In his­tor­i­cal­ly siloed IT depart­ments, the impact of the enter­prise archi­tect (EA) was often dilut­ed. Though intend­ed to bol­ster busi­ness opti­miza­tion ini­tia­tives, EAs lacked vis­i­bil­i­ty and influ­ence at the board lev­el, lim­it­ing their abil­i­ty to pro­vide vis­i­bil­i­ty of data, peo­ple, process­es, and tech­nol­o­gy to those tasked with enter­prise-wide decision-making.

More than in the past, IT invest­ments are now under­stood by busi­ness lead­ers as key dri­vers of com­pet­i­tive val­ue for orga­ni­za­tions across indus­tries, ​“but the ones that could put that in a real sce­nario were not talk­ing to each oth­er,” said Guillem Oña Viz­caino, enter­prise archi­tec­ture expert advi­sor at SAP, who spoke at the Next Gen­er­a­tion SAP Enter­prise Archi­tect Learn­ing Forum 2025.

Dis­cussing how enter­prise archi­tec­ture can bring a true com­pet­i­tive advan­tage to the busi­ness­es of the future, Vis­caino not­ed that invest­ment in IT infra­struc­ture and tech­nol­o­gy over­all con­tin­ues to grow near­ly 10% year over year. Tak­ing enter­prise-grade tech­nol­o­gy to the next lev­el and into the future will increas­ing­ly become a busi­ness differentiator.

Viz­caino argued that greater busi­ness val­ue will come from orga­ni­za­tions with lead­ers who think like enter­prise archi­tects, see­ing their company’s AI strat­e­gy, IT assets, and data as areas they can optimize.

Exec­u­tive Deci­sions Will Fall into Enter­prise Archi­tec­ture Domain 

Deci­sions around dig­i­tal trans­for­ma­tion can have a big impact on an organization’s over­all suc­cess — in both pos­i­tive and neg­a­tive directions. 

Viz­caino not­ed as an exam­ple of this the BBC’s dig­i­tal media ini­tia­tive, which launched around 2007. Its goal was to dig­i­tize its archive and all pro­duc­tion process­es in order to bet­ter man­age its exist­ing assets and pro­vide stronger cus­tomer ser­vice. This seemed to make sense from a busi­ness stand­point, but, by 2012, the project was scrapped. Com­pet­ing pri­or­i­ties, mis­aligned teams, and mis­man­aged gov­er­nance ulti­mate­ly led to the project’s down­fall, cost­ing mil­lions and pre­sent­ing legal risks.

Viz­caino also dis­cussed The Co-Oper­a­tive Bank, which had aimed to move to a more mod­ern land­scape, stream­line bank­ing oper­a­tions, and enhance dig­i­tal ser­vices. Management’s fail­ure to cre­ate a struc­tured frame­work from the start led this busi­ness astray, he recount­ed. Along with delays due to poor project gov­er­nance, con­flict­ing IT pri­or­i­ties, and inte­gra­tion issues caus­ing dis­rup­tions to core bank­ing solu­tions, the esti­mat­ed spend of the failed project was near­ly $500 mil­lion. The com­pa­ny had to seek exter­nal investors to sta­bi­lize oper­a­tions. This amount of short­fall could be detri­men­tal to many orga­ni­za­tions, lead­ing to mar­ket share loss or even bankruptcy. 

“You can all think of oth­er exam­ples that you have lived, that you have seen,” Viz­caino told the audi­ence. ​“The bot­tom line here is not hav­ing a prop­er align­ment between the vision of the busi­ness and the enablers of that vision to become true, to become rel­e­vant, to become real, is real­ly dangerous.”

Com­pet­i­tive Advan­tage Will Come from Enter­prise Architecture 

Enter­prise archi­tec­ture pro­vides the struc­ture and gov­er­nance for effec­tive dig­i­tal trans­for­ma­tion, lay­ing the ground­work for inno­va­tion such as AI models. 

Viz­caino said that SAP will soon have suc­cess­ful­ly estab­lished an AI copi­lot mode to pro­vide expan­sive process automa­tion, help­ing work­ers make deci­sions more quick­ly and effec­tive­ly. Even as com­pa­nies enhance automa­tion in this way, lead­ers’ core respon­si­bil­i­ties will large­ly remain the same: cre­at­ing busi­ness, sus­tain­ing busi­ness, and cre­at­ing com­pet­i­tive advan­tage. It’s how they go about doing this in the age of AI that will look a bit different. 

Many dif­fer­ent types of assets and tech­niques opti­mized by data and AI — as enabled by enter­prise archi­tec­ture — will become more valu­able mov­ing for­ward. Viz­caino list­ed the fol­low­ing ways that enter­prise archi­tec­ture can cre­ate value: 

• Com­plex deci­sion-mak­ing in ambigu­ous, high-stakes scenarios

• Orches­trat­ing AI investments

• Inno­va­tion: break­ing con­ven­tion­al thinking

• Eth­i­cal AI gov­er­nance and human oversight

• Ensur­ing fair­ness, trans­paren­cy, and account­abil­i­ty in AI

• Man­ag­ing human-AI work­force integration

• Dis­in­for­ma­tion secu­ri­ty and fraud management

Inno­va­tion Will Come from Orches­trat­ing AI Investments

While lever­ag­ing AI to auto­mate process­es is a key area of cur­rent inno­va­tion, future inno­va­tion will involve opti­miz­ing the ways in which those AI invest­ments are orchestrated. 

Enter­prise archi­tec­ture is not a skill, but rather a dis­ci­pline that should be infused across all depart­ments of an orga­ni­za­tion, Viz­caino added. The silos of the past should be avoid­ed in the ways that orga­ni­za­tions restruc­ture their oper­a­tions; instead, incul­cat­ing an enter­prise-archi­tec­ture mind­set with­in all busi­ness units can serve to bring stake­hold­ers from across an orga­ni­za­tion togeth­er in ser­vice of shared tech­nol­o­gy goals. 

With stake­hold­ers aligned, they’ll be able to bet­ter under­stand where to inte­grate AI into var­i­ous aspects of the busi­ness. As AI becomes more read­i­ly avail­able, the race is on to find busi­ness appli­ca­tions for this emerg­ing capa­bil­i­ty and achieve a com­pet­i­tive advantage.

Data Will Be the Most Valu­able Asset 

As com­pa­nies seek to enable AI, ensur­ing the qual­i­ty of enter­prise data through data cleans­ing, val­i­da­tion, and gov­er­nance efforts will only become more important. 

Sim­i­lar­ly, seman­tics are impor­tant; it’s through under­stand­ing data objects, struc­tures, and rela­tion­ships between objects that AI can actu­al­ly dri­ve process improve­ments. Cre­at­ing these rela­tion­ships also helps reduce the risk of hal­lu­ci­na­tions and oth­er issues with AI outputs. 

Also essen­tial to opti­miz­ing the business’s use of AI is a wide­spread under­stand­ing of data strat­e­gy, how to orga­nize data, where to put up guardrails, and where AI can be used in the first place. This lev­el of under­stand­ing from every leader across an enter­prise will bring val­ue, but it can be led by those who strive to attain an enter­prise-archi­tec­ture mindset. 

Eth­i­cal Over­sight of AI

Of course, not every deci­sion will be up to AI. There will have to be human lead­ers ensur­ing fair­ness and account­abil­i­ty in a com­pet­i­tive mar­ket, achiev­ing goals while align­ing to eth­i­cal standards. 

Viz­caino said that AI can­not process every­thing that a busi­ness does. In hir­ing, for exam­ple, AI can look at a candidate’s CV to find any red flags, but it can’t intu­it the soft skills of a per­son or why they might have tak­en a few years off from work. Cul­tur­al fit will have to be deter­mined by hir­ing man­agers, too. Try­ing to leave every­thing up to AI sim­ply won’t work. 

Train­ing the work­ers of the future — espe­cial­ly lead­ers — will also have to involve edu­ca­tion around moral and eth­i­cal chal­lenges that AI can present, he said. These lead­ers will learn to think, as opposed to mem­o­rize. In fact, Viz­caino said that being able to solve eth­i­cal chal­lenges as they relate to AI in busi­ness will set the lead­ers of tomor­row apart from oth­ers, becom­ing a high­ly val­ued skill with­in enter­pris­es embrac­ing the future of innovation.