For com­pa­nies deploy­ing work­ers for hands-on tasks, SAP Field Ser­vice Man­age­ment has become an essen­tial solu­tion for sched­ul­ing ser­vices, man­ag­ing parts, and pro­vid­ing bet­ter ser­vice to cus­tomers. Now, with SAP’s Joule AI embed­ded in the plat­form, those ser­vices can be fur­ther optimized. 

Friederike Mundt and Ryan Jones, Prod­uct Mar­ket­ing Man­agers at SAP, recent­ly spoke at an ASUG web­cast, ​“The Road Ahead: SAP Field Ser­vice Man­age­ment, SAP Busi­ness AI, and Beyond,” to share how the platform’s AI-pow­ered tools func­tion and to show­case demon­stra­tions of the tech­nol­o­gy in action. 

Inside SAP Field Ser­vice Man­age­ment

SAP Field Ser­vice Man­age­ment is close­ly inte­grat­ed with the SAP Cloud ERP and encom­pass­es both low-code and no-code solu­tions that help users with sched­ul­ing and dis­patch­ing, mobile work­force enable­ment, ana­lyt­ics, and more. 

While some SAP cus­tomers are still work­ing from paper-based sys­tems, oth­ers have gone dig­i­tal and are embrac­ing AI, uncov­er­ing in the process that data-dri­ven plan­ning brings sub­stan­tial ROI, accord­ing to Mundt and Jones.

In fact, automa­tions have already been found to improve dis­patch­er pro­duc­tiv­i­ty by about 50% and reduce errors by about 8%, they explained. One whole­sale dis­tri­b­u­tion com­pa­ny saved 40 met­ric tons of car­bon emis­sions per year due to bet­ter rout­ing and fuel use, as well as 13 min­utes of unbilled trav­el time per hour, and 2 to 5 min­utes while sched­ul­ing each job. 

The poten­tial val­ue for com­pa­nies — and their cus­tomers — is huge, Jones said. Com­ing up, ful­ly autonomous sched­ul­ing will help com­pa­nies build cus­tom rules with com­pa­ny poli­cies to make com­plex sched­ules come togeth­er even more quickly. 

Joule for SAP Field Ser­vice Man­age­ment

Today, there are AI fea­tures already avail­able in SAP Field Ser­vice Man­age­ment via Joule. These include: 

• Joule for Dis­patch­ers, which boosts dis­patch­er effi­cien­cy and pro­vides easy access to help documentation. 

• Intel­li­gent fil­ter­ing, which uses nat­ur­al lan­guage pro­cess­ing for intu­itive and accu­rate job search. 

• Equip­ment insights, which pro­vide infor­ma­tion on past equip­ment per­for­mance to help with proac­tive main­te­nance and avoid downtime. 

• Activ­i­ty sum­maries, which help tech­ni­cians quick­ly see how sim­i­lar issues were resolved in the past, help­ing with repair plan­ning and efficiency. 

• Pre­dic­tive rout­ing, which assists in map­ping the best routes to reduce trav­el times and save on car­bon emissions. 

• Job pre­dic­tion dura­tion, which uses machine learn­ing to help plan schedules. 

• AI pol­i­cy design­er and auto-sched­ul­ing, which helps design and sim­u­late com­plex com­pa­ny poli­cies for scheduling. 

• Plan­ning sim­u­la­tions, which enhance trans­paren­cy for dispatchers.

Dis­patch­ers using Joule for sched­ul­ing ser­vice can use 50 avail­able out-of-the-box rules, or they can cre­ate cus­tomized rules, like requir­ing spe­cif­ic skills for cer­tain job types or set times for ser­vice windows.

They can also view each job’s score, via a rat­ing sys­tem that helps to quan­ti­fy the qual­i­ty of a sched­ule. The scores are help­ful in test­ing out sched­ules; with­out pub­lish­ing any­thing, dis­patch­ers can eval­u­ate how a small change to their automation’s inputs can affect the desired objective. 

Cus­tomers have been ask­ing for more capa­bil­i­ties in Joule, Jones said. SAP has respond­ed by embed­ding nat­ur­al lan­guage pro­cess­ing in the plat­form; this is intend­ed to sup­port dis­patch­ers with sched­ul­ing and sup­ply tech­ni­cians with the infor­ma­tion they need on the job. For exam­ple, if a dis­patch­er asks how to cre­ate a cus­tom pol­i­cy for sched­ules, Joule will respond with the instruc­tions for doing so, along with SAP Help doc­u­men­ta­tion, link­ing to where this infor­ma­tion lives online for fur­ther con­text and validation.

Tech­ni­cians can query Joule based on var­i­ous fac­tors like pri­or­i­ty of job, geog­ra­phy, and equip­ment type, prompt­ing an option for sched­ul­ing, along with alternatives. 

These recent improve­ments to AI capa­bil­i­ties in SAP Field Ser­vice Man­age­ment have been based on cus­tomer feed­back, the pre­sen­ters explained. ​“We are still lis­ten­ing to our cus­tomers and gath­er­ing every month their feed­back to know which direc­tion to go,” Mundt said. 

Com­pa­nies using SAP Field Ser­vice Man­age­ment request­ed gen­er­a­tive AI sum­maries for overviews of data and activ­i­ty sum­maries for spe­cif­ic jobs, such as how the tech­ni­cian fixed the prob­lem and the over­all sta­tus of equip­ment on site. Tech­ni­cians now have access to those sum­maries, so they can get more infor­ma­tion about the his­to­ry of an upcom­ing project.

SAP is addi­tion­al­ly plan­ning a release of Dis­patch­er Agent via Joule in Q1 2025. This update aims to autonomous­ly sched­ule all jobs fit­ting set cri­te­ria, such as a sched­ule for a week that con­sid­ers com­pa­ny poli­cies around skills and dis­tance. A dis­patch­er can view what Joule shares, accept and dis­card as need­ed, then release the schedule. 

Jones empha­sized that after an AI-assist­ed sched­ule is cre­at­ed, there still needs to be human approval and tweaks made. 

Get­ting Start­ed 

To begin tak­ing advan­tage of these AI fea­tures, Mundt advised start­ing with use cas­es that have the biggest, most eas­i­ly mea­sured busi­ness impact, and to focus on repet­i­tive or time-crit­i­cal process­es. Then, Mundt rec­om­mend­ed lever­ag­ing exist­ing data and choos­ing pilot pro­grams with mea­sur­able outcomes. 

Mundt also not­ed it’s impor­tant to engage with end users to build trust. This is the most chal­leng­ing part of imple­men­ta­tion, she said, as there are many skep­ti­cal of AI, from peo­ple con­cerned AI will ulti­mate­ly reduce head­count to those wor­ried it will risk the secu­ri­ty of enter­prise data. ​“Here, it’s real­ly all about com­mu­ni­cat­ing trans­par­ent­ly to all the stake­hold­ers ear­ly and shar­ing what your com­pa­ny is plan­ning,” Mundt said. Every­body should under­stand what the tech­nol­o­gy is intend­ed to achieve and how to use it toward those spe­cif­ic aims. 

Addi­tion­al­ly, com­pa­nies should keep in mind that any new tech­nol­o­gy must be aligned with human over­sight to achieve the desired results. AI is designed to improve pro­duc­tiv­i­ty, not to elim­i­nate posi­tions, she said. More­over, employ­ee sat­is­fac­tion can improve through lever­ag­ing this tech­nol­o­gy, elim­i­nat­ing high­ly man­u­al and repet­i­tive process­es such as report cre­ation and data entry. 

Ulti­mate­ly, work­ing with AI now is going to ben­e­fit com­pa­nies through what Mundt referred to as ​“return on future,” or ROF. AI is here to stay, and many com­pa­nies are already using it and prepar­ing for what­ev­er comes next. ​“The ear­li­er you start, the more ahead you will be as a com­pa­ny,” she said. 

For more, watch the web­cast replay.