Decision-making powered by AI can lead to incredible actionable insights. Mithun Nagabhairava, Manager – Data Science, Kalypso, explores how expanding the role of AI helps enable autonomous decision-making, as well as augment the remaining human decision processes with context and decision support mechanisms.
As organizations lean further into artificial intelligence (AI) and machine learning (ML), they look to achieve more with less human input to reduce the increasing risks of over-reliance on human presence and human decision-making for business-critical operations. They call for practical, actionable, data-driven recommendations to help achieve autonomous decision-making capabilities in key areas such as supply chain, advanced planning and scheduling, inventory management, warehouse automation, resource allocation, and logistics.
Any company’s success depends highly on many effective decisions taken on time. However, in many instances, organizational decision-making has reached a complexity ceiling among businesses. The number of factors that come into play when making critical decisions and the complexity of the situations in which these decisions have to be made has far exceeded the human capacity to make the right choices consistently. Also, from what we have witnessed over the past couple of years, the COVID-19 pandemic has highlighted the liability human-dependent operations pose to business continuity and excellence.
To address these challenges, leading organizations prioritize adopting decision intelligence, which frames a wide range of decision-making techniques, bringing together advanced data science and multiple traditional disciplines to monitor, model, optimize, execute and maintain decision models & processes. Gartner recently named decision intelligence as one of its top technology trends for 2022 and predicted that, by 2023, more than one-third of large organizations would use AI-enabled decision intelligence technology, including decision modeling.
Continue reading: https://www.spiceworks.com/tech/artificial-intelligence/guest-article/leveraging-ai-to-embed-actionable-decision-intelligence/
As organizations lean further into artificial intelligence (AI) and machine learning (ML), they look to achieve more with less human input to reduce the increasing risks of over-reliance on human presence and human decision-making for business-critical operations. They call for practical, actionable, data-driven recommendations to help achieve autonomous decision-making capabilities in key areas such as supply chain, advanced planning and scheduling, inventory management, warehouse automation, resource allocation, and logistics.
Any company’s success depends highly on many effective decisions taken on time. However, in many instances, organizational decision-making has reached a complexity ceiling among businesses. The number of factors that come into play when making critical decisions and the complexity of the situations in which these decisions have to be made has far exceeded the human capacity to make the right choices consistently. Also, from what we have witnessed over the past couple of years, the COVID-19 pandemic has highlighted the liability human-dependent operations pose to business continuity and excellence.
To address these challenges, leading organizations prioritize adopting decision intelligence, which frames a wide range of decision-making techniques, bringing together advanced data science and multiple traditional disciplines to monitor, model, optimize, execute and maintain decision models & processes. Gartner recently named decision intelligence as one of its top technology trends for 2022 and predicted that, by 2023, more than one-third of large organizations would use AI-enabled decision intelligence technology, including decision modeling.
Continue reading: https://www.spiceworks.com/tech/artificial-intelligence/guest-article/leveraging-ai-to-embed-actionable-decision-intelligence/
