[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-290844-105":59,"doc-detail-290844-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","automated-machine-learning-ai-driven-decision-making-in-business-analytics-research-article","Automated machine learning: AI-driven decision making in business analytics - research article","","AI-driven decision-making is increasingly central to fast-moving, highly competitive markets, yet analytics expertise remains in short supply. Automated machine learning (AutoML) addresses this by enabling fully automated model selection and hyperparameter tuning, improving access for non-experts. The study benchmarks H2O AutoML against a manually tuned stacked ML model on three real-world business analytics datasets, showing the tuned model has advantages across cases while H2O AutoML delivers near-professional reliability, faster prototyping, and reduced expert-demand gaps. The work supports automated decisions and human empowerment.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-machine-learning-ai-driven-decision-making-in-business-analytics-research-article/290844/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-machine-learning-ai-driven-decision-making-in-business-analytics-research-article/290844.png","ImageObject",300,407,{"name":92,"@type":93},"Skyler","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",11,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"Why is automated machine learning (AutoML) important for business analytics?","Question",{"text":113,"@type":114},"AutoML reduces reliance on scarce ML expertise by automating model choice and hyperparameter tuning, making analytics more accessible and scalable across industries.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was H2O AutoML evaluated in the study?",{"text":118,"@type":114},"H2O AutoML was benchmarked against a manually tuned stacked ML model using three real-world datasets in business analytics.",{"name":120,"@type":111,"acceptedAnswer":121},"What were the main findings about performance and usability?",{"text":122,"@type":114},"The manually tuned model achieved performance advantages in all three cases, while H2O AutoML proved fast, easy to use, and produced reliable results close to a professionally tuned model, supporting faster prototyping.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},290844,1790465667,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":34,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336464648746,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Intelligent Systems with Applications 18 (2023) 200188  \nContents lists available at ScienceDirect  \nIntelligent Systems with Applications  \njournal [homepage:](homepage: www.journals.elsevier.com/intelligent-systems-with-applications)[ www.journals.elsevier.com/intelligent-systems-with-applications](homepage: www.journals.elsevier.com/intelligent-systems-with-applications)  \n| Automated machine learning: AI-driven decision making in business analytics |  |  | |\n| --- | --- | --- | --- |\n| Marc Schmitt\u003Cbr>Department of Computer Science, University of Oxford, UK\u003Cbr>Department of Computer & Information Sciences, University of Strathclyde, UK |  |  |  |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Artificial intelligence Machine learning\u003Cbr>AutoML\u003Cbr>Business analytics\u003Cbr>Data-driven decision making Digital transformation Human empowerment |  | The realization that AI-driven decision-making is indispensable in today’s fast-paced and ultra-competitive marketplace has raised interest in industrial machine learning (ML) applications significantly. The current demand for analytics experts vastly exceeds the supply. One solution to this problem is to increase the userfriendliness of ML frameworks to make them more accessible for the non-expert. Automated machine learning (AutoML) is an attempt to solve the problem of expertise by providing fully automated off-the-shelf solutions for model choice and hyperparameter tuning. This paper analyzed the potential of AutoML for applications within business analytics, which could help to increase the adoption rate of ML across all industries. The H2O AutoML framework was benchmarked against a manually tuned stacked ML model on three real-world datasets. The manually tuned ML model could reach a performance advantage in all three case studies used in the experiment. Nevertheless, the H2O AutoML package proved to be quite potent. It is fast, easy to use, and delivers reliable results, which come close to a professionally tuned ML model. The H2O AutoML framework in its current capacity is a valuable tool to support fast prototyping with the potential to shorten development and deployment cycles. It can also bridge the existing gap between supply and demand for ML experts and is a big step towards automated decisions in business analytics. Finally, AutoML has the potential to foster human empowerment in a world that is rapidly becoming more automated and digital. |  |\n\n1. Introduction  \nAI-enabled intelligent systems are vital to survive and thrive in this era of relentless competition shaped by accelerating globalization and an ever-increasing basket of disruptive technologies (Warner & W¨ager, 2019). All major industries – finance, healthcare, manufacturing, retail, supply chain, logistics, and utilities – have started to utilize advanced analytics and are potentially disrupted by applications based on machine learning (ML) and artificial intelligence (AI) (Dwivedi et al., 2021; Schmitt, 2020).  \nData-driven decision-making based on AI/ML has become indispensable in today’s global, fast-paced, and ultra-competitive market (Davenport, 2018). Business analytics plays a major part to facilitate this new way of decision-making (Schmitt, 2023). It is an interdisciplinary field and combines machine learning, statistics, information systems, operations research, and management science (Sharda, Delen & Turban, 2017) and is usually divided into descriptive, predictive, and prescriptive analytics (Delen & Ram, 2018).  \nThe necessity to adopt sophisticated predictive models to make  \nintelligent decisions is without question, but the ability to capture value through analytics is heavily dependent on employees with the required skill set to leverage those analytics capabilities (Grover, Chiang, Liang & Zhang, 2018). Even though initiatives toward data science education have started to manifest themselves (Clayton & Clopton, 2019), the huge demand for talent that makes sense of data and pr","cbCaijqKHHdbjAPF","https://ap.wps.com/l/cbCaijqKHHdbjAPF","pdf",682894,"English","# Introduction\n## Background and need for AI-enabled decision-making\n## Business analytics and decision-making paradigms\n## Talent gap and AutoML as a solution\n## Research framing and open-source AutoML selection","[{\"question\":\"Why is automated machine learning (AutoML) important for business analytics?\",\"answer\":\"AutoML reduces reliance on scarce ML expertise by automating model choice and hyperparameter tuning, making analytics more accessible and scalable across industries.\"},{\"question\":\"How was H2O AutoML evaluated in the study?\",\"answer\":\"H2O AutoML was benchmarked against a manually tuned stacked ML model using three real-world datasets in business analytics.\"},{\"question\":\"What were the main findings about performance and usability?\",\"answer\":\"The manually tuned model achieved performance advantages in all three cases, while H2O AutoML proved fast, easy to use, and produced reliable results close to a professionally tuned model, supporting faster prototyping.\"}]","Automated machine learning: AI-driven decision making in business analytics - research article | PDF",1789642770,18]