At Logility, we’re committed to staying at the forefront of industry trends and innovations, like Supply Chain AI, to better serve our customers. That’s why as a Director on our Innovation Team, I was particularly interested in attending the Gartner Data & Analytics Summit in March, which is known for providing cutting-edge insights and thought leadership. Throughout the conference, I gained valuable insights and new ideas related to supply chain solutions and analytics.
A key session was “The Future of Data Science and Machine Learning: Critical Trends You Can’t Ignore,” where Gartner analyst Svetlana Sicular discussed a variety of topics including those related to artificial intelligence. Specifically, generative AI, real-time AI, and responsible AI were of interest due to contribution to overall their impact on overall supply chain AI developments.
Let’s start with Generative AI, which includes innovative conversational AI bots like ChatGPT. In the session entitled “The Enterprise Implications of ChatGPT and Generative AI,”, Gartner analyst Arun Chandrasekaran provided a valuable introduction to ChatGPT and Generative AI. He discussed the benefits and risks associated with this cutting-edge technology. For our customers implementing supply chain AI solutions, the benefits of ChatGPT and generative AI are vast, including improved automated workflow and decision-making, guided scenarios and simulations, and intelligent risk management.
However, it’s important to recognize the inherent risks as well, such as the black box nature of the models and the potential for erroneous or irrelevant responses. To address these risks, companies should implement responsible practices such as usage guidelines, model monitoring, and user feedback loops.
Real Time AI
In the session “Real-Time AI and Decision Intelligence”, speaker W. Roy Schulte outlined the need for real-time decision-making in processes such as risk management and digital transformation. Real-time AI, such as natural language processing, is used on ongoing data streaming, enabling more real-time analysis and insights. The area of distributed models, including “Edge AI” or TinyML models, is gaining traction. While these models are highly responsive, they tend to be less accurate than traditional models. Advanced Virtual Assistants and Adaptive AI, self-learning AI that continuously changes and improves over time, were also highlighted.
Finally, the session “Responsible AI: From Bias and Privacy to Compliance and Risk Management” by Gartner analysts Pieter den Hamer and Svetlana Sicular, delved deeper into risk and mitigation of the broader AI field. They outlined risks for incorporating broad AI technologies, including supply chain AI models, and how companies can overcome these risks. At a minimum, they recommend companies prioritize their risks and at least focus on handling privacy, fairness, and accountability. Privacy includes ensuring that personal data is not shared. Fairness ensures that bias is eliminated against specific groups or classes of people. To achieve fairness, make sure there is diversity in the data and on the team along with create a measure of fairness in model monitoring. Accountability means making sure that frameworks are in place to prevent failures and to comply with regulatory requirements.
At Logility, we are committed to leveraging the latest technologies like Generative AI, Real-time AI, and Responsible AI to provide our customers with the best possible solutions for their supply chain needs. As the industry continues to evolve, we will remain vigilant in our research of trends in supply chain AI to better serve our customers and help them succeed in their businesses.
VP Research Product Strategy
Lynne Goldsman helps lead Logility’s innovation team to research and develop cutting edge outcomes for clients. Her experience expands over 25 years of identifying, developing, and supporting client deployments of innovative solutions to supply chain challenges. She has served as research analyst, developer, supply chain consultant, and others.