The Art of Artificial Intelligence: How dimedis Strategically Integrates AI into Everyday Life

AI as Part of Our Work Culture

At dimedis, we use AI in a tar­get­ed and struc­tured way — not just on an ad-hoc basis, but as an inte­gral part of our cor­po­rate strat­e­gy, with a focus on two key areas. In prod­uct devel­op­ment, AI sup­ports us with spec­i­fi­ca­tions, code reviews, and qual­i­ty assur­ance. At the same time, all of our approx­i­mate­ly 80 employ­ees use AI in their dai­ly work as a struc­tured tool with per­son­al­ized pro­files, cus­tom process­es, and defined workflows.

The key is that we don’t sim­ply adopt exist­ing frame­works. We use proven foun­da­tions and tai­lor them specif­i­cal­ly to our tech stack, our require­ments, and our work style. For us, this is pre­cise­ly what dis­tin­guish­es gener­ic AI out­put from tru­ly usable, high-qual­i­ty con­tent — and this applies to both soft­ware devel­op­ment and all oth­er areas of the company.

Strat­e­gy Over Chance

Behind this approach is a ded­i­cat­ed role… As AI Trans­for­ma­tion Lead, Process Coach, and Prod­uct Own­er, Maren Seufert dri­ves the entire AI method­ol­o­gy at dimedis. This includes process def­i­n­i­tions, hackathons, train­ing ses­sions, and the devel­op­ment of standards.

The deci­sion was not a spur-of-the-moment ini­tia­tive, but a care­ful­ly thought-out frame­work.

It’s also impor­tant that learn­ing isn’t just dic­tat­ed from above. Many col­leagues take the ini­tia­tive to devel­op their skills on their own, bring their own moti­va­tion to the table, and active­ly engage with the top­ics. It’s clear that this approach works well for us.

How our cus­tomers benefit

This has a direct impact, though in a way that might not be imme­di­ate­ly obvi­ous. The great­est ben­e­fit isn’t writ­ing code faster, but rather under­stand­ing and artic­u­lat­ing require­ments more effec­tive­ly. Prob­lems are described more pre­cise­ly, fol­low-up ques­tions are asked with greater focus, and spec­i­fi­ca­tions are auto­mat­i­cal­ly doc­u­ment­ed. This cre­ates a sig­nif­i­cant­ly stronger foun­da­tion for coor­di­na­tion even before imple­men­ta­tion begins.

At the same time, pro­to­types are cre­at­ed faster, allow­ing for more focused and con­cen­trat­ed cus­tomer feed­back. Cus­tomer-spe­cif­ic require­ments can be imple­ment­ed more pre­cise­ly because the start­ing point is clear­er. The sta­bil­i­ty of our solu­tions ben­e­fits from the fact that review process­es become more thor­ough and con­sis­tent. And the valu­able time saved is then direct­ed where it makes the biggest dif­fer­ence… toward solv­ing prob­lems rather than rou­tine tasks.

Inno­va­tion Requires Part­ners and Expertise

Inno­va­tion aris­es from col­lab­o­ra­tion and exper­tise. Prof. Dr. Alexan­der Lutz, a busi­ness infor­mat­ics expert spe­cial­iz­ing in big data and AI, is work­ing with us on the intel­li­gent pro­cess­ing of trade show data. Togeth­er, we com­bine sci­en­tif­ic meth­ods with our prac­ti­cal expe­ri­ence to sys­tem­at­i­cal­ly make data usable and devel­op new approach­es for exhibitors and orga­niz­ers.

On June 17, 2026, we invite you to con­nect cir­cle. Maren and Prof. Lutz will co-present a talk titled ​“Don’t Just Talk About AI. Learn How It Works.”

Was das für unsere Kun­den bedeutet

Schnellere Bug­fix­es und eine höhere Fea­ture-Tak­tung, weil Rou­tineauf­gaben weniger Zeit kosten und die gewonnene Kapaz­ität in echte Entwick­lungsar­beit fließt.

Sta­bilere Releas­es, weil sys­tem­a­tis­che, automa­tisierte Qual­i­ty Gates vor jedem Merge Fehler abfan­gen, bevor sie den Kun­den erreichen.

Mehr Zeit für indi­vidu­elle Anforderun­gen und direk­ten Kun­denkon­takt. Unsere Teams konzen­tri­eren sich auf Architek­tur und fach­liche Tiefe nicht auf Boilerplate.

Und per­spek­tivisch: datengetriebene Mehrw­erte aus der Zusam­me­nar­beit mit Alexan­der Lutz. Bessere Auswer­tung von Messe­dat­en. Neue Erken­nt­nisse für Aussteller und Ver­anstal­ter, die bish­er ver­bor­gen lagen.