{"id":28701,"date":"2018-05-01T09:23:33","date_gmt":"2018-05-01T16:23:33","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&#038;p=28701"},"modified":"2024-05-06T05:43:55","modified_gmt":"2024-05-06T12:43:55","slug":"artificial-intelligence-in-business","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/artificial-intelligence-in-business","title":{"rendered":"Geography and AI Combine for Business Intelligence"},"author":501,"featured_media":0,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","transcript_file":"","podmotor_file_id":"","podmotor_episode_id":"","castos_file_data":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[1001,791],"tags":[1741,1391,611],"department":[476812,476822],"wherenext-category":[],"industry":[],"class_list":["post-28701","wherenext","type-wherenext","status-publish","format-standard","hentry","category-commercial","category-digital-transformation","tag-ai","tag-artificial-intelligence","tag-business-intelligence","department-business-growth","department-data-and-ai"],"acf":{"short_description":"An executive\u2019s guide to the business power of artificial intelligence (AI)\u2014how to approach it, who should own it, and how to drive value from it.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"A little more than a decade ago, the IT world began to buzz about the next big thing, a concept called service-oriented architecture (SOA). SOA promised a better way to build enterprise applications, delivering efficiency, business agility, and fluid communication\u2014a near revolution in business workflows. Such was its promise that business executives\u2014not just CIOs\u2014began to ask, <em>How do I get an SOA?<\/em>\r\n\r\nIn the fog of excitement, few executives asked the more appropriate question: What exactly is an SOA? Is it an off-the-shelf product, an IT methodology, a business philosophy? And where does it belong in my organization\u2014do I need a strategy to drive business value from it?\r\n\r\nToday, artificial intelligence (AI) triggers similar levels of excitement, with a chaser of fear. In a recent survey by New Vantage Partners, <a href=\"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/business-executives-and-ai\/\">C-level executives crowned AI the most disruptive technology<\/a>\u2014far outranking cloud computing and blockchain. And nearly 80 percent of those executives fear competitors will harness AI to outflank their business."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Left","content":"<h2>An Executive Checklist for AI in the Enterprise<\/h2>\r\n<ul>\r\n \t<li><strong>Create a strategy\u2014<\/strong>AI is already making an impact in the enterprise\u2014via chatbots, virtual assistants, and other point solutions. Experts advise executives to establish a framework for how AI will be incorporated into business strategy and processes, and to define measurable goals.<\/li>\r\n \t<li><strong>Apply executive support\u2014<\/strong>Assign a C-level executive to oversee the company\u2019s strategy. \u201cWhen companies are looking to do fundamental digital transformations and reinvention of the business, there is incredible value in having top-down guidance drive much of that activity,\u201d says Microsoft\u2019s Joseph Sirosh.<\/li>\r\n \t<li><strong>Mind the <\/strong><strong>data\u2014<\/strong>\"Predictions will be accurate only if the training data used to teach the AI prediction model is truly representative of the target cases being classified or predicted,\u201d explains Esri\u2019s Sud Menon. \u201cAI is a data-driven game, hands down.\u201d<\/li>\r\n \t<li><strong>Incorporate robust datasets, including location information\u2014<\/strong>In nearly all its forms, business data can become more valuable when coupled with information about its location. This form of geoenrichment is especially useful for AI models, which can discover insight that humans might overlook. (See \u201cA Business Case\u201d in the article.)<\/li>\r\n<\/ul>","snippet":""},{"acf_fc_layout":"content","content":"That cocktail of enthusiasm and trepidation hasn\u2019t slowed progress: <a href=\"http:\/\/newvantage.com\/app\/uploads\/2018\/01\/Big-Data-Executive-Survey-2018-Findings-1.pdf\">93 percent of C-level executives say their company is investing in AI<\/a>. But for some of them, the concept and practice of AI are as murky as SOA was a decade ago.\r\n\r\nFrom an executive\u2019s perspective, now is the time to answer critical questions: What is AI, what can it do for my business, and who should be responsible for its development and strategic alignment?\r\n\r\n(To see how leading companies are using AI in the enterprise, <a href=\"https:\/\/www.esri.com\/en-us\/artificial-intelligence?adumkts=branding&amp;aduc=advertising&amp;aduSF=WhereNext&amp;utm_Source=advertising&amp;aduca=branding&amp;aduco=ArticleLink&amp;adut=Sirosh_Menon&amp;aducp=InternalPromo&amp;aduat=article&amp;adupt=awareness\">visit this eBook<\/a>.)\r\n\r\n<strong>AI in the Enterprise <\/strong>\r\n\r\nAlthough 93 percent of businesses are investing in artificial intelligence, not all are using it in the same way or toward the same end, says Sud Menon, director of software product development at Esri. \u201cAI is a very broad term, and businesses are adopting different aspects of it at different rates,\u201d Menon notes.\r\n\r\nWhen envisioning how AI can deliver value to their enterprises, business executives should think of three primary processes, according to Menon and Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft: internal business operations, customer interactions, and business planning. Interestingly, a survey by Tata Consultancy Services found that high-performing companies are <a href=\"https:\/\/hbr.org\/2017\/04\/how-companies-are-already-using-ai\">more likely to focus their AI efforts on internal operations<\/a>, while AI followers tend to concentrate on customer interactions.\r\n\r\nRegardless, each process is being transformed with help from cloud computing, data, and intelligent algorithms that power AI. Here are a few examples of how:\r\n<p style=\"padding-left: 90px;\"><strong>Internal Operations\u2014<\/strong>AI is improving companies\u2019 internal operations in several ways. In some workplaces, AI-based facial recognition systems regulate employee access to secure areas. Predictive maintenance systems run by AI help determine the optimal service schedule for fleets of delivery vans. And AI-infused bots are performing HR tasks that once required human intervention, such as guiding employees through the steps of changing their last name, or adjusting the allocation of their 401k plan. The bots connect to systems of record like ERP and HR software, analyze pertinent data, and lead employees through an intuitive workflow.<\/p>\r\n<p style=\"padding-left: 90px;\"><strong>Customer Interactions\u2014<\/strong>AI is adding intelligence to some customer-facing tasks. For example, AI powers many of the recommendation systems that suggest a relevant product or a message to a website visitor who lives in a particular location. It anchors security systems that recognize a fraudster\u2019s voice signature or suspicious online activity in real time and deny the person access to an online account. And it supports the chatbots that interact with millions of consumers online each day.<\/p>\r\n<p style=\"padding-left: 90px;\"><strong>Business Planning\u2014<\/strong>For executives and decision-makers looking for strategic guidance, AI can predict shifts in supply and demand and how businesses might react. To plan next quarter\u2019s operations, the technology can sift through customer purchasing habits and factors such as planned competitor stores to predict sales, product mix, and staffing levels. Business decisions that were once governed primarily by an executive's intuition\u2014like where to invest and when\u2014are now being strengthened by data-driven AI. (See the section titled \u201cA Business Case\u201d for an example.)<\/p>"},{"acf_fc_layout":"quote","image":28661,"text":"You can quantify the benefits of applying an AI decision versus your existing business process and understand upside to revenue, efficiencies, and savings.","author_name":"Joseph Sirosh, Microsoft","author_profession_organization":""},{"acf_fc_layout":"content","content":"<strong>AI Accuracy: Machine Learning Keeps on Learning <\/strong>\r\n\r\nMuch has been made of AI\u2019s abilities\u2014to see, to understand human speech, to predict outcomes. But some wonder whether the technology has evolved enough to form the foundation of business decisions. For instance, a <a href=\"https:\/\/www.wired.com\/story\/ai-has-a-hallucination-problem-thats-proving-tough-to-fix\/?mbid=nl_030918_daily_list_p\">recent <em>WIRED<\/em> story<\/a> reported that an AI-based image detection program was 91 percent sure that a photo of two skiers was a dog. It turns out that like any computer program, AI will need debugging before it is put into production.\r\n\r\nAI systems today are statistical learning systems that drink in data. If the data used to teach AI systems is flawed, either because it\u2019s wrong, statistically unsound, or does not cover the use cases the AI system was designed for, the outcomes can be erroneous.\r\n\r\nAs companies increasingly turn to AI and machine learning to inform business decisions, experts advise a meticulous approach to data. \u201cWhile AI models have increased greatly in sophistication, including the ability to learn from ever larger datasets of known cases, businesses need to understand that the approach is still empirical,\u201d Menon says. \u201cPredictions will be accurate only if the data used to train the prediction model truly represent the target cases being classified or predicted.\u201d"},{"acf_fc_layout":"content","content":"For example, an AI model schooled to predict the health outcomes of a certain diet might overstate results if the data used in training the model is tied to a specific subgroup of the population. In such a case, the model would have no way of taking into account the genetic and lifestyle variations in other groups that could modulate the effect of diet on health, and its results could be flawed if applied broadly.\r\n\r\nThe good news, Sirosh says, is that AI systems can be tested in scientific ways\u2014with new data\u2014and validated. Especially in the case of AI designed for mission-critical operations, it may be important to have controlled statistical testing, similar in spirit to clinical trials in medicine.\r\n\r\n\u201cIt is up to a business to gather the right data for the problem at hand and apply prediction results appropriately depending on the type of problem being solved and the decisions being made,\u201d Menon says. Executive-level support can set these ground rules for AI, helping ensure accurate decision support throughout the enterprise.\r\n\r\nWith the right data, the business case for applying AI widely is growing stronger by the week\u2014across many forms of AI. A Danish company, for example, claims that <a href=\"https:\/\/www.theregister.co.uk\/2017\/05\/24\/aipowered_dynamic_pricing_petrol\/\">the AI behind its pricing technology<\/a> can improve gas stations\u2019 margins by as much as 5 percent. Meanwhile, the insurance company Lemonade recently claimed a world record, saying <a href=\"https:\/\/www.carriermanagement.com\/news\/2017\/01\/23\/163382.htm\">the company\u2019s AI bot settled a client\u2019s claim<\/a> in three seconds (including sending wiring instructions for the payout and notifying the client of the settlement).\r\n\r\nIn all these instances, businesses are either offloading decisions to AI or strengthening them with AI\u2019s help\u2014and creating new experiences for customers, new business models, and new ways of working."},{"acf_fc_layout":"quote","image":28651,"text":"Executives need to understand what AI can bring to business planning. Business planning is becoming ever more data driven and reliant on advanced data science and machine learning.","author_name":"Sud Menon, Esri","author_profession_organization":""},{"acf_fc_layout":"content","content":"<strong>Trend Spotting: Adding Location Data to AI <\/strong>\r\n\r\n\u201cAll this decision-making feeds on data,\u201d Menon says. \u201cThe more data you have that is relevant to the problem, the better the decision-making process is.\u201d\r\n\r\nOne type of data driving AI in new directions is location, Sirosh says. \u201cGeographic information systems [GIS], which can correlate and analyze location in time and space and integrate it with many other types of information\u2014and then serve it up for higher-order AI to be applied on it\u2014are particularly interesting,\u201d he told <em>WhereNext<\/em>."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"<h2>The Pillars of Artificial Intelligence<\/h2>\r\nUnlike technologies that are well known but struggling for widespread business adoption\u2014among them, virtual reality and blockchain\u2014artificial intelligence is already being put to work in organizations worldwide.\r\n\r\nThe coming-out party for AI is due to three factors, according to Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft. The first is the massive compute power now available in the cloud or on premises, which allows data to be processed into insight. The second is the data unleashed by <a href=\"https:\/\/www.esri.com\/en-us\/digital-transformation\/overview\">digital transformation<\/a>, including sensors that relay information via the Internet of Things (IoT), GPS and mobile devices that report accurate locations, and innumerable other sources. Sirosh calls data the oxygen of artificial intelligence.\r\n\r\nThe third pillar of AI is the algorithms that fuel its intelligence. Recent innovations have provided AI with \u201cthe ability for computers to learn from every type of data, make predictions, and act without being programmed explicitly,\u201d Sirosh says.\r\n\r\nTogether, those forces help AI mimic\u2014and in some cases, outperform\u2014humans\u2019 abilities to see, analyze, communicate with, and make predictions about the world around them.","snippet":""},{"acf_fc_layout":"content","content":"\u201cGIS and geography provide organizations with additional contextual information that enriches observations, leading to better predictions,\u201d Menon explains. That might be the quarterly sales at stores in a particular market. Or the rate of home ownership in the area where a bank is considering building a new branch. It could even be data on physical phenomena such as weather, vegetation, or urban density. The more data elements that GIS catalogs, the more oxygen AI has, and the better its predictions will be.\r\n\r\n\u201cMost things are located in the world and related to or influenced by nearby things,\u201d Menon says. That simple statement underscores the value of using <a href=\"https:\/\/www.esri.com\/en-us\/location-intelligence?adumkts=branding&amp;aduc=advertising&amp;aduSF=WhereNext&amp;utm_Source=advertising&amp;aduca=branding&amp;aduco=ArticleLink&amp;adut=artificial_intelligence_in_business&amp;aducp=InternalPromo&amp;aduat=article&amp;adupt=awareness\">location data<\/a> to strengthen AI-based decision making.\r\n\r\n<strong>A Business Case: AI Powered by Location Intelligence <\/strong>\r\n\r\nJust as search engines revolutionized the speed of information discovery and knowledge sharing, AI and location data are accelerating business activities by performing some tasks faster than humans can, with more data. The benefit isn\u2019t simply faster decisions, Sirosh and Menon say. It\u2019s smarter decisions.\r\n\r\nA new breed of AI-based sales analysis is a case in point. A sales executive at a national retailer has identified young parents as a core customer segment and wants to learn more about them. But manually gleaning insight from thousands of customers and hundreds of thousands of transactions is an impossible task. The company turns to a machine learning model in the hope of discovering more insight.\r\n\r\nThe goal is to find patterns in the data that will help the company understand this core customer segment\u2014insight that will improve the company\u2019s marketing messages, store assortments, and the events it sponsors in its communities. The project team tutors an AI model using data from multiple stores, including customer addresses and a record of purchases attributed to each address.\r\n\r\nThe AI model sifts through these records looking for insight. It homes in on diaper purchases as a signal for young parents and discovers a curious correlation: many diaper purchases are accompanied by purchases of pill organizers, denture cream, and senior vitamins.\r\n\r\nTo refine the analysis, the team enriches the AI model with location-based demographic data pulled from GIS. To each customer address, the AI model adds hundreds of data points about the demographic characteristics of the surrounding neighborhood\u2014average household income, family composition, marital status, hobbies, languages spoken, and recreational preferences."},{"acf_fc_layout":"quote","image":28731,"text":"The information that AI needs in order to improve business decisions might be demographic data on where people live, their economic characteristics, their interests and tastes. Or it could be physical geographic data, like weather, temperature, geology, vegetation, and urban characteristics.","author_name":"Sud Menon, Esri","author_profession_organization":""},{"acf_fc_layout":"content","content":"Combing through that location-enriched big data, the AI algorithm reveals something executives hadn\u2019t expected. At many of the company\u2019s stores, young parents from the surrounding area live in multigenerational homes. And, as it turns out, the grandparents are doing most of the shopping.\r\n\r\nThe AI model helped executives adjust plans for marketing, merchandizing, and community outreach before they spent millions targeting the wrong demographic. And it did so by using the three traits that <a href=\"https:\/\/www.pwc.com\/us\/en\/advisory-services\/assets\/ai-predictions-2018-report.pdf\">make AI a valuable tool for augmenting the human workforce<\/a>, according to the consultants at PwC:\r\n<ul>\r\n \t<li>Automating complex business processes<\/li>\r\n \t<li>Spotting patterns in historical data that lead to business value<\/li>\r\n \t<li>Providing insight that strengthens human decisions<\/li>\r\n<\/ul>\r\n<strong>Business Strategy: Who Oversees AI<\/strong><strong>\u2014<\/strong><strong>CXOs or LOB Managers? <\/strong>\r\n\r\nConsidering AI\u2019s expected business impacts and the fact that 93 percent of organizations are already investing in the technology, it\u2019s worth asking where artificial intelligence should live in the organization, and who should be responsible for it. There may be no simple answer, but those with a ringside seat for AI\u2019s emergence have some suggestions.\r\n\r\n\u201cWhen it involves the data that a company uses and the way that decisions are made, AI requires top-down vision and investment,\u201d Menon says."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"<h2>AI Need Not Apply\u2014Business Processes Untouched by AI<\/h2>\r\nDespite the sense that AI is sweeping through every function of business, some remain AI free, according to Joseph Sirosh, corporate vice president of artificial intelligence and research at Microsoft. \u201cFor example, engineering and physics are incredibly well-developed mathematical sciences, and we are going to make tremendous progress in those areas. That will include breakthroughs in quantum computing and other disciplines. Those are all areas that are just core scientific and engineering work. AI doesn't encompass all of that, although it may help amplify some of this work.\u201d","snippet":""},{"acf_fc_layout":"content","content":"Sirosh agrees. \u201cWhere we have found dramatic wins related to AI, the CEO had a vision of how to transform the organization toward creative work and away from old-economy and labor-intensive processes, or to create new customer experiences and business models. That vision was much more cohesive and integrative than what would have bubbled up\u201d from the lines of business, he says.\r\n\r\nUsing AI to move companies away from labor-intensive processes will likely have profound effects on the workforce. McKinsey researchers assert that <a href=\"https:\/\/www.mckinsey.com\/business-functions\/digital-mckinsey\/our-insights\/four-fundamentals-of-workplace-automation\">45 percent of activities in today\u2019s workforce could be automated<\/a>\u2014whether through AI or other means. And when natural-language processing\u2014a form of AI\u2014reaches the median level of human capability, another 13 percent of jobs could be on the block.\r\n\r\nC-level executives will need to find an effective balance. Writing about <a href=\"https:\/\/sloanreview.mit.edu\/article\/five-management-strategies-for-getting-the-most-from-ai\/\">the C-level challenges of AI<\/a>, McKinsey senior partners Jacques Bughin and Eric Hazan note that measurable ROI typically comes only when AI is laced into a business\u2019s culture and workflows. That in itself is a sizable feat, the partners say, possible only with the guidance of company leaders.\r\n\r\n\u201cWhen companies are looking to do fundamental digital transformations and reinvention of the business,\u201d Sirosh says, \u201cthere is incredible value in having top-down guidance drive much of that activity.\u201d\r\n\r\nWorkforce shifts and workflow transformation aside, Sirosh and Menon advise concerned executives to focus on the foundation of AI. The goal of such a sophisticated technology, they say, is rather simplistic.\r\n\r\n\u201cAI, informed by location data, helps organizations reason and interact with the increasingly sophisticated world around us,\u201d Sirosh says.\r\n\r\n\u201cIf I had to put it in one term,\u201d Menon adds, \u201cAI is basically about decision-making\u2014smarter decision making.\u201d\r\n\r\n&nbsp;\r\n\r\n(Listen to a podcast featuring Joseph Sirosh to explore this concept in more depth, including a look at\u00a0<a href=\"https:\/\/www.esri.com\/about\/newsroom\/podcast\/ai-and-location-will-drive-tomorrows-digital-transformations\/\">how AI is changing business models<\/a>.)"}],"references":null},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Artificial Intelligence, Geography New BI\u2502An Executive Primer<\/title>\n<meta name=\"description\" content=\"Artificial intelligence is already at work in businesses worldwide. 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