SAP’s Expansion of Gen AI Assistants Could Supercharge Customer Supply Chains
Software giant SAP continues to chug forward on its integration of artificial intelligence into an increasing number of its systems.
At its annual conference, SAP Sapphire, held in Orlando last week, the company announced it would continue ramping up a co-pilot-like tool, which it calls Joule, for some of its core solutions. Joule, which the company refers to as business AI, is a chatbot meant to help automate tasks throughout a variety of roles, including logistics, procurement, sustainability, sourcing and supply chain. It works in a similar way as ChatGPT, but instead of having generalized knowledge, it can tap into SAP’s systems and processes, as well as an enterprise’s own data, happenings, shipments and more.
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The generative AI technology has now been embedded into SAP S4/HANA Cloud, the company’s proprietary enterprise resource planning (ERP) system.
By the end of the year, Joule will also be available in SAP Ariba, which helps manage sourcing and procurement risks and costs, as well as SAP Analytics Cloud, which aids in business planning and budgeting.
Christian Klein, the CEO of the German company, said that 80 percent of the most frequently completed tasks for customers can be tackled by AI, which could result in a 20 percent productivity increase.
Etosha Thurman, chief marketing and solutions officer for SAP’s Intelligent Spend and Business Network tools, said Joule’s integration into more of SAP’s systems will streamline the way companies operate.
“Now, you can ask Joule, ‘Create an RFP for me for this category.’ It can look at everything you’ve done in the past and recommend the questions and the suppliers,” Thurman said. “I’m taking away the mundane; I’m eliminating tasks, and I’m pointing you to the strategic. That’s another way of upskilling talent. I don’t have to train them on how to do these tasks anymore; I just need to be able to help them understand the context of how to make the decisions and to process the recommendations.”
Thurman said, in the future, she expects clients to use generative AI to assess which contracts should be altered versus completely renegotiated, to negotiate on low-dollar contracts with suppliers and more. For Thurman, those represent day-to-day tasks that can be automated so employees can shift their attention to more strategic initiatives, particularly in sourcing and supply chain.
“There’s still so many mundane tasks that are very operational that happen; the first value would be eliminating them or automating them so that I can focus on new [business] remits—like, how do I tackle sustainability? How do I drive multi-sourcing, because for so many years, we went to single sourcing and now, in this world we live in, you can’t get things out of certain regions,” she said. “Or, how do I create nearshoring? Who’s thinking about that [right now]? Who has time?”
Thurman said, in the sourcing and procurement side of the fashion and apparel industry, she believes that, once teams have the freedom to move away from administrative and bureaucratic tasks, the top strategy move for companies could be nearshoring and re-evaluation of the supply chain.
While sustainability has started popping up as a concern, she said, the lack of agreement on a global standard for environmental initiatives puts a damper on organizations’ sense of immediacy around it.
“The United States haven’t gotten their act together yet on [sustainability]. I think if the United States was on the same page as Europe, with the regulation and expectation, that would be the forefront. But because we’re not there yet, I think the continued political turmoil is going to drive sourcing strategy, category strategy to be a prominent need,” Thurman told Sourcing Journal.
Some of SAP’s future-focused plans for AI require a deeper foundation of trust in the technology among SAP’s customers and some further advances in the technology itself. Nonetheless, some of SAP’s customers have already started pairing their data with AI for things like demand forecasting, which could set them up for fast expansion later on.
Aldo Group, the Canadian shoe and accessory purveyor, has begun using generative AI to help it crunch data in forecasting. Matthieu Houle, the company’s chief information officer, said leveraging the technology helps Aldo bring extra considerations into the mix, rather than solely relying on historical data.
“Without AI, we [would] have a group of people that show up with Excel spreadsheets and reports…and we’re looking at last year, [what] we sold and what we should be able to sell this year,” Houle said. “Now, [with AI], you can crunch a lot of data. You can have weather forecasts, you can have consumer sentiment, you can go very local… Generative AI brings that extra capability to add data…but it’s not to replace [people]; it’s just giving more weapons to the team.”
Kristin Howell, global vice president of retail solution management at SAP, said testing and learning like this will cause retailers to become more confident of AI’s capabilities and strong suits. Once that happens, she said, a slew of new use cases could open up.
“When you’ve really matched the capabilities of the AI with the constructs of your data, you can start to see a scenario where the AI is ingrained in the business,” Howell told Sourcing Journal. “[Eventually AI may] know when to place the order, not just finding the trend or highlighting, ‘You’re going to be low on inventory on Tuesday’—it does that today, but I can see retailers in the future saying, ‘Great, not only spot the trend that I’m low inventory, but go ahead and place the next order for me; cut a purchase order for the next bolt of fabric or the next job.'”
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