The purpose of the research was to assess the impact of Citizen Development activities on digital transformation. The research identified eight categories that contribute to the success of Low-code No-code (LCNC) proj...The purpose of the research was to assess the impact of Citizen Development activities on digital transformation. The research identified eight categories that contribute to the success of Low-code No-code (LCNC) projects: 1) Strategy;2) Infrastructure;3) Technology;4) Processes & Procedures;5) Governance;6) Culture;7) People;8) Goals & Metrics and selected six critical success factors from these categories: 1) Operational Efficiency;2) Time Savings;3) Timeframe to Realize Value;4) Employee Engagement;5) Participation;6) Number of Sponsored Ideas. End users of the digital transformation efforts through Citizen Development were asked to assess the six critical success measures in terms of performance and importance criteria. The research results identified that focus should be applied to improving “Timeframe to Realize Value”, on “Operational Efficiency”, and on “Time Savings” to deliver success.展开更多
This study presents X-AI,a domain-native,agent-driven,and end-to-end modeling platform developed to support digital transformation in the energy sector.X-AI integrates advanced Machine Learning(ML)and Deep Learning(DL...This study presents X-AI,a domain-native,agent-driven,and end-to-end modeling platform developed to support digital transformation in the energy sector.X-AI integrates advanced Machine Learning(ML)and Deep Learning(DL)capabilities into a workflow-driven environment that enables energy engineers to construct and deploy predictive models without prior AI expertise.A key innovation is the introduction of Dragon Dawn(D2),an intelligent agent powered by Large Language Models(LLMs)and agent-based reasoning.D2 interprets natural language instructions,retrieves domain-relevant knowledge,orchestrates modeling workflows,and guides multistep optimization processes,thereby lowering technical barriers and cognitive load for users.To quantitatively evaluate platform usability,a novel metric termed Cognitive-Operation Efficiency Ratio(COER)is proposed,capturing both task efficiency and cognitive effort.Experimental evaluation shows that D2 significantly enhances modeling productivity,with over eightfold improvement in COER.A real-world case study on inflow forecasting in cascade hydropower systems validates the platform’s capabilities.By comparing LSTM and D2-assisted XGBoost models,the study demonstrates how the agent facilitates iterative reasoning,feature enhancement,and hyperparameter tuning.These findings establish X-AI as a practical,scalable AI solution for accelerating intelligent decision-making in the energy domain.展开更多
文摘The purpose of the research was to assess the impact of Citizen Development activities on digital transformation. The research identified eight categories that contribute to the success of Low-code No-code (LCNC) projects: 1) Strategy;2) Infrastructure;3) Technology;4) Processes & Procedures;5) Governance;6) Culture;7) People;8) Goals & Metrics and selected six critical success factors from these categories: 1) Operational Efficiency;2) Time Savings;3) Timeframe to Realize Value;4) Employee Engagement;5) Participation;6) Number of Sponsored Ideas. End users of the digital transformation efforts through Citizen Development were asked to assess the six critical success measures in terms of performance and importance criteria. The research results identified that focus should be applied to improving “Timeframe to Realize Value”, on “Operational Efficiency”, and on “Time Savings” to deliver success.
文摘This study presents X-AI,a domain-native,agent-driven,and end-to-end modeling platform developed to support digital transformation in the energy sector.X-AI integrates advanced Machine Learning(ML)and Deep Learning(DL)capabilities into a workflow-driven environment that enables energy engineers to construct and deploy predictive models without prior AI expertise.A key innovation is the introduction of Dragon Dawn(D2),an intelligent agent powered by Large Language Models(LLMs)and agent-based reasoning.D2 interprets natural language instructions,retrieves domain-relevant knowledge,orchestrates modeling workflows,and guides multistep optimization processes,thereby lowering technical barriers and cognitive load for users.To quantitatively evaluate platform usability,a novel metric termed Cognitive-Operation Efficiency Ratio(COER)is proposed,capturing both task efficiency and cognitive effort.Experimental evaluation shows that D2 significantly enhances modeling productivity,with over eightfold improvement in COER.A real-world case study on inflow forecasting in cascade hydropower systems validates the platform’s capabilities.By comparing LSTM and D2-assisted XGBoost models,the study demonstrates how the agent facilitates iterative reasoning,feature enhancement,and hyperparameter tuning.These findings establish X-AI as a practical,scalable AI solution for accelerating intelligent decision-making in the energy domain.
