AI is a workforce multiplier, not a reducer: Derek Gittoes, VP, Supply Chain Management Product Strategy, Oracle 

 It aligns with what is our market strategy for our supply chain products in general. There are certain industries that we have set as being very important to us and that’s where we’ve been investing our product development resources. So industrial manufacturers, telecommunications companies, medical device and medical equipment manufacturers, consumer products, consumer goods, and then the fifth major one is healthcare providers, which is very different from manufacturing, but still have very critical supply chain needs. Those have been our major focus areas.  

 The technology that garners the most attention is generative AI and Gen AI-based use cases. There are also the applications themselves. Traditionally, a lot of these enterprise applications, supply chain included, are not designed to suit the person on the shop floor, in the factory or the person in the warehouse. They were designed more for data collection, record of production, shipping, etc. It didn’t really help the person producing the product.

Hence, we’ve invested in what we call a Smart Operations Initiative, which includes, the user interface for folks on the shop floor, workstation, mill who actually do the work. So we provide those technologies to support the people who are doing the work. And it’s critical because, the best user experience generates the best results. You can positively impact the quality and quantity of work. AI also helps people become experts in their roles faster. So, we put a lot of emphasis on AI use cases, but a lot of that has been focused on how we improve the user experience of the person actually doing the work.  

 There is definitely a positive reaction particularly from manufacturing folks.

 The biggest thing that I’m excited about is the multi-agent AI use cases. In order management, we delivered an AI agent that helps the customer service representative answer questions that customers may have about their order. We built an AI agent that can utilise all of the customer’s policies around returns, pricing, discount. The next step is not one but multiple agents to answer queries. One agent can figure out customer options and then hand that information off to the next agent, which then implements the customer choice. An agent in the background can then create the order without the customer service rep having to type in the information and create the order. Being able to sequence these agents to perform multiple tasks will have a very big impact on productivity.  

 It has the potential to. In these current use-cases, we’re still inserting human decision-making in that process. Someone still has to decide which solution to offer to the customer or the discretion of what discount gets applied or what the reaction is. But over time as the reasoning capabilities of the LLMs improve and people become more comfortable with the recommendations that the agents produce in leveraging those advances, they will let more of those functions be automated.

 One of the biggest challenges that supply chain organisations face globally is a lack of sufficient manpower. The only way you can get more work done is you need to make the people who are there doing the work more productive. India might be a little different because it’s still in a growth stage in terms of demographics but that will not last forever. In many other markets, the number of people who work in factories and warehouses is shrinking. Yet the demand for the manpower is increasing. So it’s not about technology. No one in manufacturing, I can guarantee you, wants to figure out ways to get rid of people working in the factories because they can never find enough people to work in the factories. I describe it as a people or a workforce multiplier, not a reducer.  

 No, not necessarily. The labour issue may not be as pressing but the point on user experience tailored to the needs of operational roles, it applies just the same. For example, we’ve built AI agents that can guide from a manufacturer’s perspective, the quality inspection plans. We’ve provided AI agents that provide operational guidance to the shop floor workers. It helps make a junior person operate at a level of quality and productivity as a senior person. So again, those, it might be slightly different motivation, but the actual use and technology is the same.