Presented by SAP
When SAP ran a quiet internal experiment to measure consultants’ attitudes toward AI, the results were surprising. Five teams were asked to verify answers to more than 1,000 business requirements completed by SAP’s AI co-pilot, Juul for Consultants – a workload that would typically take several weeks.
The four teams were told that the analysis was completed by junior trainees fresh out of the school. He reviewed the material, found it impressive and gave the work a nearly 95% accuracy rating.
The fifth team was told that the same answers had come from the AI.
He rejected almost everything.
Only when asked to verify each answer one by one did they discover that the AI was, in fact, highly accurate – revealing detailed insights that the consultants had initially dismissed. Overall accuracy? Again, about 95%.
“The lesson learned here is that we need to be very cautious when we introduce AI – especially how we communicate with senior consultants about its possibilities and how to integrate it into their workflow,” says Guillermo B. Vazquez Méndez, chief architect, RI business transformation and architecture, SAP America Inc.
This experiment has since become a promising starting point for SAP’s effort toward the Consultant 2030: a practitioner who is deeply human, enabled by AI, and no longer bogged down by the technical drudgery of the past.
Overcoming AI skepticism
The resistance isn’t surprising, Vazquez says. Advisors with two or three decades of experience have vast institutional knowledge – and an understandable degree of caution.
But for consultants, AI co-pilots like Juul aren’t replacing expertise. They are increasing it.
“What Juul really does is make their very expensive time management more effective,” says Vazquez. “It eliminates clerical work, so they can focus on delivering high-quality answers in less time.”
He constantly emphasizes this message: “AI is not replacing you. It is a tool for you. Human oversight is always needed. But now, instead of wasting your time looking for documentation, you are gaining valuable time and increasing the effectiveness and detail of your answers.”
Advisory Time-Shift: From Technical Execution to Business Insight
Historically, consultants spent about 80% of their time understanding technical systems – how processes run, how data flows, how tasks are executed. In contrast, customers spend 80% of their time focused on their business.
That mismatch is exactly where Juul steps in.
“There’s a gap there — and the bridge is AI,” says Vazquez. “This reverses the timing equation, enabling advisors to invest more of their energy in understanding the client’s industry and business goals. AI takes the heavy technological lift, so advisors can focus on driving the right business outcomes.”
Getting new advisors up to speed
AI is also changing the way new employees learn.
“We’re excited to see Juul serve as a bridge between senior advisors, who are adapting more slowly, and trainees and new advisors, who are already tech-savvy,” says Vazquez.
Junior consultants advance faster because Juul helps them work independently. Meanwhile, senior people engage where their insights matter most.
This is also where many advisors learn the fundamentals of today’s AI copilots. Much of the work relies on rapid engineering – for example, instructing Juul to act as a senior chief technology architect specializing in finance and SAP S/4HANA 2023, then asking him to analyze business requirements and deliver the output as tables or PowerPoint slides.
Once they understand how to formulate signals, advisors consistently get higher quality, more structured answers.
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 easier. This has created a real synergy, says Vazquez – senior advisors see how quickly new hires are adjusting to and learning AI, and that speed encourages them to keep pace and adopt the technology themselves.
Looking towards the future of AI copilots
“We are still in the early stages of AI – we are kids,” says Vazquez. “Right now, co-pilots rely on quick engineering to get good answers. The better you signal, the better answer you get.”
But this only represents the initial stage of what these systems will eventually do. As co-pilots mature, they will move beyond responding to prompts and begin to interpret entire business processes – understanding the sequence of steps, identifying where human intervention is needed, and figuring out where an AI agent can take over. That shift leads directly to agentic AI.
SAP’s depth of process knowledge is what makes that growth possible. The company has mapped more than 3,500 business processes across various industries – a repository Vazquez calls “some of the most valuable, rigorously tested processes developed over the last 50 years.” Every day, SAP systems support approximately $7.3 trillion in global commerce, giving these emerging AI agents a rich base to navigate and reason on.
“With that level of process insight and data, we can make a real leap forward,” he says, “equipping our advisors with agentic AI that can solve complex challenges and push us toward increasingly autonomous systems.”
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