Presented by SAP
When SAP conducted a quiet internal experiment to gauge advisor attitudes toward AI, the results were surprising. Five teams were asked to validate responses to more than 1,000 business requirements completed by SAP’s AI co-pilot, Jewel for Consultants—a workload that would typically take several weeks.
The four teams were told that the analysis had been completed by a junior intern outside the school. They reviewed the material, found it impressive, and rated the work about 95% accurate.
A fifth team was told that the same answers came from the AI.
He rejected almost everything.
Only when asked to validate each answer did they discover that the AI ​​was, in fact, highly accurate — on top of the detailed insights the advisors had initially omitted. Overall accuracy? Again, about 95%.
“The lesson here is that we need to be very careful as we introduce AI — particularly in how we communicate with senior consultants about its potential and how it can be integrated into their workflows,” said Guillermo B. Vazquez Mendez, chief architect, RI Business Transformation and Architecture, SAP Americas Inc.
That experience has since become a revelatory starting point for SAP’s push toward the 2030 advisor: a practitioner who is deeply human, enabled by AI, and no longer weighed down by the technicalities of the past.
Overcoming AI Doubts
The resistance isn’t surprising, Vazquez notes. Advisors with two or three decades of experience have a great deal of institutional knowledge – and an understandable degree of caution.
But for advisors, AI copilots like Joule aren’t replacing expertise. They are increasing it.
“What Jewel really does is make their precious time far more efficient,” Vazquez says. “It removes the cognitive work, so they can focus on giving high-quality answers in a fraction of the time.”
He consistently emphasizes this message: “AI is not replacing you. It’s a tool for you. Human oversight is always needed. But now, instead of spending your time searching for documents, you’re gaining valuable time and increasing the effectiveness and detail of your answers.”
Consultant Timeshift: From Tech Implementation to Business Insights
Historically, consultants spent about 80% of their time understanding technical systems – how processes work, how data flows, how functions work. In contrast, consumers spend 80% of their time focused on their business.
That similarity is exactly where Jolol enters.
“There’s a gap — and there’s a bridge,” says Vazquez. “This flips the timing equation, enabling advisors to spend more of their energy understanding the client’s industry and business goals. AI does the heavy technical lifting, so advisors can focus on driving the right business outcomes.”
Bringing new advisors up to speed
AI is also changing how new hires learn.
“We’re excited to have Jewel act as a bridge between senior consultants, who are adapting more slowly, and interns and new consultants who are already technically savvy,” says Vazquez.
Junior consultants grow quickly as Joule helps them operate independently. Seniors, meanwhile, tend to engage where their insights matter most.
This is where many advisors learn the fundamentals of today’s AI copilots. Much of the work depends on immediate engineering – for example, instructing Joule to act as a senior chief technology architect specializing in finance and SAPS/4 Hana 2023, then asking him to analyze business requirements and deliver the output as a table or PowerPoint slide.
Once they understand how to prompt, counselors consistently get higher-quality, more structured responses.
New architects are also able to communicate more clearly with their more experienced counterparts. They know what they don’t know and can ask targeted questions, making guidance much smoother. “It’s created a real synergy,” Vazquez added.
Looking forward to the future of AI Copilots
“We’re still in the baby steps of AI — we’re a toddler,” Vazquez says. “Right now, copilots rely on quick engineering to get good answers. The better you point, the better the answer you get.”
But this represents just the beginning of what the system will eventually do. As copilots mature, they’ll move beyond responding to cues and begin interpreting entire business processes—understanding sequences of actions, identifying where human intervention is needed, and where AI can take over for an agent. That change is what leads directly to agent AI.
SAP’s depth of process knowledge is what makes this evolution possible. The company has mapped more than 3,500 business processes across industries — a collection Vazquez says is “some of the most valuable, rigorously tested processes developed in the last 50 years.” Every day, SAP systems support approximately $7.3 trillion in global trade, providing a rich base for these emerging AI agents to navigate and influence.
“With this level of process insight and data, we can make a real leap,” he says, “to equip our advisors with agent AI that can solve complex challenges and push us toward increasingly autonomous systems.”
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