a solid AI product that provides either customer, employee, operational or investor value is about 40% problem and product definition, 40% data sourcing, cleaning, filling, and merging, and only 20% algorithm development.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5efaae3 elementor-widget elementor-widget-text-editor\" data-id=\"5efaae3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tSolving these problems requires ongoing partnership between business and technology.\u00a0 Yet most companies do not have a clear point of view about how AI can help organizations make better, more informed and faster decisions, or smarter products and services.\u00a0 Automating parts of decision-making and product development requires a person that can work at the intersection of strategy, business models, code development, algorithm creation, and product development\u2014a rare breed.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-401d815 elementor-widget elementor-widget-text-editor\" data-id=\"401d815\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHaving someone in this role even pays dividends when it comes to algorithm development.\u00a0 From rules-based systems to logistic regression to neural networks and beyond, the algorithms that are used in AI each have different characteristics, good and bad.\u00a0 Although we wouldn\u2019t expect all business leaders to know these, we would expect a good AI business leader\/translator to engage with developers on these pros and cons to help drive the right decision.\u00a0 For example, neutral networks, though powerful, lack explainability.\u00a0 It is difficult to say exactly why the model returns the results that it does.\u00a0 For many products, a \u201cblack box\u201d solution won\u2019t do\u2014the users want or need to understand how it works.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-872dcf3 elementor-widget elementor-widget-text-editor\" data-id=\"872dcf3\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tMany companies rush into the AI race without clear objectives, hope a brilliant AI researcher and a technology team can create something great without guidance, and end up with little to show for it.\u00a0 Recruiting an AI quarterback to provide the business input, and ensuring success with well-defined metrics, is the most important job that most companies miss entirely.\u00a0<a href=\"https:\/\/www.mckinsey.com\/business-functions\/mckinsey-analytics\/our-insights\/analytics-translator\" class=\"broken_link\" rel=\"noopener\">Some have argued<\/a>\u00a0for the importance of the translator function for traditional business analytics, but given the complexity of AI it is even more important with that set of technologies. Indeed, many large AI groups will need multiple people to play the translator role.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-240919c elementor-widget elementor-widget-text-editor\" data-id=\"240919c\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tThe businessperson who fills this role does not need to become a programmer, know the best AI tools from vendors, or delve into the nuances of neural networks versus logistic regression.\u00a0 He or she does, however, need to understand the basics of how different types of AI work and the data sets that will be deployed with them., Such individuals should also have a desire to get deeply involved and work iteratively with the AI team rather than throwing requirements \u201cover the wall,\u201d leaving the machine learning team with the tough decisions.\u00a0 In addition, they need to create a clear economic use case and product road map that produces value for customers, employees, partners or investors. In most cases, these individuals should lead the AI group, and the researchers, engineers, and data czar should report to them.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ca48a4d elementor-widget elementor-widget-text-editor\" data-id=\"ca48a4d\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\tHaving someone on board that who is in or reports directly to the C-suite with an understanding of these topics, and who can oversee the other important AI roles we have discussed, will help the organization achieve its core objective\u2014value for stakeholders\u2014while avoiding the costly, unproductive cycles we often see in poorly managed AI development.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-aafcc88 elementor-widget elementor-widget-text-editor\" data-id=\"aafcc88\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-size: 11px;\"><em><span style=\"font-family: courier new, courier, monospace;\">Co-Authors:<\/span><\/em><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-023a6ec elementor-widget elementor-widget-text-editor\" data-id=\"023a6ec\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<span style=\"font-size: 11px;\"><em><a href=\"https:\/\/hbr.org\/search?term=megan%20beck&amp;search_type=search-all\" rel=\"noopener\">Megan Beck<\/a>\u00a0is Chief Product and Insights Officer at OpenMatters, a machine learning startup, and a digital researcher at the SEI Center at Wharton.\u00a0She is the coauthor of\u00a0<a href=\"http:\/\/www.amazon.com\/Network-Imperative-Survive-Digital-Business\/dp\/1633692051?\" rel=\"noopener\">The Network Imperative: How to Survive and Grow in the Age of Digital Business Models<\/a>.<\/em><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-91a6447 elementor-widget elementor-widget-text-editor\" data-id=\"91a6447\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<span style=\"font-size: 11px;\"><em><a href=\"https:\/\/hbr.org\/search?term=thomas%20h.%20davenport&amp;search_type=search-all\" rel=\"noopener\">Thomas H. Davenport<\/a>\u00a0is the President\u2019s Distinguished Professor in Management and Information Technology at Babson College, a research fellow at the MIT Initiative on the Digital Economy, and a senior adviser at Deloitte Analytics. He is the author of over a dozen management books, most recently\u00a0<a href=\"https:\/\/www.amazon.com\/Only-Humans-Need-Apply-Machines\/dp\/0062438611\" rel=\"noopener\">Only Humans Need Apply: Winners and Losers in the Age of Smart Machines\u00a0<\/a>and\u00a0<a href=\"https:\/\/www.amazon.com\/Advantage-Artificial-Intelligence-Revolution-Management\/dp\/0262039176\/ref=sr_1_1_sspa?s=books&amp;ie=UTF8&amp;qid=1544458197&amp;sr=1-1-spons&amp;keywords=the+ai+advantage&amp;psc=1\" rel=\"noopener\">The AI Advantage.<\/a><\/em><\/span>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>There is an&nbsp;intensively competitive market for artificial intelligence and machine learning specialists.&nbsp; Many companies first attempt to hire Ph.D.-level data scientists with expertise in AI algorithms and feature engineering.&nbsp;Some analysts&nbsp;have even equated &ldquo;AI talent&rdquo; with such researchers. However, AI talent goes far beyond machine learning Ph.D&rsquo;s. &nbsp;Equally important and less understood are the set of talent issues emerging around AI product development and engineering. Most firms have not filled these roles, and their AI projects are suffering as a result.<\/p>\n","protected":false},"author":69,"featured_media":3654,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[1627],"class_list":["post-1891","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":1627,"user_id":69,"is_guest":0,"slug":"barry-libert","display_name":"Barry Libert","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Libert","first_name":"Barry","job_title":"","description":"Barry Libert is the co-founder of AIMatters, an AI startup serving the C-Suite with the first AI powered strategy platform. &nbsp;He serves on a number of boards and advises CEO of startups and large enterprises on becoming AI-first companies. He also co-wrote The Network Imperative: How to Survive and Grow in the Age of Digital Business Models published by HBR.&nbsp;"}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1891","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/69"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1891"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1891\/revisions"}],"predecessor-version":[{"id":36805,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1891\/revisions\/36805"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3654"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1891"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1891"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1891"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1891"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}