[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126148-en":3,"doc-seo-126148-105":31,"detail-sidebar-cat-0-en-105":93},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},126148,687207022233,"Riley","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",6,"Technology","Sustainable AI - Innovations for Energy-Efficient Machine Learning Models","This study investigates integrating artificial intelligence and machine learning into Salesforce development to optimize code and configuration work. The research evaluates how AI-powered recommendation engines improve development efficiency, code quality, and user satisfaction through simulated data and empirical analysis. Results show reduced development time (from 100 to 65 units) and defect density (from 3.8 to 1.9 defects per 1,000 lines of code), alongside better customization accuracy and higher satisfaction. Findings support that AI increases both effectiveness and reliability while enabling faster, more consistent Salesforce outcomes.","| \u003Cbr>Sustainable AI: Innovations for Energy-Efficient |\n| --- |\n| Machine Learning Models\u003Cbr>\u003Cbr>Md Abdullah Al Nahid\u003Cbr>School of IT\u003Cbr>Washington University of Science and Technology.\u003Cbr>[hosennahid511@gmail.com](hosennahid511@gmail.com)\u003Cbr>Mahabub Alam Khan\u003Cbr>School of IT\u003Cbr>Washington University of Science and Technology.\u003Cbr>[mahabub95tech@gmail.com](mahabub95tech@gmail.com)\u003Cbr>Shekh Tareq Ali\u003Cbr>School of IT\u003Cbr>Washington University of Science and Technology.\u003Cbr>[alitareq774@gmail.com](alitareq774@gmail.com)\u003Cbr>Sheikh Md Kamrul Islam Rasel\u003Cbr>School of IT\u003Cbr>Washington University of Science and Technology.\u003Cbr>[sheikhmdkamrul49@gmail.com](sheikhmdkamrul49@gmail.com)\u003Cbr>Mohammad Majharul Islam Jabed\u003Cbr>School of IT\u003Cbr>Washington University of Science and Technology.\u003Cbr>[mi_jabed@yahoo.com](mi_jabed@yahoo.com)\u003Cbr>ABSTRACT\u003Cbr>This study investigates the integration of artificial intelligence (AI) and machine learning (ML) into Salesforce development to optimize code and configuration processes. The primary purpose was to evaluate how AI-powered recommendation engines enhance development efficiency, code quality, and user satisfaction. Using a combination of simulated data and empirical analysis, the study developed an AI recommendation engine and assessed its impact on key performance metrics including development speed, error rates, and customization accuracy. Major findings reveal that AI integration led to a significant reduction in development time (from 100 to 65 units) and defect density (from 3.8 to 1.9 defects per 1,000 lines of code), while improving customization accuracy and user satisfaction. The analysis demonstrates that AI tools streamline development processes and enhance code quality, leading to faster and more reliable outcomes. These findings support the hypothesis that AI significantly benefits Salesforce development by increasing efficiency and effectiveness. The study underscores the value of AI in |\n\nsoftware customization and highlights areas for further research.  \nKeywords: Salesforce, AI Integration, Machine Learning, Development Efficiency, Code Quality  \nIntroduction  \nThe landscape of software development is undergoing a transformative shift, largely driven by advancements in artificial intelligence (AI) and machine learning (ML) . As organizations increasingly rely on platforms like Salesforce for customer relationship management (CRM) and  \nenterprise applications, the demand for enhanced development practices and intelligent customizations grows. Salesforce, a leading CRM platform, has been pivotal in shaping how businesses interact with their customers, manage data, and optimize workflows. However, as the complexity of Salesforce implementations expands, the need for sophisticated tools that can streamline development and  \nconfiguration becomes paramount.  \nAI and ML technologies are emerging as powerful enablersin this context, offering novel ways to enhance the Salesforce development experience. Traditionally, Salesforce customization and development required significant manual effort, involving repetitive tasks and extensive trial and error to achieve optimal configurationsand code quality. This process not only consumes valuable developer time but also introduces variability in the quality of the final product. The integration of AI into Salesforce development promises to address these challenges by providing intelligent recommendations, automating routine tasks, and enhancing decision-making processes.  \nOne of the key innovations in this space is the development of AI-powered recommendation engines designed to optimize code and configuration suggestions. These engines leverage machine learning algorithms to analyze historical data, identify patterns, and predict the most effective customizations. By analyzing past development projects, AI models can generate recommendations that align with best practices and specific project requirements, thereby reducing the likelihood of errors and","cbCaiqLijr7a3ifG","https://ap.wps.com/l/cbCaiqLijr7a3ifG","pdf",584668,11,1,12,"English","en",105,"# Abstract\n# Introduction\n## AI and ML in Software Development\n## Challenges in Salesforce Customization\n## AI-Powered Recommendation Engines\n## Impact on Development Efficiency and Code Quality\n## Impact on User Satisfaction\n## Paradigm Shift in Customization Approach\n## Overall Significance","[{\"question\":\"What is the main goal of this study on AI in Salesforce development?\",\"answer\":\"The study aims to assess how AI-powered recommendation engines can optimize code and configuration processes by improving development efficiency, code quality, and user satisfaction.\"},{\"question\":\"Which performance metrics does the research evaluate?\",\"answer\":\"It evaluates development speed, error rates (including defect density), and customization accuracy using simulated data and empirical analysis.\"},{\"question\":\"What results demonstrate the benefit of AI integration?\",\"answer\":\"AI integration significantly reduces development time (100 to 65 units) and defect density (3.8 to 1.9 defects per 1,000 lines of code), while improving customization accuracy and user satisfaction.\"}]","Sustainable AI - Innovations for Energy-Efficient Machine Learning Models | PDF",1785903408,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"sustainable-ai-innovations-for-energy-efficient-machine-learning-models","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/technology/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/sustainable-ai-innovations-for-energy-efficient-machine-learning-models/126148/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What is the main goal of this study on AI in Salesforce development?","Question",{"text":77,"@type":78},"The study aims to assess how AI-powered recommendation engines can optimize code and configuration processes by improving development efficiency, code quality, and user satisfaction.","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"Which performance metrics does the research evaluate?",{"text":82,"@type":78},"It evaluates development speed, error rates (including defect density), and customization accuracy using simulated data and empirical analysis.",{"name":84,"@type":75,"acceptedAnswer":85},"What results demonstrate the benefit of AI integration?",{"text":86,"@type":78},"AI integration significantly reduces development time (100 to 65 units) and defect density (3.8 to 1.9 defects per 1,000 lines of code), while improving customization accuracy and user satisfaction.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,112,115,120,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":108,"doc_module":4,"doc_module_name":47,"category_name":109,"show_sort_weight":110,"slug":111},5,"Comic",60,"comic",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":113,"slug":114},50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":121,"doc_module":4,"doc_module_name":47,"category_name":122,"show_sort_weight":30,"slug":123},8,"Research & Report","research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":108,"slug":139},19,"General","general"]