{"id":330971,"date":"2020-04-14T06:29:16","date_gmt":"2020-04-14T13:29:16","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&p=330971"},"modified":"2024-05-10T05:52:32","modified_gmt":"2024-05-10T12:52:32","slug":"artificial-intelligence-and-location-intelligence-for-business","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/artificial-intelligence-and-location-intelligence-for-business","title":{"rendered":"Artificial Intelligence and Location Intelligence: A Business Guide"},"author":501,"featured_media":0,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","castos_file_data":"","podmotor_file_id":"","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":[369852,369842,381312],"tags":[1741,441081,281,1711],"department":[476822,488812],"wherenext-category":[],"industry":[],"class_list":["post-330971","wherenext","type-wherenext","status-publish","format-standard","hentry","category-artificial-intelligence--ai","category-big-data-advanced-analytics","category-cxo-priorities","tag-ai","tag-geoai","tag-location-intelligence","tag-machine-learning","department-data-and-ai","department-emerging-technologies"],"acf":{"short_description":"For business executives intrigued by AI but confused about its risks and rewards, a primer on location intelligence offers critical context.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"To guide their companies through economic shifts, senior executives must understand how technology supports business strategy. Today, that awareness includes artificial intelligence (AI)<\/a>. But without business context, AI will deliver limited value."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"Article snapshot:<\/strong> Business executives, pressed to deliver on strategic goals like market growth and risk mitigation, find important context in combining artificial intelligence with location intelligence technology.","snippet":""},{"acf_fc_layout":"content","content":"Forward-thinking executives are discovering that a complementary technology can contextualize AI information and help them discover new markets, better understand and serve customers, improve asset management, optimize their supply chains, and mitigate risk.\r\n\r\nMost C-suite executives sense that AI is a crucial aspect of digital transformation<\/a>. Three out of four business leaders say they are working to integrate company-wide AI initiatives<\/a>, according to an Accenture survey. The same percentage believe that if they fail to do so, their companies will be defunct in five years."},{"acf_fc_layout":"quote","image":330991,"text":"The next half-decade will be crucial. McKinsey research suggests that the AI adoption rate among corporations will rise steadily during this period\u2014then increase significantly.","author_name":"","author_profession_organization":""},{"acf_fc_layout":"content","content":"

The Business of Machine Learning<\/strong><\/h3>\r\nIn a business context, AI often refers to machine learning. By studying enormous datasets, this type of AI learns to spot patterns, draw conclusions, and even offer predictions. Around one in five C-suite executives say they already use machine learning<\/a> in some capacity.\r\n\r\nWhat these early adopters\u2014and those who follow\u2014will discover is that the insight generated by machine learning, no matter how advanced, is not enough to accomplish strategic goals. For an AI program, the world is numbers, images, and patterns. For an executive, the world is a place where events occur in certain locations at specific times. Even the best machine learning analysis needs real-world context.\r\n\r\nLocation intelligence (LI)<\/a> provides the foundation for that context. Recent technological developments have brought LI and AI together in a hybrid sometimes called GeoAI<\/a>. Geographic information system (GIS) technology generates location intelligence, and through GIS, executives can bring geographic context to AI and discover insight on key decisions.\r\n\r\nThe combination of machine learning and location intelligence helps executives drive strategic initiatives including market growth, customer engagement, and risk mitigation.\r\n

Predicting and Discovering New Markets <\/strong><\/h3>\r\nAccording to a Gartner survey, 53 percent of CEOs list growth among their top three priorities<\/a>. And while the COVID-19 pandemic has idled many growth plans, some executives are already planning for recovery. Once that phase arrives, companies that most effectively use insight and predictive capabilities will outpace their industries, according to McKinsey<\/a>. An AI-fueled approach to location intelligence can support those plans and foster growth in three ways: by automating processes, detecting patterns within large sets of data, and making predictions.\r\n\r\nImagine a bank\u2019s planning committee analyzing the expansion possibilities in a particular city. Using GIS-based machine learning, they can explore the sales data of existing branches and the demographics of surrounding neighborhoods\u2014along with the site\u2019s proximity to the bank\u2019s own branches and competitors\u2019. The machine learning program predicts<\/a> locations that would yield the highest returns with the least cannibalization of business at existing branches.\r\n\r\nRenewable energy companies already use machine learning and location intelligence to understand potential markets. Decision-makers at a solar company, for example, can use AI-based image detection to assess a neighborhood's viability for solar panels, even crunching historical weather data to calculate the ideal placement of roof panels for maximum sun exposure.\r\n\r\nVirtually any business can employ a combination of AI and LI for growth planning<\/a>.\r\n

Uncovering Hidden Patterns in Market Data<\/strong><\/h3>\r\nAnother combination of AI and LI uncovers patterns and offers predictions so executives can discover trends and hot spots lurking in enormous datasets.\r\n\r\nA wireless service provider, for example, can combine machine learning and location intelligence to detect trends<\/a> and guide business investments. Company planners might perform a GIS-based cluster analysis of dropped calls to identify areas where additional cell towers are needed. When they add demographic predictions to the maps, executives can see where population growth will create pockets of potential new subscribers. This allows the company to prioritize network build-outs to meet demand."},{"acf_fc_layout":"form","form_type":"aside","form_position":"Left","form_title":"THE ESRI BRIEF","form_desc":"A biweekly email connecting senior executives and business leaders with thought-provoking articles on location intelligence and critical technology trends.","form_button_label":"Sign up now","form_content":"https:\/\/go.pardot.com\/l\/82202\/2017-10-12\/jw1bmb","form_tag":"low-commitment-form\/sign-up-form"},{"acf_fc_layout":"content","content":"Prediction represents the cutting edge of machine learning and location intelligence. Analysts have even used GIS-based machine learning to solve one of the most complex phenomena in retail\u2014the so-called \"halo effect\"<\/a>\u2014in which a store's sales and nearby online sales are mutually reinforcing.\r\n\r\nThrough a process called \"quantifying proximity,\" location technology<\/a> reveals where physical stores will maximize online sales. To do this, a GIS-based machine learning program models the likely performance of stores in different geographic areas\u2014neighborhoods in a city, for example, or cities across the country\u2014by analyzing factors such as demographic information and the travel time of likely customers.\r\n\r\nThis reveals which locations will produce the most successful stores. Planners can then perform further analysis of online spending patterns in those areas, producing a smart map that highlights which physical locations will experience the greatest halo effect\u2014with revenue predictions for each location.\r\n\r\nAnother form of AI-based trendspotting applies to the insurance industry. GIS technology can create a training data set of past accidents with details on location, time of day, and road conditions. A machine learning program analyzes the data and indicates the probability of accidents<\/a> on specific streets at specific times. The insurer can then advise customers on alternative routes to reduce their risk.\r\n\r\nInsurers are also using a combination of AI and LI to speed relief<\/a> to clients whose homes have been damaged by natural disasters. After training on large sets of images, an AI program learns to distinguish a damaged property from an undamaged property, allowing insurers to automate and accelerate the delivery of payment, which traditionally took days or weeks to complete.\r\n

Understanding Customers and Competition<\/strong><\/h3>\r\nEvery executive takes pains to understand a brand's customers<\/a>; location intelligence strengthens that effort.\r\n\r\nMobile phones, combined with the world's 30 billion IoT<\/a> sensors, have created an avalanche of anonymized human movement data that helps retailers understand their customers better. Add psychographic and demographic data\u2014aggregated at the group level, not by individuals\u2014and the result is a bevy of information with useful truths about customers. But that data is often too vast to explore without machine learning.\r\n\r\nTo better understand how location intelligence adds context to machine learning, consider some of the questions executives ask when planning brick-and-mortar locations:\r\n