[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-391251-105":59,"doc-detail-391251-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","generative-aihcltech-webinar-final","Generative AI@HCLTech Webinar Final","","Overview of Generative AI from an HCLTech executive perspective, covering how NLP components such as NLU/NLG, machine translation, synthetic voice, and speech recognition work together. Explores value streams across business process operations, application development and support, infrastructure and operations, and systems/product engineering, including observability, RCA, troubleshooting, automation, and AI-enabled devices. Outlines enterprise adoption dependencies, risks, and an AI philosophy spanning silicon to business processes, plus available HCLTech services and ecosystems.",{"@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":30,"@type":76,"position":81},"https://docshare.wps.com/document/technology/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/generative-aihcltech-webinar-final/391251/",{"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/generative-aihcltech-webinar-final/391251.png","ImageObject",300,407,{"name":92,"@type":93},"Grenda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-20","2026-09-25",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"How does the webinar explain how Generative AI works?","Question",{"text":113,"@type":114},"It connects core capabilities such as NLP (NLU/NLG), machine translation, synthetic voice, and speech recognition, then maps them into business and engineering workflows through search, intent extraction, and response.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"What value streams are covered for Generative AI in operations?",{"text":118,"@type":114},"It covers business process operations (observability and cases), application development and support (mining, experiment/pilot, automation workflows), and infrastructure/operations (observability, root cause analysis, troubleshooting, restoration, and remediation).",{"name":120,"@type":111,"acceptedAnswer":121},"What are the main enterprise adoption risks and dependencies?",{"text":122,"@type":114},"Dependencies include experience with LLM models and data quality/training maturity. Risks highlighted include data privacy and copyright issues, explainability bias, deepfakes and accidental sensitive-data usage, and output veracity concerns with potential cost and outage impacts.","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},391251,1790388854,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":29,"category_name":30,"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":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":146},7971474921005,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Generative AI@HCLTech  \nKalyan Kumar (KK)  \nGlobal CTO and Head, Ecosystems, HCLTech  \nTwitter: @kklive  \n( [https://www. hcltech.com/ai](https://www. hcltech.com/ai))  \nTopics  \n• Understanding Generative AI  \n• Value Streams around Generative AI  \n• Generative AI@HCLTech Services  \n• Generative AI@HCLSoftware  \n• Q&A  \nCopyright © 2023 HCLTech | Confidential  \n2  \nUnderstanding Generative AI  \nHow it Works?  \nSource: Adapted from Gartner (786698 _C)  \nNLP (Natural Language Processing) (NLU and NLG)  \nMachine Translation  \nSynthetic Voice  \nSpeech Recognition  \nValue Stream for Business Process Operations   \nBusiness Process Observability / Cases  \n(Issues / Request)  \nFront Office (Customer Care / Service)  \nMiddle Office (Supply Chain / Risk etc.)  \nBack Office ( Finance / Procurement etc.)  \nProcess Analysis  \n• Search and Retrieval  \n• Intent Extraction and Response  \nProcess Execution  \nProcess Improvement  \n• Knowledge Summarization  \n• Language Translation  \n• Assisted process reengineering  \nCustomer Value = Customer Experience  \nCopyright © 2023 HCLTech | Confidential  \n4  \nValue Stream for Application Development and Support   \nCustomer Requirements / Features / Bugs  \nCustom & Packaged Software  \nAnalysis & Process Mining  \nExperiment & Pilot  \n• AI Transformation Roadmap Development  \n• Model Development  \n• Use/Test Case Generation  \nBuild, Operate & govern  \n• Automation Workflows  \n• Automated Code / Test / UI / UX Generation  \nAdopt and Change  \n• Translation  \n• Knowledge Mining, Triaging and Search  \nData Management & Analytics  \nSoftware Development Processes  \nCustomer Value = Intelligent Apps  \nCopyright © 2023 HCLTech | Confidential  \nValue Stream for Infrastructure & Operations   \nTech Observability & Ticket  \n(Incident / Request /Changes) Projects  \nHybrid Infrastructure (Cloud / Networks / Edge / DC)  \nSystem / Root Cause Analysis (RCA)  \n• Analysis and potential RCA extraction  \n• Intent Extraction and Response  \nTroubleshooting and Remediation  \nRestoration and Support  \n• Remedial Script and SOP generation  \n• Assisted Threat modelling  \n• Enhance employee performance  \n• Knowledge Summarization  \n• Language Translation  \nEnd User and Devices  \nCyber-Security  \nCustomer Value = Service Assurance  \nCopyright © 2023 HCLTech | Confidential  \n6  \nValue Stream for Systems and Product Engineering   \nCustomer Need / System Requirements  \nSoftware Product Engineering  \nSilicon and Mechatronics  \nOperational Technologies  \nPlan & Feasibility  \nDesign & Engineer  \n• Improved efficiency for feasibility analysis  \n• Assisted plan & Specification generation  \n• Smart Factory Automation  \n• Software Engineering, Hardware & VLSI Design workflows  \n• UX Design & Content generation  \nBuild  \n& Test  \n• Automated Code Generation & Review  \n• DevSecOps  \n• Code Testing & Optimization  \nSupport  \n& Maintenance  \n• System simulation & Monitoring  \n• Automated L1 Support  \n• AI enabled QC & Inspection  \nCustomer Value = AI Enabled Devices & Systems  \nCopyright © 2023 HCLTech | Confidential  \n7  \nAnticipated Applicability across various IT Service activities   \n| Dependencies | Risks |\n| --- | --- |\n| • Experience in | • Data privacy & |\n| using LLM models | copyright issues |\n| / GenAI skills |  |\n| • Functional or | • Uncertainty and explainability bias |\n| Operational |  |\n| Complexity | • Costly production outages |\n| • LLM Model |  |\n| Maturity | • Usage of Deepfakes |\n| • Quality of Model |  |\n| training data | • Accidental usage of sensitive data |\n| • Output Veracity |  |\n| and Changes | • Data explosion and |\n| required for use | costs |\n\nAdoption in Enterprises will mirror Cloud Journey and the FinOps discipline to govern consumption will become  \nextremely crucial.  \nCopyright © 2023 HCLTech | Confidential 8  \nAI Philosophy @HCLTech  \nCreate Infuse Embed Integrate  \nAI  \nFrom  \nSilicon  \nto  \nInfrastructure, Apps, Data &  \nBusiness Processes  \nTo  \nsupercharge  \ncustomers journey toa Generative Enterprise  \nAI","cbCaiaZkDyBHxc3R","https://ap.wps.com/l/cbCaiaZkDyBHxc3R","pdf",3138991,23,"English","# Topics\n## Understanding Generative AI\n## Value Streams around Generative AI\n## Generative AI@HCLTech Services\n## Generative AI@HCLSoftware\n## Q&A","[{\"question\":\"How does the webinar explain how Generative AI works?\",\"answer\":\"It connects core capabilities such as NLP (NLU/NLG), machine translation, synthetic voice, and speech recognition, then maps them into business and engineering workflows through search, intent extraction, and response.\"},{\"question\":\"What value streams are covered for Generative AI in operations?\",\"answer\":\"It covers business process operations (observability and cases), application development and support (mining, experiment/pilot, automation workflows), and infrastructure/operations (observability, root cause analysis, troubleshooting, restoration, and remediation).\"},{\"question\":\"What are the main enterprise adoption risks and dependencies?\",\"answer\":\"Dependencies include experience with LLM models and data quality/training maturity. Risks highlighted include data privacy and copyright issues, explainability bias, deepfakes and accidental sensitive-data usage, and output veracity concerns with potential cost and outage impacts.\"}]","Generative AI@HCLTech Webinar Final | PDF",1790350380,58]