{"id":2324,"date":"2020-03-18T04:35:59","date_gmt":"2020-03-18T01:35:59","guid":{"rendered":"http:\/\/kusuaks7\/?p=1929"},"modified":"2023-12-27T12:27:07","modified_gmt":"2023-12-27T12:27:07","slug":"ai-in-business-the-transfer-of-expertise-from-internet-companies-to-the-enterprise","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/ai-in-business-the-transfer-of-expertise-from-internet-companies-to-the-enterprise\/","title":{"rendered":"AI In Business: The Transfer Of Expertise From Internet Companies To The Enterprise"},"content":{"rendered":"\t\t
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Takeaway:<\/strong>\u00a0The enterprise has started to integrate AI and ML into its operations, but not nearly to the extent that many internet businesses have. Help from these companies could be the key to enterprise AI adoption.<\/i><\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tHyperscale<\/a>\u00a0internet companies have leapfrogged several levels of\u00a0machine learning<\/a>\u00a0with increasing\u00a0automation<\/a>\u00a0in data processing and modeling sophistication since 2015. The enterprise, with a few exceptions, has been lagging in adoption of\u00a0artificial intelligence<\/a>\u00a0but sees, in internet companies, partners who can help it to catch up.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThe prospective enterprise users of machine learning have a long way to go to match the talent pools, computing prowess, scale, and the data volumes for training\u00a0algorithms<\/a>\u00a0that internet companies have accumulated, especially over the last four years. In many verticals of the enterprise, the\u00a0business processes<\/a>\u00a0have not been\u00a0digitally transformed<\/a>\u00a0for the automation of data processing and the instant execution of business decisions based on insights gained from artificial intelligence. Moreover, several of the verticals do not yet have well-defined use cases that lend themselves to the profitable execution of artificial intelligence. (For more on AI in business, see\u00a0Overcoming IT Service Management Change Management Woes With the Power of AI<\/a>.)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Adoption of Artificial Intelligence in Business<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAdoption of\u00a0artificial intelligence<\/a>\u00a0in business is at an early stage, especially when we consider its sophisticated users who have gone beyond exploration and pilots to a stage where they gain business value from its usage. O\u2019Reilly, a technology media company, found in its 2018 survey,\u00a0\u201cThe State of Machine Learning Adoption in the Enterprise,\u201d<\/a>\u00a0that sophisticated users were only 15% of the total enterprise users worldwide and 18% in North America.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nExternal sources of expertise and learning play a significant role in aiding business users to catch up with the state-of-art in machine learning, especially for advanced AI techniques. A 2018 survey by\u00a0Deloitte<\/a>\u00a0found 59% of the enterprise buyers acquire AI expertise from\u00a0enterprise software<\/a>\u00a0companies with AI capabilities, 53% co-develop it with partners, 49% acquire it from\u00a0cloud<\/a>\u00a0AI companies, and 39% crowdsource it from sites like GitHub. Cloud AI companies provide AI as a service, which saves on the cost of infrastructure and talent development on-premise.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tFor advanced AI development, cloud companies are a more important source of expertise. Thirty-nine percent of the business respondents showed a preference for cloud companies as a source of advanced AI compared to 15% for on-premise software. AI as a service has grown at a brisk rate of 48%.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Adoption of Artificial Intelligence in Verticals<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWe spoke to Aditya Kaul, research director at\u00a0Tractica<\/a>, an industry analyst firm focused on artificial intelligence and\u00a0robotics<\/a>. Kaul has been investigating the adoption of\u00a0artificial intelligence<\/a>\u00a0in 30 verticals for over 300 use cases in businesses across the world. \u201cTelecommunications<\/a>\u00a0and financial services have been the leaders in AI adoption, and they started early with more rudimentary statistical techniques going back as far back as the 1980s,\u201d Kaul told us. \u201cAdoption in retail, automotive and healthcare has surged in more recent times while the majority of the enterprise remains at an early stage of adoption,\u201d he added, \u201cHorizontal business services such as\u00a0CRM<\/a>, supply chain, and HR have expanded the adoption of AI rapidly as its predictive capabilities<\/a> help in identifying prospects, consumer demand trends, and talented employees.\u201d\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\n\u201cMonitoring, synchronization, and optimization of complex and heterogeneous\u00a0software-defined networks<\/a>\u00a0is a critical use case in the telecom sector,\u201d Kaul surmised. \u201cVoice-assistants in cars have surged in the automotive sector with an increasing accent on the in-car personalization of services,\u201d he noted. He also informed us that \u201cThe banking sector is deploying artificial intelligence for customer service including\u00a0chatbots<\/a>\u00a0as they face intense competition from smaller internet banks, apart from using it for fraud detection, loan analysis, and other backend operations.\u201d\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhile the healthcare sector has enormous potential, it had lagged until recently due to regulatory barriers to using its data. \u201cSeveral venture-backed start-ups have now focused on machine learning in clinical trials to speed up drug discovery,\u201d Kaul revealed.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tRetail stores have accelerated investments in machine learning as they achieve mastery in predicting demand and supply accurately. German retailer Otto cut returns by more than 2 million items a year and excess stock by 20% using\u00a0deep learning<\/a>\u00a0algorithms to predict what customers will buy, according to a research report by\u00a0McKinsey<\/a>. Its AI engine now autonomously orders 200,000 items a month because it can forecast what Otto will sell over the next 30 days with 90% accuracy. (Not sure how AI would fit in with your company? Check out\u00a05 Ways Companies May Want to Consider Using AI<\/a>.)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Partnership with Cloud AI Companies<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tHyperscale cloud AI companies have been willing to partner with enterprise customers to advance their artificial intelligence skills, but they are uncertain about the ways to collaborate with enterprise software companies who are indispensable for backend plumbing. \u201cCloud companies have been generous to enterprise customers with their freebies including free cloud time, consulting, and training resources,\u201d Kaul observed.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSince cloud AI companies like Google have made a quick transition from hand-engineered algorithms in 2015 to deep learning in 2016 and lately more advanced algorithms like\u00a0reinforcement learning<\/a>, they are able to counsel early adopters on how to make progress in their journey to AI learning maturity.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\u201cThe costs of AI are also dropping as we see increased availability of pre-trained models, labeled datasets and a general reduction in cloud AI pricing,\u201d Kaul explained. \u201cConcurrently, the time for data processing, ingestion, data preparation, and labeling, which accounts for 90% of the effort, has been shortened with techniques like\u00a0AutoML<\/a>\u00a0which automates these processes,\u201d he added. Nvidia, a partner of hyperscale cloud AI companies, has repackaged its\u00a0GPUs<\/a>\u00a0(graphical processing units) for the enterprise. \u201cNvidia has repositioned to target\u00a0data science<\/a>\u00a0and\u00a0analytics<\/a>\u00a0use cases in the enterprise which speeds up the training of large analytical models compared to\u00a0CPUs<\/a>\u00a0(central processing units),\u201d Kaul explained.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tEnterprise software companies will have to find a way to accommodate cloud AI companies, especially as they bring new capabilities to the market which become a part of the fabric of enterprise business. \u201cFunctions like chatbots and\u00a0computer vision<\/a>\u00a0capabilities for\u00a0image recognition<\/a>\u00a0are enabled by deep learning which extends the value that AI brings,\u201d Kaul asserted. \u201cSoftware itself is not hardcoded anymore but adapts to needs of data and analytics,\u201d he added. There is, as yet, insufficient evidence to show that enterprise software companies, with a few exceptions like Microsoft, can catch up with cloud AI companies in algorithms. By all indications, the new terms of engagement between cloud AI companies and enterprise software companies, however, have not been resolved yet.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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Conclusion<\/h2><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tMachine learning<\/a>\u00a0will reinvent the enterprise as it redefines enterprise software itself. The enterprise will adapt faster to the external business environment with the automation of data processing and faster execution of business decisions based on insights gained from algorithms that shorten the time to learn from data. Enterprise software will evolve and reconfigure more often to keep pace with algorithms.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\t\nThis article appeared in\u00a0Techopedia<\/a>.\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":"

Machine learning will reinvent the enterprise as it redefines enterprise software itself. The enterprise will adapt faster to the external business environment with the automation of data processing and faster execution of business decisions based on insights gained from algorithms that shorten the time to learn from data. The enterprise has started to integrate AI and ML into its operations, but not nearly to the extent that many internet businesses have. Help from these companies could be the key to enterprise AI adoption.<\/p>\n","protected":false},"author":748,"featured_media":4007,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[3597],"class_list":["post-2324","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":3597,"user_id":748,"is_guest":0,"slug":"kishore-jethanandani","display_name":"Kishore Jethanandani","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Jethanandani","first_name":"Kishore","job_title":"","description":"Kishore Jethanandani is Chief Futurist and Editor at the FuturistLens Magazine."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2324","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\/748"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=2324"}],"version-history":[{"count":7,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2324\/revisions"}],"predecessor-version":[{"id":35218,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2324\/revisions\/35218"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/4007"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=2324"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=2324"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=2324"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=2324"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}