{"id":769947,"date":"2025-11-24T06:12:18","date_gmt":"2025-11-24T14:12:18","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=wherenext&#038;p=769947"},"modified":"2025-11-24T06:12:18","modified_gmt":"2025-11-24T14:12:18","slug":"fast-four-ai-agentic-flows-with-a-geospatial-twist","status":"publish","type":"wherenext","link":"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/fast-four-ai-agentic-flows-with-a-geospatial-twist","title":{"rendered":"Fast Four: AI Agentic Flows\u2014with a Geospatial Twist"},"author":501,"featured_media":0,"parent":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_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":[],"tags":[1741,200462,479082,479122],"department":[476822],"wherenext-category":[],"industry":[],"class_list":["post-769947","wherenext","type-wherenext","status-publish","format-standard","hentry","tag-ai","tag-business-strategy","tag-cxo-priorities","tag-fast-four","department-data-and-ai"],"acf":{"short_description":"A new form of AI harnesses spatial and nonspatial data to make sense of pivotal business decisions. Mansour Raad explains how.","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"content","content":"If a group of eagles is a convocation and a gathering of bears is a sleuth, what do you call an assembly of AI agents working together?"},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Left","content":"<strong>Snapshot:<\/strong> Business advisers are combining AI agents with geospatial analysis to convert massive stores of data into clarity and speed up pivotal decisions.","snippet":""},{"acf_fc_layout":"content","content":"Whatever the term, it\u2019s Mansour Raad\u2019s specialty. As Esri\u2019s global chief technologist, Raad helps companies worldwide use AI and big data to answer complex business questions. Much of that data is messy\u2014tabular, unstructured, nearly impossible to analyze manually. But when AI unlocks it, companies get answers to high-value decisions fast.\r\n\r\nThe latest evolution: AI agents. Each agent addresses a specific business process and coordinates with others\u2014suggesting which land to acquire or how to reroute delivery trucks on the fly. These agentic flows handle repeatable processes, freeing up GIS professionals for higher-level analysis and giving executives insight that accelerates business processes.\r\n\r\nIn this Fast Four interview, Raad explains what this looks like in practice, and why \u201ctrust but verify\u201d matters more than ever."},{"acf_fc_layout":"kaltura","video_id":"1_hthhlxyp","time":false,"start":0,"stop":""},{"acf_fc_layout":"content","content":"<strong>Mansour, you've traveled the world working with all kinds of companies. At a high level, what are they focused on these days\u2014what are they trying to achieve?<\/strong>\r\n\r\nWe're trying to answer really complex questions today that involve a huge amount of data\u2014we\u2019re talking billions of records. A lot of them now want to blend in AI, these AI assistants, with this massive amount of data."},{"acf_fc_layout":"sidebar","layout":"standard","image_reference":null,"image_reference_figure":"","spotlight_image":null,"section_title":"","spotlight_name":"","position":"Right","content":"<strong>A Pioneer on the Future of AI and GIS<\/strong>\r\n\r\nFor more on AI agents and the future of GIS, see the section \u201cArtificial Intelligence and Intelligent Agents\u201d <a href=\"https:\/\/lsc-pagepro.mydigitalpublication.com\/publication\/?i=853231&amp;p=32&amp;view=issueViewer\">in this <em>xyHt<\/em> article<\/a>.","snippet":""},{"acf_fc_layout":"content","content":"Of course, we look at everything with a <a href=\"https:\/\/www.esri.com\/en-us\/lg\/publications\/exploring-the-business-trend-of-spatial-thinking-wherenext-webcast-10\">geospatial component<\/a>\u2014but what we want to bring in is also their documents that have nothing geospatial in it. That blend of spatial, nonspatial data, real-time, batch, at massive scale\u2014and making everything easy through these AI assistants today\u2014is the challenge that we're confronting today.\r\n\r\n<strong>What does a company do to answer those big questions?<\/strong>\r\n\r\nI'll give you an example . . . We're working with a huge global energy company that wants to do site selection. They have billions of records of parcels all over the world. They want to be able to do site selection in the form of something like, \u201cI'm interested in buying this property, but I'm looking for property with certain constraints, a certain distance from the river, it has to be in a certain soil type, and I want to be able to search for these things planet-wide.\r\n\r\nAnd I need to be able to do it in the local language. If I speak Malay or I speak Dutch or I speak Portuguese, I should be able to do that super easy without knowing GIS.\u201d So, we enabled that to happen, and this quick decision-making\u2014even at this massive scale\u2014can really save lots and lots of dollar values.\r\n\r\n<strong>That's an interesting application of AI with language understanding, etc. But you're pushing that even further. Can you tell us about that?<\/strong>\r\n\r\nThe way we're doing that is we're starting to bring in these agentic flows where these AI agents now can be associated with a large language model and a set of tools and memory to do its job.\r\n\r\nLet me give you an idea. I have a logistics company that wants to transfer goods between a truck that's going between Boston and Atlanta and another truck going from Charlotte to Denver. The logistic person that is managing this whole thing just wants to ask, \u201cI want to transfer these goods between those guys, where do I do that?\u201d\r\n\r\nNow, typically, what they'll do is they'll turn around, ask their GIS person, \u201cHow do I solve this problem?\u201d But think about it. It's a nine-step, multi-reasoning task. It has to geocode all four places, create a route, then pull the geometry, perform the intersection if they intersect, get the intersection, take all that, report it back.\r\n\r\nOur agents today, through this agentic flow, can solve this problem, and now suddenly this logistics person [who] knows nothing about GIS can turn around, answer this question, and get the result that they're looking for and continue with their job.\r\n\r\nBy the way, we're not removing <a href=\"http:\/\/esri.com\/about\/newsroom\/wherenext\/category?search=WhereNext%20profiles\">the GIS person<\/a> out of the equation, we're just making them make the data better, create these agentic flows. So we're elevating their capabilities.\r\n\r\n<strong>That's a great example of time to insight, speed to insight there. Where do we go from here?<\/strong>\r\n\r\nWhat I've been discussing is things on the back end, these agents on the back end. What we're looking for now is agents in the front end. That collaboration between the front and back is going to really give you what I call expressive applications rather than the static applications in here.\r\n\r\nWhat we're looking at [is] not just tabular data, but what we like to see is multisensor coming in here. OK. Imagine you're monitoring a motor, right? You get from the tabular data the speed and the temperature. Well, what if you can put a camera and now you can watch if, for example, smoke happens, you can detect that via a GeoAI model and [it] can tell you that. . . Or imagine you put in a smell sensor, and you can start smelling leaks of oil or gas. Or you can put in a hearing sensor, if the motor starts rattling. So, this multisensor story, an agent looking at that\u2014the back end and the front end combination\u2014is really where we believe is the future of things to happen. And, by the way, we're doing that right now.\r\n\r\n<strong>A lot of possibilities ahead, so thank you for your insight, Mansour.<\/strong>\r\n\r\nYou're welcome. And I want to leave you with a couple of things. You need to curate the data. Trust but verify. And to the famous words of Uncle Ben from Spider-Man, my favorite superhero, with great power comes great responsibility. So, use these things for good. The future is here. We're doing it, so you can do it too.\r\n\r\n<strong>Excellent, great note to end on. If you are interested in AI and big data and how those forces are coming together to help businesses, check out <a href=\"http:\/\/esri.com\/about\/newsroom\/wherenext\/overview\"><em>WhereNext<\/em><\/a> for more interviews and articles and resources.\u00a0<\/strong>"}],"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>Creating AI Agentic Flows to Tame Business Complexity<\/title>\n<meta name=\"description\" content=\"Mansour Fast Four\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/about\/newsroom\/publications\/wherenext\/fast-four-ai-agentic-flows-with-a-geospatial-twist\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Fast 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