Industrial Automation and Field Service Ideas

"We are harmonizing and optimizing SAP’s support & cloud operations processes on user-centric, innovative, performant platforms, focused on delivering a simple, stable, effective, desirable and AI infused internal (for SAP employees) and external (for SAP customers and partners) service management solutions."

"How do you tell if AI call summaries are useful? Our managers are looking at how teams measure AI in customer support and keep running into the same issue. A lot of the easy metrics don’t tell you much about whether the tool is helping. I know this just because I have good ties with one of the managers and hes telling me the procedures. Take AI call summaries. You can measure accuracy and generation rate but a summary can be technically correct while still missing the detail the next agent or supervisor needs. Then someone ends up opening the transcript anyway. Same problem with QA. If managers only review a small sample of calls then it’s hard to know if the patterns they find represent what’s happening across the whole contact center. AI tools that analyze every conversation seem useful here since you can look for trends across AHT transfers resolution and customer sentiment instead of relying on random samples. Real time agent assist is an area I’m reading and hunting since I do want to help them out because I see this workplace long term. Instead of only analyzing what went wrong after a call it can surface answers or flag missed steps while the customer is still on the line. That sounds more useful than adding another dashboard managers check once a week. Are you looking at model accuracy itself or tying AI usage back to things like AHT first contact resolution transfers repeat contacts and CSAT?"

"How do you tell if AI call summaries are useful? Our managers are looking at how teams measure AI in customer support and keep running into the same issue. A lot of the easy metrics don’t tell you much about whether the tool is helping. I know this just because I have good ties with one of the managers and hes telling me the procedures. Take AI call summaries. You can measure accuracy and generation rate but a summary can be technically correct while still missing the detail the next agent or supervisor needs. Then someone ends up opening the transcript anyway. Same problem with QA. If managers only review a small sample of calls then it’s hard to know if the patterns they find represent what’s happening across the whole contact center. AI tools that analyze every conversation seem useful here since you can look for trends across AHT transfers resolution and customer sentiment instead of relying on random samples. Real time agent assist is an area I’m reading and hunting since I do want to help them out because I see this workplace long term. Instead of only analyzing what went wrong after a call it can surface answers or flag missed steps while the customer is still on the line. That sounds more useful than adding another dashboard managers check once a week. Are you looking at model accuracy itself or tying AI usage back to things like AHT first contact resolution transfers repeat contacts and CSAT?"

"To be honest, we're really trying to figure it out. I think lots of folks have jumped in, feet first, all the way in. We're going to go AI because, you know, it's the hot topic of the day. You know, on something like AI, really, you know, automation, digitalization, I think you have to have a strategy. You know, you can't just jump in there and decide we're going to – something sounds cool or someone's got a cool idea. So, I think we're trying to figure out what our strategy is. I often get the question, do I see in the future where AI is going to run our manufacturing plants? The answer is no. I think AI will help us run our manufacturing plants, will help us be more predictive in our maintenance practices."