The fol­low­ing part­ner insight was authored by Kei­th Hoff­mann, Indus­try Prin­ci­pal at HCLTech.

Util­i­ties today are drown­ing in data. From smart meters and IoT sen­sors to cus­tomer billing and call cen­ter logs, enor­mous vol­umes of infor­ma­tion flow through util­i­ty sys­tems every day. Yet sim­ply hav­ing data is not the same as gain­ing insight — data with­out knowl­edge is useless. 

Many util­i­ty exec­u­tives wor­ry about the ​“unknown unknowns” that are hid­ing in their oper­a­tions: those weak points and inef­fi­cien­cies they don’t even know to look for. Tra­di­tion­al report­ing tools and ana­lyt­ics only reveal what you ask them to; they often miss those sub­tle pat­terns or emerg­ing issues that aren’t on management’s radar. This leaves util­i­ties with blind spots in both oper­a­tional per­for­mance and cus­tomer ser­vice. In an era when 74% of ener­gy and util­i­ty com­pa­nies are already imple­ment­ing or explor­ing AI in their oper­a­tions, rely­ing on lega­cy meth­ods is quick­ly becom­ing insufficient. 

Util­i­ties need a new approach to unlock action­able intel­li­gence from their data and to shine light on those blind spots, and that is where arti­fi­cial intel­li­gence (AI) comes in.

Excep­tion Man­age­ment: From Man­u­al Slog to Intel­li­gent Automation

One tra­di­tion­al­ly high-effort area primed for AI trans­for­ma­tion is excep­tion man­age­ment in oper­a­tional sys­tems. ​“Excep­tions” in a util­i­ty con­text are those irreg­u­lar­i­ties or errors that crop up in process­es like meter read­ing, billing and field operations. 

For exam­ple, a meter read­ing out­side of the expect­ed range would be flagged as an excep­tion, or a bill may fail to gen­er­ate due to a data dis­crep­an­cy. In the past (and for many, still today), teams of employ­ees’ slog through these excep­tions one by one, inves­ti­gat­ing each case, find­ing the cause and fix­ing the data or process man­u­al­ly. This is slow, tedious, and expen­sive work. In fact, resolv­ing a sin­gle billing or meter­ing excep­tion could take hours of an analyst’s time. With thou­sands of such excep­tions per month, it’s no won­der back­logs build up and impor­tant details slip through the cracks.

AI is fun­da­men­tal­ly chang­ing this. By deploy­ing AI-dri­ven automa­tion, util­i­ties can han­dle the bulk of rou­tine excep­tions at machine speed, while also uncov­er­ing the pat­terns behind those excep­tions. Instead of treat­ing each anom­aly in iso­la­tion, an AI sys­tem can ana­lyze data across mil­lions of records to iden­ti­fy com­mon threads. 

For exam­ple, a par­tic­u­lar mod­el of smart meter is fail­ing or mis­read­ing usage in 30% of cas­es, AI would flag that trend, which is some­thing a human review­ing indi­vid­ual tick­ets might nev­er notice. Or con­sid­er billing errors clus­tered in one geo­graph­ic area: an AI might cor­re­late them to a spe­cif­ic sub­sta­tion expe­ri­enc­ing pow­er surges. These insights allow the util­i­ty to address root caus­es (like replac­ing a batch of faulty meters or repair­ing equip­ment in that neigh­bor­hood) rather than just repeat­ed­ly fix­ing symp­toms. In oth­er words, AI helps util­i­ties stop los­ing sight of the for­est for the trees in excep­tion management.

The effi­cien­cy gains are dra­mat­ic. AI-pow­ered excep­tion man­age­ment tools have demon­strat­ed they can shrink res­o­lu­tion times from hours down to min­utes. Faster, smarter excep­tion han­dling means cus­tomer issues, like billing inac­cu­ra­cies, are resolved more quick­ly and cor­rect­ly, which boosts cus­tomer trust. It also reduces cost­ly errors, avoid­ing mis­bills, reg­u­la­to­ry fines and the bad press that comes with large-scale mistakes. 

Inter­nal­ly, automat­ing the ​“easy” excep­tions frees your skilled employ­ees to focus on com­plex, tru­ly excep­tion­al cas­es that require human judg­ment. Instead of expand­ing head­count or, as some do, loos­en­ing the thresh­olds for flag­ging errors just to reduce the queue, util­i­ties can main­tain high stan­dards and let AI tack­le the vol­ume. The result is a more intel­li­gent oper­a­tions back­bone: one that not only reacts to issues faster but con­tin­u­ous­ly learns to pre­vent them.

Cus­tomer Engage­ment: From Frus­tra­tion to Proac­tive Service

Anoth­er domain ripe for AI trans­for­ma­tion is the cus­tomer expe­ri­ence, par­tic­u­lar­ly in util­i­ty call cen­ters and sup­port chan­nels. Con­sid­er the typ­i­cal jour­ney of a frus­trat­ed cus­tomer today: They receive a bill that’s much high­er than expect­ed and can’t fig­ure out why. They try the web­site or app, but not find­ing clear answers, they reluc­tant­ly dial the call cen­ter. After wrestling with an auto­mat­ed IVR sys­tem and repeat­ing their account details mul­ti­ple times, they final­ly reach a live agent. The agent, in turn, scram­bles through numer­ous screens and sys­tems to piece togeth­er the customer’s usage his­to­ry, weath­er data and account notes to explain the high bill. All this effort, only to deter­mine that, yes, the customer’s ener­gy usage spiked due to a heat wave and the bill is accu­rate, some­thing that could have been com­mu­ni­cat­ed proac­tive­ly. The process is frus­trat­ing for the cus­tomer and inef­fi­cient for the utility.

AI can fun­da­men­tal­ly improve this expe­ri­ence. By ana­lyz­ing the huge range of data avail­able – from smart meter read­ings and grid data to indi­vid­ual cus­tomer pro­files – AI can pre­dict why a cus­tomer is call­ing before an agent ever picks up. In fact, AI could pre­vent the call alto­geth­er by address­ing the issue proactively. 

For exam­ple, AI ana­lyt­ics might detect an unusu­al con­sump­tion spike at a res­i­dence and auto­mat­i­cal­ly alert the cus­tomer through their pre­ferred chan­nel ahead of the bill, explain­ing the like­ly cause and even sug­gest­ing ener­gy-sav­ing pro­grams or a per­son­al­ized rate plan. Tak­ing such proac­tive mea­sures ​“low­ers the bar­ri­ers to ser­vice” by head­ing off sur­pris­es. When cus­tomers do need to call, AI can make the inter­ac­tion smoother and faster.

At HCLTech, we devel­oped the Intel­li­gent Cus­tomer Engage­ment (iCE) frame­work that extends the stan­dard Call Cen­ter tool by aggre­gat­ing data from the CIS, out­age and oth­er edge sys­tems to give the call cen­ter agent a 360° view of the cus­tomer. This frame­work uses a sim­ple ​“red/​yellow/​green” traf­fic-light indi­ca­tor to flag the most like­ly rea­son for the call. For instance, a red light might indi­cate the caller is prob­a­bly con­cerned about an unusu­al­ly high bill or a recent ser­vice disruption. 

This kind of intel­li­gent agent assist, pow­ered by AI ana­lyt­ics, means the rep­re­sen­ta­tive doesn’t have to blind­ly probe or rely on the customer’s vague descrip­tions. Even a rel­a­tive­ly junior agent can quick­ly val­i­date the like­ly issue and pro­vide a solu­tion or expla­na­tion with con­fi­dence. The result: short­er call-han­dling times, high­er first-call res­o­lu­tion, and a more pos­i­tive expe­ri­ence for the customer. 

In fact, AI-dri­ven sup­port in util­i­ty con­tact cen­ters has been shown to low­er call vol­umes, by resolv­ing issues through self-ser­vice or proac­tive out­reach, and to speed up call res­o­lu­tion when live agents are need­ed. That trans­lates direct­ly into reduced oper­at­ing costs and even improved employ­ee sat­is­fac­tion, since agents would deal with few­er frus­trat­ed callers and can trust the AI to sur­face the infor­ma­tion they need.

Beyond the call cen­ter, AI chat­bots and vir­tu­al assis­tants are increas­ing­ly capa­ble of engag­ing util­i­ty cus­tomers in nat­ur­al lan­guage. Unlike the rigid phone menus that often infu­ri­ate callers, mod­ern AI assis­tants can under­stand free-form ques­tions and pro­vide instant, accu­rate answers to com­mon inquiries, such as ​“Why is my bill so high this month?”. 

And if the AI can­not solve the issue, it seam­less­ly pass­es the con­text to a human agent, so the cus­tomer doesn’t have to start over – there­by reduc­ing fric­tion. Cus­tomers get solu­tions faster and with less effort, and util­i­ties build good­will by being respon­sive and easy to do busi­ness with. It’s a far cry from the old-school sce­nario of press­ing zero repeat­ed­ly just to escape an unhelp­ful auto­mat­ed system. 

In short, AI allows util­i­ties to trans­form cus­tomer engage­ment from a reac­tive, and often frus­trat­ing, process into a proac­tive ser­vice that meets cus­tomers’ needs with intel­li­gence and empathy.

A New Era of Intel­li­gent Utilities

The util­i­ty sec­tor is on the cusp of a new era defined by data-dri­ven intel­li­gence and agili­ty. AI tech­nolo­gies are enabling util­i­ties to vast­ly improve two areas that have tra­di­tion­al­ly been labor-inten­sive and reac­tive: oper­a­tional excep­tion man­age­ment and cus­tomer engagement. 

By har­ness­ing AI to auto­mate rou­tine excep­tions and reveal hid­den pat­terns, util­i­ties can sig­nif­i­cant­ly enhance reli­a­bil­i­ty, safe­ty and effi­cien­cy in their oper­a­tions, all while reduc­ing cost and risk. By infus­ing AI into cus­tomer-fac­ing process­es, they can turn call cen­ters from cost cen­ters into val­ue cen­ters, deliv­er­ing faster res­o­lu­tions and proac­tive ser­vice that delight cus­tomers. The jour­ney to get there requires more than just tools, it calls for a strate­gic vision of an AI-first orga­ni­za­tion, a com­mit­ment to mod­ern­ize data and sys­tems and enlight­ened change man­age­ment that brings employ­ees along as co-pilots in this transformation.

Util­i­ty lead­ers read­ing this should feel encour­aged that the build­ing blocks are in place to start unlock­ing this oper­a­tional intel­li­gence. Many of your peers have begun explor­ing or imple­ment­ing AI solu­tions, and the tech­nol­o­gy has matured to a point where tan­gi­ble ben­e­fits are with­in reach. The key is to start now, with well-cho­sen ini­tia­tives that address real busi­ness chal­lenges, and to scale up from there. 

Kei­th Hoff­mann is Indus­try Prin­ci­pal at HCLTech. For more cut­ting-edge insights from HCLTech, reg­is­ter to attend the SAP for Util­i­ties, Pre­sent­ed by ASUG con­fer­ence this fall (Sept. 8 – 10; in Den­ver, Colorado).