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Jared Coyle could have been SAP Americas’ first chief Awesome New Stuff officer.
Before becoming the company’s inaugural chief AI officer, Coyle led a team by that name that explored emerging AI technologies and how they might be of use to the German enterprise software giant.
“It was one of those kinds of grassroots communities—if you could call it that—and then it really converted into a formalized identity because of the hot topic,” Coyle said.
The Awesome New Stuff group even played around with a fledgling GPT model for customer service years before ChatGPT made the tech mainstream. (Could Coyle sense then that it would later become a tech revolution? “I would love to say I had that prescience; no.”)
Fast forward a few years, and generative AI is now a much bigger deal for SAP. The company’s AI copilot, Joule, is embedded throughout its products. SAP has spun up AI agents under the Joule umbrella for tasks like expense validation, sales and customer service, and supply chain management. “We are once again doubling down on AI in 2025,” CEO Christian Klein said on an earnings call in January.
SAP tapped Coyle to be the face of these AI operations in the Americas, a role that covers both how SAP uses the technology internally as well as the selling of AI business products to enterprise customers.
Years into the generative AI craze, chief AI officer remains a popular job for companies looking for someone to spearhead AI transformations; you can read Tech Brew’s other CAIO profiles here.
Svelte-LMs: It’s a big job in an increasingly crowded space, but Coyle said SAP’s already-established place in offices everywhere works to its advantage.
“What we realized is, we are running a huge portion of the world’s economy through our systems, and we need to bring AI to the business processes in those solutions,” he said.
That bigger mass of data doesn’t necessarily translate to bigger models, though. Coyle said giving smaller, specialized foundation models a knowledge graph of information has proven to cut down on hallucinations. Often, AI use cases that end up being most efficient might be seen as “boring,” he said on a recent panel.
“Instead of looking at large models, you look at small foundation models with very specific skill sets for a vertical, like in supply chain, or in collections, or in accounts receivable, et cetera, et cetera.” Coyle told us. “One of the best things we’ve done is creating a knowledge graph, basically a graph engine that can understand, what’s a sales order, what’s a purchase order, what is this random field that existed in your [enterprise resource planning] system 30 years ago that you don’t know what it is?”
A day in the life: So what does an average day in such a far-reaching job look like? Well, with “a remit [of] anything between Canada and Argentina,” it often starts with getting on an airplane, Coyle said.
After that, he’s “usually giving a few presentations, whether that’s a keynote on a bigger stage, whether it’s a panel…or whether that’s meeting with fellow top executives at one of our customers—either one who already has purchased or one who is purchasing.”
With the relatively new line of business growing so fast, the job also currently involves a lot of hiring and other HR activities, Coyle said. Then there’s making sure that the AI business is keeping up with its objectives and metrics for success.
“If I’m to really bucket my time, there are the externally facing things that you see on LinkedIn or in a publication or whatnot that gets a lot of the attention. There are the human elements, which are really critical when you’re in the stage of building an organization like this. And then there is also the aspect of, well, we have to deliver against what we are promising to the board,” Coyle said.
The physics of it: Coyle has had plenty of experience with wrapping his head around complicated systems. Before joining SAP in 2013, he taught quantum electrodynamics at Drexel University. He has a PhD in electrical and computer engineering.
It doesn’t necessarily sound like the conventional background of someone who works at a company perhaps best known to workers for airport coffee reimbursements and benefits hubs. But maybe it is? “When I made the move, it was actually because I met a number of former physicists and chemists who worked at SAP,” Coyle said.
“Having the technology background was really helpful, but having the practical attitude of ‘Listen—the laws of physics, if I can say that, apply to any situation regardless,’ has been immensely helpful for me,” Coyle said. “In my first job, when a system was escalating and crashing, no matter how much a CEO or a CFO yells about that, that doesn’t solve the problem. Likewise, when you’re implementing something, the system has to reflect reality. The data has to reflect reality.”
As for the future of AI, Coyle said he’s maybe most excited about “the Moore’s Law of AI”—AI agents getting more efficient with energy and compute so they can work together in surprising ways in the physical world.
“We’ll think about, ‘OK, I need this optical character recognition at a warehouse that’s going to see that the truck has arrived, and then it’s going to take a set of discrete actions in the system,’ and as more complex reasoning capabilities and models come out that can run on that edge camera, the opportunities in the physical world are going to manifest themselves in ways we have yet to imagine,” Coyle said.