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",{"_key":868,"_type":168,"marks":869,"text":870},"e1d82d8f20a6",[798],"Automatisierte Tests",{"_key":872,"_type":168,"marks":873,"text":874},"227fe018a198",[]," liefen durchgehend mit und prüften, ob Anfragen dem richtigen Themengebiet zugeordnet und die Antworten technisch korrekt erzeugt wurden.",[],{"_key":877,"_type":164,"children":878,"markDefs":882,"style":172},"ed012232db32",[879],{"_key":872,"_type":168,"marks":880,"text":881},[],"Aus den Ergebnissen aller drei Verfahren entstanden konkrete Optimierungsschritte, die Einfluss auf den technischen Aufbau oder die Wissensbasis hatten. Schnell zeigte sich, dass sich ein Großteil der problematischen Antworten nicht durch technische Anpassungen verbessern ließ. Die Problemstellung lag hauptsächlich in den Inhalten: widersprüchliche Aussagen zwischen einzelnen Seiten, veraltete Informationen, die niemand mehr aktualisiert hatte, und Inhalte ohne Kontext. Solche Stellen führten dazu, dass Aussagen in den Antworten falsch wiedergegeben wurden oder ganz fehlten.",[],{"_key":884,"_type":164,"children":885,"markDefs":890,"style":172},"7499165cbf4e",[886],{"_key":887,"_type":168,"marks":888,"text":889},"5fe4729cb267",[],"Diese Präzision in der Fehlersuche unterscheidet Evaluation von einer klassischen Content-Prüfung. Wer die Website betreut, kennt in der Regel die eigenen Inhalte, aber nicht automatisch jede fehlerhafte Stelle. Evaluation macht jede einzelne sichtbar. Der wirksamste Hebel für bessere Antworten lag deshalb meist in der Wissensbasis: den Website-Inhalten, auf die der Chatbot zugreift. Und eine bessere Wissensbasis wirkt nicht nur im Chatbot: Dieselben Inhalte werten auch KI-Suchsysteme aus (vgl. Abb. 1).",[],{"_key":892,"_type":893,"alt":894,"asset":895,"caption":897},"7e831c6dff74","newsImage","Der Evalutionskreislauf für die Chatbot-Evaluierung",{"_ref":896,"_type":223},"image-0c371fb74d3b1acc8a5b30c479171ad5a106d31b-3072x1680-png","Abb. 1: Der Evaluationskreislauf: Erkenntnisse aus den Tests verbessern die Wissensbasis und damit sowohl den Chatbot als auch die Quelle für KI-Suchen. (Quelle: Experience One)",{"_key":899,"_type":164,"children":900,"markDefs":905,"style":172},"0a023af592fd",[901],{"_key":902,"_type":168,"marks":903,"text":904},"454edb38f69d",[798],"Welche Effekte das konkret für GEO bringt",[],{"_key":907,"_type":164,"children":908,"markDefs":913,"style":172},"10bd195e5f08",[909],{"_key":910,"_type":168,"marks":911,"text":912},"42b6af943e2e",[],"Jede Lücke, jeder unklare Begriff und jede veraltete Information, die eine Evaluation aufdeckt, stört auch andere KI-Systeme, die auf denselben Content zugreifen. Umgekehrt gilt: Jede Anpassung, die den eigenen Chatbot verbessert, macht das Unternehmen zu einer verlässlicheren Quelle für generative Suchsysteme.",[],{"_key":915,"_type":164,"children":916,"markDefs":937,"style":172},"fb5535e852a8",[917,921,925,929,933],{"_key":918,"_type":168,"marks":919,"text":920},"e65bd487e9e9",[],"Genau hier setzt ",{"_key":922,"_type":168,"marks":923,"text":924},"be363e52e995",[798],"Generative Engine Optimization (GEO)",{"_key":926,"_type":168,"marks":927,"text":928},"1bad6c474d41",[]," an: Sie bereitet Inhalte für KI-gestützte Such- und Antwortsysteme auf, nicht für klassische Rankings. ",{"_key":930,"_type":168,"marks":931,"text":932},"cf1c6cdab652",[798],"SEO",{"_key":934,"_type":168,"marks":935,"text":936},"b980c814db34",[]," zielt auf Klicks und Positionen, GEO auf die Aufnahme in eine generierte Antwort – oft ganz ohne Website-Besuch.",[],{"_key":939,"_type":164,"children":940,"markDefs":945,"style":172},"9b155ad3962e",[941],{"_key":942,"_type":168,"marks":943,"text":944},"35efa7b7f3df",[],"Drei Effekte lassen sich dabei besonders deutlich beobachten:",[],{"_key":947,"_type":164,"children":948,"level":254,"listItem":957,"markDefs":958,"style":172},"7e2a5c317487",[949,953],{"_key":950,"_type":168,"marks":951,"text":952},"31fac81be446",[798],"Konsistenz statt Widerspruch:",{"_key":954,"_type":168,"marks":955,"text":956},"1eb75b904d06",[]," Evaluation deckt widersprüchliche oder veraltete Aussagen und Begriffe auf. Für KI-Systeme sind solche Inkonsistenzen ein Störfaktor. KI-Systeme bevorzugen eindeutige, konsistente Fakten gegenüber branchen- und unternehmensspezifischer Markensprache.","number",[],{"_key":960,"_type":164,"children":961,"level":254,"listItem":957,"markDefs":970,"style":172},"605f7a1cf3db",[962,966],{"_key":963,"_type":168,"marks":964,"text":965},"931adf9367d0",[798],"Antwortformate statt Fließtext:",{"_key":967,"_type":168,"marks":968,"text":969},"9f810f758280",[]," Aus den Tests mit Nutzer:innen entstehen häufig klar formulierte Frage-Antwort-Paare, Glossare und Schritt-für-Schritt-Erklärungen – genau die Formate, die generative Suchsysteme bevorzugt zitieren.",[],{"_key":972,"_type":164,"children":973,"level":254,"listItem":957,"markDefs":982,"style":172},"07b5d17b2e4a",[974,978],{"_key":975,"_type":168,"marks":976,"text":977},"5f97c05a54db",[798],"Belegte Fakten statt Behauptungen:",{"_key":979,"_type":168,"marks":980,"text":981},"c1aed5bc5d8c",[]," Wo Evaluation Lücken aufdeckt, wird Content nachgeschärft und mit Quellen unterlegt. Das erhöht die Wahrscheinlichkeit, in einer generierten Antwort zitiert zu werden.",[],{"_key":984,"_type":164,"children":985,"markDefs":990,"style":172},"10e285007b0c",[986],{"_key":987,"_type":168,"marks":988,"text":989},"a205e287d9bf",[],"Wenn sich die Suche in den Antwortbereich der KI-Systeme verlagert, erhalten Nutzer:innen dort eine Antwort statt einer Linkliste. Ob sie danach noch eine Website besuchen, hängt davon ab, ob diese in der Antwort als Quelle genannt wird. Entscheidend ist, dass Inhalte eindeutig, aktuell und widerspruchsfrei sind.",[],{"_key":992,"_type":164,"children":993,"markDefs":998,"style":172},"0c1302509281",[994],{"_key":995,"_type":168,"marks":996,"text":997},"6373974bfb1f",[],"Vor diesem Hintergrund sind FAQs, Glossare und Schritt-für-Schritt-Erklärungen mehr als gute Redaktionspraxis. Entstehen sie aus einer sauberen Chatbot-Evaluation, helfen sie Unternehmen, sich in den Antworten der KI-Suche zu behaupten.",[],{"_key":1000,"_type":164,"children":1001,"markDefs":1006,"style":172},"b356d9495aa4",[1002],{"_key":1003,"_type":168,"marks":1004,"text":1005},"74bc16536d8b",[798],"Fazit: Evaluation früh einplanen",[],{"_key":1008,"_type":164,"children":1009,"markDefs":1014,"style":172},"656760944d54",[1010],{"_key":1011,"_type":168,"marks":1012,"text":1013},"16758c996c0a",[],"Ein selbst entwickelter Chatbot bringt mehr als Unabhängigkeit vom Funktionsumfang einer Fertiglösung. Er erlaubt es, systematisch zu testen, aus den Ergebnissen zu lernen und den eigenen Content laufend zu verbessern – mit Wirkung über den Chatbot hinaus. Als reiner Qualitätscheck kurz vor dem Launch wird Evaluation unterschätzt. Wer heute über einen eigenen Chatbot nachdenkt, sollte Evaluation deshalb von Anfang an einplanen.",[],{"_key":1016,"_type":164,"children":1017,"markDefs":1022,"style":172},"e31681031cd6",[1018],{"_key":1019,"_type":168,"marks":1020,"text":1021},"d5eb6e8fd767",[798],"Möchten Sie wissen, wie ein evaluationsgetriebener Chatbot auch die Sichtbarkeit Ihres Unternehmens in der KI-Suche verbessert?",[],{"_key":1024,"_type":1025,"href":1026,"label":1027,"reference":66},"d0f3cacb4339","cta","mailto: business@experienceone.com","Sprechen Sie mit uns",{"contactPerson":1029},{"_type":1030,"copyContent":1031,"person":1032,"title":1065},"contactPerson","department",{"_createdAt":1033,"_id":1034,"_rev":1035,"_system":1036,"_type":1039,"_updatedAt":1040,"department":1041,"name":1048,"position":1049,"sanityImage":1055,"tenant":39},"2026-09-08T09:50:59Z","abd5dfda-6cea-4080-8936-11a63e0c5878","S5EFnJjn0OdXSc45PwCFLe",{"base":1037},{"id":1034,"rev":1038},"g0HYAqMVNkHt5H2w9Uy5bP","person","2026-10-12T12:57:36Z",[1042,1046],{"_key":1043,"_type":1044,"language":13,"value":1045},"a794fed517145db77b890bc664b59486","internationalizedArrayStringValue","Platform Program Management",{"_key":1047,"_type":1044,"language":759,"value":1045},"5337d228d5195953c4fa08e6f45b2717","Hannes Frey",[1050,1053],{"_key":1051,"_type":1044,"language":13,"value":1052},"e5ea2055a9dc081afee0b1ee009510d3","Senior Platform Program Manager",{"_key":1054,"_type":1044,"language":759,"value":1052},"f1930eb1581aa215f825c713be471af9",{"_type":218,"alt":1048,"aspectRatioXs":245,"asset":1056,"crop":1058,"hotspot":1060,"title":1048},{"_ref":1057,"_type":223},"image-a88e620258e0c2c461bea3faf17e18e654a5e0a1-2383x3177-jpg",{"_type":249,"bottom":1059,"left":250,"right":250,"top":250},0.2633789957267945,{"_type":252,"height":1061,"width":1062,"x":1063,"y":1064},0.7366210042732055,0.9782790085113057,0.48913950425565283,0.36831050213660277,"Autor",{"_key":1067,"_type":1068,"aspectRatioSm":1069,"aspectRatioXs":1069,"contactPerson":66,"content":66,"headline":1070,"imageSliderPersons":66,"items":1071,"persons":66,"reference":66,"sidebar":66},"6430c9fe2e09","teaserGroup","8x9","Weitere spannende Themen",[1072,1453],{"_createdAt":1073,"_id":1074,"_key":1075,"_ref":1074,"_rev":1076,"_system":1077,"_type":1080,"_updatedAt":1081,"category":1082,"content":1084,"language":13,"reference":66,"seo":1420,"slug":1438,"teaser":1440,"tenant":39,"title":1452},"2026-05-04T07:00:12Z","1b9e49fd-21c1-4fce-ab9b-37d795f2d75d","1be580340ea1","3AqyM4eZIuX1fS18ttGfZz",{"base":1078},{"id":1074,"rev":1079},"nWdHZ1zqJ7jFG6zlWezf41","project","2026-11-02T14:17:47Z",{"slug":1083},{"_type":450,"current":21},[1085,1089,1113,1126,1149,1172,1193,1216,1254,1274,1316,1351,1385,1395],{"_key":1086,"_type":198,"headline":1087,"overline":1088},"ec04f9bcde6f","KI-Beratung für 8 Millionen Versicherte und alle Interessierten: Agentic AI im Kundendialog ","BARMER | KI-Chatbot ",{"_key":1090,"_type":1091,"impacts":1092,"sanityImage":1110},"91fd94c28357","kpiStage",[1093,1099,1105],{"_key":1094,"_type":1095,"headline":1096,"headlinePostfix":1097,"subline":1098},"191971dc6b57","impact","8"," Millionen","Versicherte",{"_key":1100,"_type":1095,"headline":1101,"headlinePostfix":1102,"headlinePrefix":1103,"subline":1104},"fd6fd9944acd","65k","Konversationen",">","in den ersten 4 Monaten",{"_key":1106,"_type":1095,"headline":1107,"headlinePrefix":1108,"subline":1109},"d5e9c0e2b2ed","1.000","+","Konversationen pro Tag",{"_type":218,"aspectRatioSm":777,"aspectRatioXs":343,"asset":1111},{"_ref":1112,"_type":223},"image-c24c2668bbcb38c058cec742c2e35b3c188ae013-3600x1500-jpg",{"_key":1114,"_type":1115,"content":1116,"headline":1125},"0dde9b6ca94a","twoColumnsTextFluid",[1117],{"_key":1118,"_type":164,"children":1119,"markDefs":1124,"style":172},"a1a5e8cbd1e2",[1120],{"_key":1121,"_type":168,"marks":1122,"text":1123},"cd7ab5c378ce",[],"Von Versicherungsfragen über Prävention bis zu digitalen Gesundheitsanwendungen: Der KI-Chatbot der BARMER macht das Gesundheitswissen der Krankenkasse direkt zugänglich. Das System besitzt Kontextverständnis, gibt selbstständig relevante Antworten und steuert komplexe Anfragen. Wenige Tage nach Launch wurden bereits mehrere Tausend Konversationen über den KI-Chatbot geführt – der Chatbot war ab Tag 1 produktiv im Einsatz.\nDas neue Serviceangebot soll zukünftig die Beraterinnen und Berater im Kundenservice entlasten und dabei unterstützen, Anfragen effizienter und besser zu priorisieren. Mit dieser Lösung verfügt die BARMER nun über die technische Plattform sowie die interne Kompetenz, ihre KI-basierten Services eigenständig weiterzuentwickeln.",[],"Der digitale Beratungsassistent für barmer.de – immer erreichbar, direkt informiert",{"_key":1127,"_type":214,"headline":1128,"sanityImage":1129,"text":1132},"5d6fe6fb3631","Präzise Antworten, klare Grenzen ",{"_type":218,"aspectRatioXs":245,"asset":1130},{"_ref":1131,"_type":223},"image-7026db7b3c7440f6da97c78803f703457a24f12d-1560x1560-jpg",[1133,1141],{"_key":1134,"_type":164,"children":1135,"markDefs":1140,"style":172},"c995896dae1b",[1136],{"_key":1137,"_type":168,"marks":1138,"text":1139},"1102c01f3125",[],"Der BARMER KI-Chatbot beantwortet Fragen rund um Kranken- und Pflegeversicherung, Gesundheitsleistungen, Mitgliedschaft und viele weitere Themen der Krankenkasse – direkt auf barmer.de, rund um die Uhr. Seine Wissensbasis speist sich aus dem gesamten Website-Content der BARMER, der strukturiert aufbereitet und regelmäßig aktualisiert wird. Anders als ein klassisches FAQ-System versteht der Assistent den Gesprächskontext: Folgefragen werden nicht isoliert bewertet, sondern im Zusammenhang des Konversationsverlaufs beantwortet.",[],{"_key":1142,"_type":164,"children":1143,"markDefs":1148,"style":172},"2a621c629d2a",[1144],{"_key":1145,"_type":168,"marks":1146,"text":1147},"5f1a97b432cd",[],"Ebenso klar definiert ist, was er nicht beantwortet: Individuelle Fallauskünfte und medizinische Diagnosen liegen bewusst außerhalb seines Zuständigkeitsbereichs – entsprechende Anfragen werden erkannt und an weiterführende Informationsseiten oder fachkundige Beraterinnen und Berater weitergeleitet. In Notfallsituationen verweist der Chatbot direkt auf geeignete Hilfsangebote. Die Nutzeroberfläche führt Nutzende dabei zielgenau zum passenden Kontaktpunkt.",[],{"_key":1150,"_type":214,"headline":1151,"sanityImage":1152,"text":1155},"69f33c5e94bc","Von Intent-Logik zu agentischer Entscheidung",{"_type":218,"aspectRatioXs":245,"asset":1153},{"_ref":1154,"_type":223},"image-23d172ab37e1d2010858cf63af6c03ee3b10b108-1560x1560-jpg",[1156,1164],{"_key":1157,"_type":164,"children":1158,"markDefs":1163,"style":172},"d508dc76584e",[1159],{"_key":1160,"_type":168,"marks":1161,"text":1162},"366b31266649",[],"Der ursprüngliche Plan sah einen Intent-basierten Chatbot vor: vordefinierte Pfade, manuell gepflegte Antwortstrukturen, klare Routing-Logiken. Für den Wissensumfang einer großen Krankenkasse ist dieses Modell nicht tragfähig: zu viele Themen, zu viele Varianten, zu viel Pflegeaufwand. Das System wäre ein zweites CMS geworden – mit dem gleichen strukturellen Problem: wachsende Komplexität, sinkende Beherrschbarkeit.",[],{"_key":1165,"_type":164,"children":1166,"markDefs":1171,"style":172},"4a3b380d4e31",[1167],{"_key":1168,"_type":168,"marks":1169,"text":1170},"6fda0dd7234c",[],"Die Entscheidung war konsequent: Intent-Erkennung und Routing werden nicht mehr regelbasiert gelöst, sondern durch einen agentischen Ansatz. Ein Large Language Model (LLM) klassifiziert die Nutzeranfragen und leitet diese im Kontext des Gesprächsverlaufs an den für die Aufgabe zuständigen Agenten weiter – immer entlang der implementierten Guardrails, die in einem regulierten Umfeld wie dem der BARMER zwingend erforderlich sind. Das Ergebnis: Ein System, das mit wachsender Wissensbasis nicht schwerfälliger, sondern präziser wird.",[],{"_key":1173,"_type":1174,"author":1175,"sanityImage":1181,"text":1184},"f7c568d51348","quoteContainer",{"description":1176,"name":1177,"sanityImage":1178},"Chatbot Product Owner, BARMER","Apolline Tabourot-Emig",{"_type":218,"alt":1177,"aspectRatioXs":245,"asset":1179,"title":1177},{"_ref":1180,"_type":223},"image-bb112ce6908657de1419c3a41a13f17b7ce3f052-1278x852-webp",{"_type":218,"alt":1177,"aspectRatioSm":220,"aspectRatioXs":417,"asset":1182,"title":1177},{"_ref":1183,"_type":223},"image-4b5915911c7b3567b8c1d37f5eccd9fd273db09f-3600x2026-jpg",[1185],{"_key":1186,"_type":164,"children":1187,"markDefs":1192,"style":172},"4c010e63af97",[1188],{"_key":1189,"_type":168,"marks":1190,"text":1191},"791f9c83a270",[],"Früh war klar: Ein Chatbot, der wirklich beraten soll, braucht Zugriff auf die gesamte Wissensbasis. Ein klassischer, Intent-basierter Ansatz war hier nicht zielführend. Gemeinsam mit Experience One haben wir eine Lösung umgesetzt, die wir heute eigenständig weiterentwickeln können.",[],{"_key":1194,"_type":214,"headline":1195,"sanityImage":1196,"text":1199},"99dc51ff9188","Infrastruktur aufbauen, Fähigkeiten etablieren ",{"_type":218,"aspectRatioXs":245,"asset":1197},{"_ref":1198,"_type":223},"image-a2045a195e29a9c94f3862dd2097021392a7d8a0-1560x1560-jpg",[1200,1208],{"_key":1201,"_type":164,"children":1202,"markDefs":1207,"style":172},"9b7ab4057e12",[1203],{"_key":1204,"_type":168,"marks":1205,"text":1206},"e89d450d3b0b",[],"Das Ziel des Projekts war nicht allein der Launch des Chatbots – sondern sicherzustellen, dass die BARMER auch nach Projektabschluss in der Lage ist, das System eigenständig weiterzuentwickeln. Was sonst in Workshops und Seminaren nachgeholt wird, war hier von Anfang an Teil des Projekts.",[],{"_key":1209,"_type":164,"children":1210,"markDefs":1215,"style":172},"ad2ffb9a879f",[1211],{"_key":1212,"_type":168,"marks":1213,"text":1214},"d8e72920a586",[],"Das bedeutet: das interne Team der BARMER von Beginn an einbinden und befähigen, nicht nur informieren und das Ergebnis übergeben. Technisches Setup und Architekturherleitung wurden gemeinsam erarbeitet. Durch Pair Programming, Wissenstransfer-Workshops und gemeinsame Architekturentscheidungen entstand nachhaltiges internes KI-Know-how nicht im Nachgang, sondern direkt im Projekt, bis zum eigenständigen Betrieb in der Linie.",[],{"_key":1217,"_type":214,"headline":1218,"text":1219,"vimeo":1236},"a7f5f5521c04","Vom Sprint zum Prototyp: Optimierung als eigentliche Kernarbeit ",[1220,1228],{"_key":1221,"_type":164,"children":1222,"markDefs":1227,"style":172},"1271db667c18",[1223],{"_key":1224,"_type":168,"marks":1225,"text":1226},"19c41aaf5edb",[],"KI-Entwicklung folgt einer anderen Logik als klassische Softwareprojekte. Definierter Code produziert vorhersehbares, identisches Verhalten – ein LLM nicht. Sein Verhalten kann sich bei jeder Anfrage verändern, abhängig von Kontext, Formulierung und Datenlage. Das System muss deshalb kontinuierlich test-driven auf allen Ebenen gleichzeitig weiterentwickelt werden: vom Experience Design über das Modell und die Infrastruktur bis hin zur Knowledge Base.",[],{"_key":1229,"_type":164,"children":1230,"markDefs":1235,"style":172},"fcfb3f24cd6d",[1231],{"_key":1232,"_type":168,"marks":1233,"text":1234},"00348a1ae788",[],"Wir haben diese Realität zur Methode gemacht, um schnellstmöglich den ersten testbaren Prototypen zu liefern und in die Evaluation zu kommen. Was in klassischen Projekten 80 % Entwicklung und 20 % Test ist, lief bei der Entwicklung des Chatbots umgekehrt. Interne Testgruppen lieferten frühzeitig belastbares Feedback. Der stufenweise Rollout erfolgte zunächst für 950 Mitarbeitende der Telefongeschäftsstelle; anschließend wurden alle BARMER-Mitarbeitenden ongeboardet. 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Der Haupterfolg bei diesem Projekt war aber nicht alleine der Launch, sondern die BARMER zu befähigen, danach eigenständig weiterzumachen.",[],{"_key":1275,"_type":1276,"columns":1277,"headline":1315,"numbered":216},"5504f3b5bc7b","threeColumnsText",[1278,1291,1303],{"_key":1279,"_type":1280,"copy":1281,"headline":1290},"e97755005759","threeColumnsTextColumn",[1282],{"_key":1283,"_type":164,"children":1284,"markDefs":1289,"style":172},"d449c88a0907",[1285],{"_key":1286,"_type":168,"marks":1287,"text":1288},"7b1536a6fcc3",[],"KI-Systeme lassen sich zu Projektstart nur schwer zu Ende planen. Sie müssen test-driven zu einem Go Live-fähigen Produkt erprobt werden. Das klingt einfach, verändert aber grundlegend, wie ein Projekt aufgesetzt sein muss: welche Kompetenzen von Anfang an am Tisch sitzen, wie Entscheidungen getroffen werden und wann. Wir bringen die Erfahrung mit, diese Arbeitsweise von Beginn an zu etablieren und begleiten Teams dabei, diesen Ansatz zu verankern.",[],"Iteration statt Konzeption  ",{"_key":1292,"_type":1280,"copy":1293,"headline":1302},"9a885f43ce23",[1294],{"_key":1295,"_type":164,"children":1296,"markDefs":1301,"style":172},"859f71a7b29d",[1297],{"_key":1298,"_type":168,"marks":1299,"text":1300},"bf58b173e2b2",[],"Ein funktionierender KI-Chatbot ist das Ergebnis von Dutzenden ineinandergreifender Entscheidungen: Architektur, Wissensstruktur, Evaluationsdesign, Nutzerakzeptanz, Infrastruktur, Compliance. 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Heute zeige ich es einfach.“","2026-10-06",{"_key":1470,"_type":775,"sanityImage":1471},"cc63c6c00c14",{"_type":218,"alt":1472,"aspectRatioSm":777,"aspectRatioXs":343,"asset":1473,"title":1475},"Tobias Mihalek, BARMER.KI, im Interview zu \"Owning the Vibe\"",{"_ref":1474,"_type":223},"image-77c364e7e9e8d1743cd6c374b248e6d59e351841-5400x2250-png","Tobias Mihalek, BARMER.KI",{"_key":1477,"_type":782,"content":1478,"sidebar":1583},"9e3bf8557227",[1479,1487,1495,1503,1511,1519,1527,1535,1543,1551,1559,1571,1576],{"_key":1480,"_type":164,"children":1481,"markDefs":1486,"style":172},"d698808b349b",[1482],{"_key":1483,"_type":168,"marks":1484,"text":1485},"1a233d545a94",[],"Wie verändert Vibe Coding, wie wir arbeiten und entscheiden? Im Interview spricht Tobias Mihalek, Senior Projektmanager bei der BARMER.KI, über die Potenziale des aktuellen „KI-Vibes“ und wie es gelingt, in hochregulierten Branchen die Brücke zwischen gesetzlichen Leitplanken und innovativer Technologie zu schlagen.",[],{"_key":1488,"_type":164,"children":1489,"markDefs":1494,"style":172},"6f7699801b79",[1490],{"_key":1491,"_type":168,"marks":1492,"text":1493},"952afb14d461",[798],"Herr Mihalek, Sie begleiten seit 20 Jahren große Projekte – wie sind Sie zu einem der treibenden Köpfe hinter der BARMER.KI geworden?",[],{"_key":1496,"_type":164,"children":1497,"markDefs":1502,"style":172},"e395e50bc7d0",[1498],{"_key":1499,"_type":168,"marks":1500,"text":1501},"fcda4f3c4f7d",[],"Ich hatte das Glück, mich bewusst dafür entscheiden zu können. Zuvor hatte ich bereits Berührung mit vielen neuen Technologiethemen. Auch privat bin ich schon lange ein Early Adopter, ob bei Elektroauto oder Hausautomation. Dieser intrinsische Forscherdrang hilft mir, in Neuem die Chancen zu sehen – auch wenn man dafür in Vorleistung gehen muss. Bei der BARMER.KI hat die Mission zwei Seiten: neue Technologie ausprobieren und einen Pfad finden, sie in der Organisation zu verankern. Das braucht jemanden, der nicht nur technologisch überzeugt ist, sondern die Überzeugungskraft auch nach innen ausstrahlt. Ich kenne das Business von der Kundenbetreuung bis zu den Legacy-Systemen – das schafft Vertrauen. Und ich glaube daran, dass man transformative Themen nur mit Wir-Entscheidungen trägt, für die man gemeinsam einsteht.",[],{"_key":1504,"_type":164,"children":1505,"markDefs":1510,"style":172},"339d949c64f9",[1506],{"_key":1507,"_type":168,"marks":1508,"text":1509},"ef592da4ae1e",[798],"Wie nutzen Sie KI und was davon hätte sich vor einem Jahr noch niemand vorstellen können?",[],{"_key":1512,"_type":164,"children":1513,"markDefs":1518,"style":172},"3e346b4506c7",[1514],{"_key":1515,"_type":168,"marks":1516,"text":1517},"62aa2766ba75",[],"Im Geschäftsalltag nutze ich KI als Berater, zur Bildgenerierung und in Projekten zunehmend als Coding Agent. Was ich mir persönlich noch vor einem Jahr nicht hätte vorstellen können? Dass ich ohne klassische Coding-Expertise eigenständig Webanwendungen auf die Beine stellen kann. Früher brauchte man für ein Mockup oder einen ersten Prototyp mehrere Wochen und ein Team. Heute kann ich eine Vision im Sparring mit der KI viben und in wenigen Stunden sichtbar und erlebbar machen.",[],{"_key":1520,"_type":164,"children":1521,"markDefs":1526,"style":172},"3c0d0dbd3d92",[1522],{"_key":1523,"_type":168,"marks":1524,"text":1525},"e98957711ba4",[798],"Hat Vibe Coding Ihre Art Produkte zu denken und priorisieren verändert?",[],{"_key":1528,"_type":164,"children":1529,"markDefs":1534,"style":172},"9e43465845cc",[1530],{"_key":1531,"_type":168,"marks":1532,"text":1533},"36e0388a36c7",[],"Die Frage, ob eine Idee oder ein Projekt funktionieren kann, lässt sich wesentlich schneller beantworten. Vibe Coding minimiert die Hürde, Dinge einfach auszuprobieren. In meiner bisherigen Arbeit wurde oft lange darüber diskutiert, ob etwas überhaupt umsetzbar ist. Ich bin von Natur aus jemand, der ein „das geht so nicht“ erst glaubt, wenn er selbst daran gescheitert ist. Ein Beispiel: Für ein aktuelles Projekt musste ich prüfen, ob eine OCR-Strecke zur Analyse von Quittungsdokumenten technisch machbar ist. Ich habe an einem Wochenende einen privaten Server aufgesetzt, n8n und Claude genutzt und den Kernprozess erprobt. Es hat funktioniert. Mit diesem Micro-Mocking, das zeigt, dass der Kern der Anforderung erfüllbar ist, überzeugt man Fachabteilungen und Entscheidende wesentlich schneller als mit jeder Präsentation.",[],{"_key":1536,"_type":164,"children":1537,"markDefs":1542,"style":172},"64ff21c77193",[1538],{"_key":1539,"_type":168,"marks":1540,"text":1541},"c41ddcd53a07",[798],"Wie halten Sie diesen Explorationsdrang und konkrete Unternehmensziele zusammen, ohne den Fokus zu verlieren?",[],{"_key":1544,"_type":164,"children":1545,"markDefs":1550,"style":172},"2fbf7fe1e6c3",[1546],{"_key":1547,"_type":168,"marks":1548,"text":1549},"edb2a2e6c7a5",[],"Forschung ist explizit Teil unseres Auftrags bei der BARMER.KI. 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