Leveraging AI to Embed Actionable Decision Intelligence
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 itstop technology trends for 2022and predicted that, by 2023, more than one-third of large organizations would use AI-enabled decision intelligence technology, including decision modeling.
Decision intelligence is designed to drive smarter decision-making by leveraging AI and ML to observe and analyze data, explore the chain of cause-and-effect relationships governing the system, and understand how actions lead to outcomes. Because the decision intelligence process is automated, it is quicker, non-biased, and rational. This means teams can evaluate recommendations, consider actions and potential outcomes, the risk-reward dynamic and improve their decision-making process by utilizing data they already have.
Enterprises across a wide variety of sectors have already expanded the role of AI to enable autonomous decision-making and augment the remaining human decision processes with context and decision support mechanisms. By leveraging existing digital operations and business data, enterprises can reach a new level of optimization, freeing up human resources and enabling an autonomous enterprise.
As more businesses accelerate their digital transformation efforts, the amount of data generated increases exponentially. Also, many of the critical decisions required to solve complex production challenges in this day and age are highly intertwined across all the levels of the organization’s ISA-95 model.
Take, for example, business planning decides what products to make, where, and with which materials from which suppliers based on the demand cycles that vary constantly.
