{"id":2009,"date":"2019-10-15T05:18:33","date_gmt":"2019-10-15T05:18:33","guid":{"rendered":"http:\/\/kusuaks7\/?p=1614"},"modified":"2024-03-12T13:21:43","modified_gmt":"2024-03-12T13:21:43","slug":"how-to-support-the-widespread-adoption-of-ai","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/ai-ml\/how-to-support-the-widespread-adoption-of-ai\/","title":{"rendered":"How to Support the Widespread Adoption of AI"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\t\u201cArtificial intelligence is reshaping business – though not at the blistering pace many assume,\u201d notes\u00a0Building the AI-Powered Organization<\/a>, an article in\u00a0Harvard Business Review<\/em>\u00a0by McKinsey partners\u00a0Tim Fountaine<\/a>,\u00a0Brian McCarthy<\/a>, and\u00a0Tamim Saleh<\/a>\u00a0in its opening sentence.\u00a0\u00a0A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years.\u00a0\u00a0Two recent reports by\u00a0McKinsey<\/a>\u00a0and\u00a0PwC<\/a>\u00a0respectively\u00a0concluded<\/a>\u00a0that AI has the potential to incrementally increase global economic output between now and 2030 by an additional $13 to $15 trillion.\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\u201cYet, despite the promise of AI, many organizations\u2019 efforts with it are falling short,\u201d adds the\u00a0HBR<\/em>\u00a0article.\u00a0\u00a0Surveys with thousands of executives, showed that most firms are only using AI in ad hoc pilots or applying it to a single business process.\u00a0\u00a0Only 8% of firms are engaged in practices that support widespread adoption.\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\tWhy the slow progress?\u00a0 \u201cAt the highest level, it\u2019s a reflection of a failure to rewire the organization.\u00a0 In our surveys and our work with hundreds of clients, we\u2019ve seen that AI initiatives face formidable cultural and organizational barriers\u2026\u00a0\u00a0at most businesses that aren\u2019t born digital, traditional mindsets and ways of working run counter to those needed for AI.\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\tTo support the widespread adoption of AI, companies must make three fundamental shifts.\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\tFrom siloed work to interdisciplinary collaboration<\/em>.\u00a0\u00a0As\u00a0Yogi Berra<\/a>\u00a0might have said, this first shift feels like\u00a0d\u00e9j\u00e0 vu<\/em><\/a>\u00a0all over again,\u00a0<\/em>as IT had to go through something similar over its first few decades.\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\tCompanies first embraced IT in the 1960s and 1970s to automate their existing back-end processes in applications like inventory management, financial transactions and airline reservations.\u00a0\u00a0The advent of PCs in the 1980s then made it possible to apply IT to front-office processes and applications, such as word processing in office systems, spreadsheets in data analysis, and customer support.\u00a0\u00a0The use of IT grew rapidly with the automation of an increasing number of individual processes, while the underlying structure of organizations remained in place.\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\tBut during this period of rapid IT growth, US\u00a0labor productivity<\/a>\u00a0grew<\/a>\u00a0at only 1.5% between 1973 and 1995.\u00a0 \u201cYou can see the computer age everywhere but in the productivity statistics,\u201d said MIT Nobel Prize laureate economist\u00a0Robert Solow<\/a>\u00a0in 1987, in what\u2019s become known as the\u00a0Solow productivity paradox<\/em><\/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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\n\t\t\t\t\t\t\t\t\tIt wasn\u2019t until the 1990s, with the emergence of\u00a0business process reengineering<\/a>\u00a0and of sophisticated\u00a0business management<\/a>\u00a0applications like\u00a0enterprise resource planning<\/a>\u00a0(ERP), that companies realized that using IT to automate existing processes wasn\u2019t enough.\u00a0 Rather, it was time for organizations to\u00a0fundamentally rethink<\/a>\u00a0their operations, redesign the flow of work in their companies, and integrate the various processes and functions in the enterprise.\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\tIn their 2009 book\u00a0Wired for Innovation: How Information Technology is Rewiring the Economy<\/a>,\u00a0Erik Brynjolfsson<\/a>\u00a0and\u00a0Adam Saunders<\/a>\u00a0introduced the concept of\u00a0organizational capital\u00a0<\/em>as the necessary critical ingredient that enabled a company to take full advantage of major IT advances.\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 companies with the highest returns on their technology investments did more than just buy technology; they invested in organizational capital to become digital organizations.\u00a0 Productivity studies at both the firm level and the establishment (or plant) level during the period 1995-2008 reveal that the firms that saw high returns on their technology investments were the same firms that adopted certain productivity-enhancing business practices.\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\tSimilarly, says the\u00a0HBR<\/em>\u00a0article, \u201cAI has the biggest impact when it\u2019s developed by cross-functional teams with a mix of skills and perspectives.\u00a0\u00a0Having business and operational people work side by side with analytics experts will ensure that initiatives address broad organizational priorities, not just isolated business issues.\u00a0\u00a0Diverse teams can also think through the operational changes new applications may require – they\u2019re likelier to recognize, say, that the introduction of an algorithm that predicts maintenance needs should be accompanied by an overhaul of maintenance workflows.\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\tFrom experience-based, leader-driven decision making to data-driven decision making at the front line.\u00a0\u00a0<\/em>Will there be enough work in the future?\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\tOver the past few years, a number of\u00a0papers<\/a>,\u00a0reports<\/a>\u00a0and\u00a0books<\/a>\u00a0have addressed this very important question.\u00a0\u00a0They generally conclude that AI will have a major impact on jobs and the very nature of work.\u00a0 For the most part,\u00a0 they view AI as mostly augmenting rather than replacing human capabilities, automating the more routine parts of a job and increasing the productivity and quality of workers, so they can focus on those aspect of the job that most require human attention.\u00a0\u00a0Overall<\/a>, few jobs will be entirely automated, but automation will likely transform the vast majority of occupations.\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\tTo many, AI feels different from previous technologies, as it forces us to explore\u00a0the very boundaries<\/a>\u00a0between machines and human.\u00a0\u00a0Learning to create a new smart workplace, where humans and machine collaborate to attain major increases in business performance will take time and considerable reskilling.\u00a0\u00a0In such a\u00a0hybrid space<\/a>\u00a0of human-machines activities, humans work closely with their smart machines, each doing what they do best.\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\u201cWhen AI is adopted broadly, employees up and down the hierarchy will augment their own judgment and intuition with algorithms\u2019 recommendations to arrive at better answers than either humans or machines could reach on their own.\u00a0\u00a0But for this approach to work, people at all levels have to trust the algorithms\u2019 suggestions and feel empowered to make decisions – and that means abandoning the traditional top-down approach.\u00a0\u00a0If employees have to consult a higher-up before taking action, that will inhibit the use of AI.\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\tDespite the recent\u00a0hype<\/a>, we\u2019re still in the early stages of AI\u2019s deployment.\u00a0\u00a0It\u2019s only been in the last few years that complementary innovations like machine learning have taken AI from the lab\u00a0to early adopters in the marketplace<\/a>.\u00a0\u00a0Beyond the relatively small number of early adopters, AI is still in\u00a0the experimental stage<\/a>.\u00a0\u00a0To achieve a wider deployment, considerable experimentation is still required with a variety of pilots, use cases and marketplace applications.\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\u201cOrganizations must shed the mindset that an idea needs to be fully baked or a business tool must have every bell and whistle before it\u2019s deployed\u2026\u00a0\u00a0Such fundamental shifts don\u2019t come easily. They require leaders to prepare, motivate, and equip the workforce to make a change.\u00a0 But leaders must first be prepared themselves. We\u2019ve seen failure after failure caused by the lack of a foundational understanding of AI\u00a0among senior executives<\/a>.”\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\u201cThe ways AI can be used to augment decision making keep expanding,\u201d write the authors in conclusion. \u201cNew applications will create fundamental and sometimes difficult changes in workflows, roles, and culture, which leaders will need to shepherd their organizations through carefully.\u00a0\u00a0Companies that excel at implementing AI throughout the organization will find themselves at a great advantage in a world where humans and machines working together outperform either humans or machines working on their own.\u201d\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":"

\u201cArtificial intelligence is reshaping business – though not at the blistering pace many assume,\u201d notes\u00a0Building the AI-Powered Organization, an article in\u00a0Harvard Business Review\u00a0by McKinsey partners\u00a0Tim Fountaine,\u00a0Brian McCarthy, and\u00a0Tamim Saleh\u00a0in its opening sentence.\u00a0\u00a0A number of studies agree that AI is the biggest commercial opportunity for companies and industries over the next 10-15 years.\u00a0\u00a0Two recent reports by\u00a0McKinsey\u00a0and\u00a0PwC\u00a0respectively\u00a0concluded\u00a0that<\/p>\n","protected":false},"author":612,"featured_media":4229,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[183],"tags":[97],"ppma_author":[3320],"class_list":["post-2009","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ml","tag-artificial-intelligence"],"authors":[{"term_id":3320,"user_id":612,"is_guest":0,"slug":"irving-wladawsky-berger","display_name":"Irving Wladawsky-Berger","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Wladawsky-Berger","first_name":"Irving","job_title":"","description":"Irving Wladawsky-Berger, a Guest Columnist at WSJ CIO Journal, is Research Affiliate at MIT Sloan School of Management, Adjunct Professor at Imperial College, London, and Chairman Advisory Board at r4 Technologies."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2009","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\/612"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=2009"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2009\/revisions"}],"predecessor-version":[{"id":36380,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/2009\/revisions\/36380"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/4229"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=2009"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=2009"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=2009"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=2009"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}