Real Busi­ness Ben­e­fits with Arti­fi­cial Intelligence

Real Busi­ness Ben­e­fits with Arti­fi­cial Intelligence

Arti­fi­cial intel­li­gence offers sig­nif­i­cant oppor­tu­ni­ties for orga­ni­za­tions of all sizes. How­ev­er, real­iz­ing real ben­e­fits requires iden­ti­fy­ing the right use cas­es — which is not always straightforward.

The best results are achieved when AI is not viewed mere­ly from a tech­no­log­i­cal per­spec­tive, but with a focus on the organization’s core process­es. This way, it is pos­si­ble to iden­ti­fy the tasks where AI can tru­ly add val­ue — and, at best, even man­age entire process­es independently.

For AI solu­tions to become a nat­ur­al part of an organization’s every­day oper­a­tions, both deep busi­ness under­stand­ing and strong tech­ni­cal exper­tise are required. This calls for seam­less col­lab­o­ra­tion between the tech­nol­o­gy provider and the client. Often, it is pre­cise­ly the lack of this coop­er­a­tion that keeps the promised ben­e­fits of gen­er­a­tive AI from being real­ized in practice.

Islet is a trust­ed AI part­ner: from cre­at­ing an AI strat­e­gy and iden­ti­fy­ing use cas­es, to the tech­ni­cal imple­men­ta­tion of AI agents, their exten­sive uti­liza­tion, and advanced ana­lyt­ics. We turn into prac­tice the meth­ods by which AI agents are inte­grat­ed into the core process­es of your orga­ni­za­tion, work­ing along­side peo­ple and deliv­er­ing real busi­ness val­ue. Also, we help our clients imple­ment advanced ana­lyt­ics solu­tions, such as machine learn­ing-based fore­cast­ing and clas­si­fi­ca­tion models.

Arti­fi­cial intel­li­gence offers sig­nif­i­cant oppor­tu­ni­ties for orga­ni­za­tions of all sizes. How­ev­er, real­iz­ing real ben­e­fits requires iden­ti­fy­ing the right use cas­es — which is not always straightforward.

The best results are achieved when AI is not viewed mere­ly from a tech­no­log­i­cal per­spec­tive, but with a focus on the organization’s core process­es. This way, it is pos­si­ble to iden­ti­fy the tasks where AI can tru­ly add val­ue — and, at best, even man­age entire process­es independently.

For AI solu­tions to become a nat­ur­al part of an organization’s every­day oper­a­tions, both deep busi­ness under­stand­ing and strong tech­ni­cal exper­tise are required. This calls for seam­less col­lab­o­ra­tion between the tech­nol­o­gy provider and the client. Often, it is pre­cise­ly the lack of this coop­er­a­tion that keeps the promised ben­e­fits of gen­er­a­tive AI from being real­ized in practice.

Islet is a trust­ed AI part­ner: from cre­at­ing an AI strat­e­gy and iden­ti­fy­ing use cas­es, to the tech­ni­cal imple­men­ta­tion of AI agents, their exten­sive uti­liza­tion, and advanced ana­lyt­ics. We turn into prac­tice the meth­ods by which AI agents are inte­grat­ed into the core process­es of your orga­ni­za­tion, work­ing along­side peo­ple and deliv­er­ing real busi­ness val­ue. Also, we help our clients imple­ment advanced ana­lyt­ics solu­tions, such as machine learn­ing-based fore­cast­ing and clas­si­fi­ca­tion models.

Islet’s AI Services

AI Strat­e­gy

A typ­i­cal goal of an AI strat­e­gy is to cre­ate a clear, step-by-step roadmap for the client orga­ni­za­tion to ful­ly real­ize the broad ben­e­fits of arti­fi­cial intel­li­gence. AI strat­e­gy includes an analy­sis of the cur­rent state, a con­crete descrip­tion of the tar­get state, a plan for how to achieve it, a com­par­i­son of essen­tial alter­na­tive tech­nol­o­gy sce­nar­ios, archi­tec­tur­al descrip­tions, a roadmap, and poten­tial project plan­ning. Defin­ing the strat­e­gy also involves iden­ti­fy­ing and plan­ning for staff train­ing needs, as well as out­lin­ing the require­ments and scope of the AI gov­er­nance mod­el and meth­ods at a high lev­el. If need­ed, we also orga­nize AI-relat­ed infor­ma­tion ses­sions dur­ing the strat­e­gy work.

In addi­tion to archi­tec­ture and tech­ni­cal solu­tions, the AI strat­e­gy address­es orga­ni­za­tion­al struc­ture, ways of work­ing, and process­es. Tru­ly lever­ag­ing AI requires a deep under­stand­ing of which orga­ni­za­tion­al process­es and their stages can be assigned to an AI agent.

AI Strat­e­gy

A typ­i­cal goal of an AI strat­e­gy is to cre­ate a clear, step-by-step roadmap for the client orga­ni­za­tion to ful­ly real­ize the broad ben­e­fits of arti­fi­cial intel­li­gence. AI strat­e­gy includes an analy­sis of the cur­rent state, a con­crete descrip­tion of the tar­get state, a plan for how to achieve it, a com­par­i­son of essen­tial alter­na­tive tech­nol­o­gy sce­nar­ios, archi­tec­tur­al descrip­tions, a roadmap, and poten­tial project plan­ning. Defin­ing the strat­e­gy also involves iden­ti­fy­ing and plan­ning for staff train­ing needs, as well as out­lin­ing the require­ments and scope of the AI gov­er­nance mod­el and meth­ods at a high lev­el. If need­ed, we also orga­nize AI-relat­ed infor­ma­tion ses­sions dur­ing the strat­e­gy work.

In addi­tion to archi­tec­ture and tech­ni­cal solu­tions, the AI strat­e­gy address­es orga­ni­za­tion­al struc­ture, ways of work­ing, and process­es. Tru­ly lever­ag­ing AI requires a deep under­stand­ing of which orga­ni­za­tion­al process­es and their stages can be assigned to an AI agent.

AI Plat­form

AI plat­form pro­vides the tech­ni­cal foun­da­tion for sus­tain­able, con­trolled, and secure scal­ing of AI usage. Islet’s tech­nol­o­gy archi­tec­tures cov­er all crit­i­cal aspects of AI devel­op­ment — from user man­age­ment and secu­ri­ty, to sys­tem and data plat­form inte­gra­tions, as well as envi­ron­ment monitoring.

Agent appli­ca­tions for dif­fer­ent use cases

Autonomous Agents as Part of Core Busi­ness Processes

Company’s oper­a­tions are built on process­es, which in turn con­sist of indi­vid­ual tasks or steps. Autonomous agents are AI appli­ca­tions that per­form one or more of these tasks, pre­vi­ous­ly han­dled by humans. These agents are cus­tomized appli­ca­tions equipped with mul­ti­ple skills, or tools. With these tools autonomous agents can, with­in the lim­its of their assigned tasks, for exam­ple use dif­fer­ent sys­tems, access inter­nal infor­ma­tion, and com­mu­ni­cate with peo­ple via email or chat.

Assis­tive Agents to Ease Employ­ees’ Dai­ly Work

Assis­tive agents are not respon­si­ble for entire work steps them­selves; instead, their role is to make employ­ees’ work eas­i­er. These appli­ca­tions can, for exam­ple, facil­i­tate infor­ma­tion retrieval, pro­vide an alter­na­tive user inter­face to a sys­tem, or ana­lyze giv­en doc­u­ments. Typ­i­cal­ly, assis­tive agents are acti­vat­ed by a user prompt, such as a mes­sage received in Teams chat.

Gen­er­al-Pur­pose Chatbots

Gen­er­al-pur­pose chat­bots are use­ful con­ver­sa­tion­al part­ners sim­i­lar to Chat­G­PT, but restrict­ed for use with­in the orga­ni­za­tion with secu­ri­ty in mind. They do not have access to inter­nal infor­ma­tion and do not pos­sess spe­cial tools. Gen­er­al-pur­pose chat­bots can be used, for exam­ple, for ad hoc brain­storm­ing, draft­ing var­i­ous types of con­tent, and per­form­ing lan­guage translations.

Agents Embed­ded in Busi­ness Applications

Agents built into busi­ness appli­ca­tions — either sup­port­ing human work or per­form­ing entire work steps — help auto­mate parts of process­es and pro­vide trans­paren­cy, sit­u­a­tion­al aware­ness, and sug­ges­tions to sys­tem users.

CHECK OUT OUR AI WEBI­NA­RS: (in Finnish)

Advanced Ana­lyt­ics Solu­tions to Sup­port Decision-Making

Edis­tynyt ana­lyti­ik­ka pitää sisäl­lään ”per­in­teiset” koneop­pimiseen perus­tu­vat ratkaisut, jos­sa suuren datamäärän avul­la ensin koulute­taan matemaat­ti­nen malli, jota sit­ten käytetään esimerkik­si aikasar­jaen­nus­tamiseen, tapah­tu­mien luokit­telu­un tai poikkeamien ennustamiseen.

Edis­tyneen ana­lyti­ikan pro­jek­tit ovat luon­teeltaan data sci­ence ‑pain­ot­teisia, hyvin iter­ati­ivisia ja tutkivia. Pro­jek­tien keskiössä on tarvit­ta­van koulu­tus­datan keräämi­nen ja pros­es­soin­ti mallin tarkku­u­den mak­si­moimisek­si. Koulute­tut mallit tulee tyyp­il­lis­es­ti käyt­töön osak­si data-alus­tan ELT ‑pros­es­sia, tai niitä käytetään API-rajap­in­nan kaut­ta jonkin sovel­luk­sen toimesta.

Advanced ana­lyt­ics includes “tra­di­tion­al” machine learn­ing solu­tions, where a math­e­mat­i­cal mod­el is first trained using large amounts of data. This mod­el is then used, for exam­ple, for time series fore­cast­ing, event clas­si­fi­ca­tion, or anom­aly prediction.

Advanced ana­lyt­ics projects are typ­i­cal­ly data sci­ence – ori­ent­ed, high­ly iter­a­tive, and explorato­ry in nature. At the core of these projects is the col­lec­tion and pro­cess­ing of the nec­es­sary train­ing data to max­i­mize mod­el accu­ra­cy. The trained mod­els are usu­al­ly deployed as part of the data platform’s ELT (Extract, Load, Trans­form) process, or they are uti­lized via an API inter­face by an application.

AI Func­tion­al­i­ties Embed­ded in Systems

Agent-to-agent Inte­gra­tion: Smart Col­lab­o­ra­tion from SAP Joule to Microsoft Copilot

Agent-to-agent inte­gra­tion opens a new era of intel­li­gent col­lab­o­ra­tion as SAP Joule and Microsoft Copi­lot join forces. SAP’s busi­ness process­es and Microsoft 365 tools com­mu­ni­cate seam­less­ly with each oth­er. Joule and Copi­lot share infor­ma­tion, auto­mate rou­tines, and gen­er­ate insights togeth­er. This enables orga­ni­za­tions to make smarter deci­sions, as SAP data and con­text are brought direct­ly into the Copi­lot inter­face. Users can act faster and with more con­fi­dence when the nec­es­sary infor­ma­tion is always available.

Inte­gra­tion between agents frees up time by automat­ing repet­i­tive tasks across SAP and Microsoft envi­ron­ments. The user expe­ri­ence improves as employ­ees can lever­age the best fea­tures of both sys­tems in one smooth work­flow. This is not just inte­gra­tion but a leap towards a tru­ly intel­li­gent and con­nect­ed work environment.

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