The fol­low­ing part­ner insight was authored by Vin­cent Wang, Senior Man­ag­er, AI Enter­prise at Cog­nizant, and Cal Kailasam, SAP Man­u­fac­tur­ing Ser­vice Line Leader at Cognizant. 

Imag­ine slash­ing excep­tion-han­dling time by 30% and cut­ting train­ing effort by a whop­ping 80%. 

That’s exact­ly what a lead­ing ener­gy util­i­ties com­pa­ny is set to achieve, thanks to Cognizant’s agen­tic-AI-dri­ven case man­age­ment solu­tion. Cer­ti­fied by SAP Busi­ness AI and already mak­ing waves — as evi­denced by recent­ly win­ning sec­ond place at the 2025 SAP Hack2Build glob­al event — this pow­er­ful solu­tion is sched­uled for go-live in Octo­ber 2026. 

Accord­ing to Cognizant’s indus­try bench­marks, these time-sav­ing improve­ments could mean over $2 mil­lion in cost sav­ings. What’s behind these impres­sive results? It’s Cognizant’s com­mit­ment to treat­ing enter­prise AI not just as the lat­est tech trend, but as a smart, ongo­ing invest­ment in busi­ness excellence.

In 2023, Cog­nizant com­mit­ted $1 bil­lion to enter­prise AI ini­tia­tives, stak­ing out the company’s posi­tion as an ear­ly investor in AI, even before the boom peri­od that fol­lowed the main­stream release of Chat­G­PT. These invest­ments fund Cognizant’s AI Lab for research, Agent Foundry for deploy­ing AI agents, and AI Train­ing Data Ser­vices to ensure eth­i­cal data quality.

The scale of that com­mit­ment was recent­ly evi­dent in Cognizant’s com­pa­ny­wide Vibe Cod­ing ini­tia­tive, which set a Guin­ness World Record ear­li­er this year for the largest online gen­er­a­tive AI hackathon, with more than 53,000 par­tic­i­pants. Mean­while, TIME Mag­a­zine named Cog­nizant CEO Ravi Kumar S. to its 2025 TIME100 AI list, high­light­ing him this year as one of the most influ­en­tial peo­ple shap­ing the future of AI.

Behind those mile­stones oper­ates a dis­ci­plined sys­tem for trans­lat­ing exper­i­men­ta­tion into enter­prise-grade results. Cognizant’s oper­at­ing mod­el com­bines strate­gic direc­tion with field-lev­el exper­i­men­ta­tion, which the com­pa­ny describes as a bot­tom-up and top-down approach. Lead­er­ship sets the vision while teams across busi­ness units explore AI appli­ca­tions rel­e­vant to their indus­tries, from life sci­ences to man­u­fac­tur­ing to communications.

As com­pa­nies flock to deploy AI, the busi­ness news head­lines are full of AI ini­tia­tives that fail to deliv­er busi­ness ben­e­fits. Why? Because they start with tech­nol­o­gy, instead of busi­ness use cas­es, lack work­flow inte­gra­tion, and/​or define no mea­sur­able out­comes. Cognizant’s approach inverts that pattern. 

Teams start with the user jour­ney and mea­sur­able out­come, not with the algo­rithm or plat­form. With­in SAP land­scapes, com­mon oppor­tu­ni­ties exist in repet­i­tive, excep­tion-heavy process­es where AI can relieve peo­ple of high­ly man­u­al work. Cog­nizant cre­ates solu­tions that serve as ​“AI dig­i­tal work­mates,” like a per­son­al assis­tant or coach that sim­pli­fies work, accel­er­ates res­o­lu­tion, and reduces the learn­ing curve.

Anoth­er thing that can derail AI pilots is try­ing to trans­form every­thing at once. Suc­cess depends on iden­ti­fy­ing one high-val­ue use case, prov­ing its val­ue, and expand­ing only after results are vis­i­ble and trusted. 

As it turns out, what works in the excit­ing world of AI may actu­al­ly be quite bor­ing. Cog­nizant has a guid­ing prin­ci­ple in select­ing a use case that is like­ly to suc­ceed: fit AI to how your peo­ple already work — mak­ing the most com­plex or man­u­al work eas­i­er, to save them time and energy.

AI Readi­ness: A Frame­work to Ensure Mea­sur­able Results

Turn­ing that phi­los­o­phy into con­sis­tent per­for­mance requires a struc­tured framework.

Cog­nizant uses a capa­bil­i­ty matu­ri­ty mod­el that moves orga­ni­za­tions through three phases:

1. Labor arbi­trage, where automa­tion aug­ments human effort through tools like robot­ic process automation; 
2. Tech­nol­o­gy arbi­trage, where busi­ness val­ue man­age­ment and observ­abil­i­ty cre­ate machine-first oper­a­tions along­side human over­sight; and 
3. Autonomous oper­a­tions, where agen­tic AI allows sys­tems to man­age them­selves while peo­ple inter­vene only when judg­ment is required.

This mod­el mea­sures readi­ness across peo­ple, process­es, data, and infra­struc­ture by assess­ing whether orga­ni­za­tions have large lan­guage mod­els in place, what train­ing pro­grams exist for employ­ees, which tool sets are avail­able, and how cur­rent pain points are being addressed. By ground­ing each engage­ment in this matu­ri­ty assess­ment, teams reduce over­reach, con­trol costs, and pre­vent the high fail­ure rates com­mon in ear­ly-stage ini­tia­tives. Built-in respon­si­ble AI prac­tices such as explain­abil­i­ty, con­fi­dence scor­ing, and strict access con­trol rein­force trust at every step.

The case man­age­ment solu­tion demon­strates the matu­ri­ty mod­el in action. Beyond the head­line sav­ings, the sys­tem uses agen­tic AI to gen­er­ate busi­ness require­ment doc­u­ments auto­mat­i­cal­ly, con­duct fit-gap assess­ments, and sup­port test case devel­op­ment. As orga­ni­za­tions advance to high­er matu­ri­ty stages, the AI begins con­nect­ing inci­dents across dif­fer­ent parts of the enter­prise and fix­ing billing issues with­out human inter­ven­tion, all while stay­ing com­pli­ant with reg­u­la­tions and cut­ting the time need­ed to resolve problems.

The frame­work has proven adapt­able across func­tions and indus­tries: pro­cure­ment depart­ments apply it to sup­pli­er excep­tion man­age­ment, sup­ply chain oper­a­tions use it to address order delays, and finance teams lever­age it for reg­u­la­to­ry rec­on­cil­i­a­tion tasks. Automa­tion han­dles the repet­i­tive work in each case, while AI sum­maries help users act on what matters.

Finance offers a sec­ond exam­ple. Asset uni­ti­za­tion once required exten­sive man­u­al spread­sheet work to com­plete a sin­gle state­ment. Cog­nizant brought in AI-assist­ed con­fig­u­ra­tion blue­prints, auto­mat­ed data map­ping, and tools that gen­er­ate audit-ready finan­cial nar­ra­tives. The result: what once took exten­sive man­u­al work now requires about twen­ty min­utes of human review. As orga­ni­za­tions move up the matu­ri­ty lev­els, the sys­tems become more reli­able and han­dle more work, but human judg­ment stays involved where it mat­ters most.

The archi­tec­ture ques­tion comes down to this: how do you test new ideas with­out threat­en­ing sta­ble oper­a­tions? SAP Busi­ness Tech­nol­o­gy Plat­form (SAP BTP) han­dles the sep­a­ra­tion, inte­grat­ing mul­ti­ple SAP com­po­nents: AI Core runs mod­els at scale. HANA Cloud Vec­tor DB retrieves con­tex­tu­al infor­ma­tion when need­ed. Machine learn­ing and LLMs dri­ve the automa­tion and sum­ma­riza­tion work. Build Work Zone pro­vides dash­boards cus­tomized for dif­fer­ent user roles. Build Process Automa­tion han­dles work­flows, and Joule func­tions as the con­ver­sa­tion­al copilot.

Test­ing hap­pens in iso­la­tion from pro­duc­tion. Teams can try new approach­es with­out risk­ing the sys­tems that keep the busi­ness running.

Scal­ing AI: The Intel­li­gent Enter­prise Is In Sight

Agen­tic AI trans­forms the architecture’s capa­bil­i­ties. The tra­di­tion­al approach was straight­for­ward but lim­it­ed — users posed ques­tions to a sin­gle sys­tem and got answers back, all with­in one appli­ca­tion. Agen­tic AI breaks down those walls, coor­di­nat­ing work across mul­ti­ple plat­forms simultaneously.

A request ini­ti­at­ed in an inven­to­ry plat­form can trig­ger rec­on­cil­i­a­tion in SAP, com­plete relat­ed trans­ac­tions, and close a tick­et in Ser­vi­ceNow, all while the user remains in Microsoft Teams. AI agents han­dle the move­ment of data and actions between sys­tems, free­ing peo­ple to focus on out­comes rather than administration.

This is the prac­ti­cal mean­ing of autonomous oper­a­tions: intel­li­gence that man­ages rou­tine cas­es so humans can spend time where their judg­ment mat­ters most. Unlike tra­di­tion­al sta­t­ic work­flows, agen­tic AI cre­ates dynam­ic automa­tion that con­tin­u­ous­ly incor­po­rates human feedback.

Yet even sophis­ti­cat­ed archi­tec­tures depend on data qual­i­ty. Frag­ment­ed, unstruc­tured infor­ma­tion can stall advanced AI pro­grams. SAP Busi­ness Data Cloud address­es that issue by cre­at­ing an AI-first foun­da­tion where SAP and non-SAP sources con­verge in a sin­gle mod­el. With Data­bricks as part of an OEM solu­tion, Busi­ness Data Cloud pro­vides a cen­tral­ized struc­ture where work­flows can draw on accu­rate, gov­erned data to pro­duce auditable results.

Scal­ing those capa­bil­i­ties for Cognizant’s clients requires an ecosys­tem of trust­ed part­ners. Cog­nizant gains ear­ly access to tech­nolo­gies such as the HANA Cloud Vec­tor Engine through SAP’s Ear­ly Adop­tion Care Pro­gram. Joint pro­grams like SAP Busi­ness AI Jump Start, Hack2Build, and the SAP Not Dia­mond Prompt Opti­miz­er Ear­ly Access pro­gram com­press the val­i­da­tion and cer­ti­fi­ca­tion cycle from months to weeks. Col­lab­o­ra­tion with AWS and Azure extends sim­i­lar agili­ty into deploy­ment and mar­ket­place scaling.

Finan­cial stew­ard­ship remains anoth­er part of respon­si­ble adop­tion. Cloud con­sump­tion, mod­el tokens, and pro­cess­ing band­width all influ­ence cost. Cog­nizant treats cost as an engi­neer­ing para­me­ter, mod­el­ing expect­ed usage ear­ly and val­i­dat­ing assump­tions through lim­it­ed proofs of con­cept so clients can opti­mize mod­el behav­ior before com­mit­ting at scale.

Mov­ing For­ward with Confidence 

Util­i­ties and man­u­fac­tur­ers begin­ning their AI jour­ney should take a dual approach: work from the bot­tom up to iden­ti­fy spe­cif­ic pain points that deliv­er mea­sur­able improve­ment with­in a quar­ter, and from the top down to cre­ate a roadmap tied to the matu­ri­ty mod­el. Orga­ni­za­tions should choose use cas­es that are proven in the indus­try and can demon­strate clear val­ue before expanding.

By invit­ing enter­pris­es to par­tic­i­pate in AI Use Case Val­i­da­tion Work­shops, Cog­nizant helps de-risk ini­tial adop­tion for their clients. Not only does the pro­gram pro­vide tech­ni­cal guid­ance from experts across all three orga­ni­za­tions, but finan­cial sup­port for ideation and proof-of-con­cept devel­op­ment is also available.

Orga­ni­za­tions seek­ing faster deploy­ment can join a pri­vate Hack2Build pro­gram launch­ing in Q4 2025. The pro­gram com­press­es what typ­i­cal­ly takes months into a mat­ter of days. Par­tic­i­pants work along­side Cog­nizant and SAP engi­neers and archi­tects to build a func­tion­ing SAP BTP pro­to­type. By the end, com­pa­nies have both a work­ing pro­to­type and a clear path to pro­duc­tion. While slots are lim­it­ed, the pro­gram runs quar­ter­ly. (Cog­nizant invites clients to con­tact their account rep­re­sen­ta­tive for more information.) 

Get­ting AI right in the enter­prise takes equal parts dis­ci­pline and imag­i­na­tion. Com­pa­nies need to show users clear val­ue while fol­low­ing a struc­tured path for­ward. Core sys­tems have to stay sta­ble even as teams exper­i­ment at the mar­gins. And mov­ing from pilot to pro­duc­tion requires part­ners who under­stand both the tech­nol­o­gy and the busi­ness context.

Cognizant’s work in the SAP ecosys­tem fol­lows this log­ic, build­ing from tar­get­ed automa­tion toward oper­a­tions where AI comes first with a focus on results you can mea­sure, trust you can ver­i­fy, and sys­tems that scale.

Vin­cent Wang is Senior Man­ag­er, AI Enter­prise at Cog­nizant, and Cal Kailasam is SAP Man­u­fac­tur­ing Ser­vice Line Leader at Cognizant.