Date: 21.01.2026 (2026-01-21)
Julia Schwieger (21.01.2026) Context: • https://www.linkedin.com/in/juliaschwieger/ • Head of Finance at KONUX • Transform Railway Operations for a Sustainable Future | We are hiring! WEF Technology Pioneer #Sustainability #AI #IoT • MUC • My message: • Ich bin auf Ihr Profil gestoßen, da Sie einen super interessanten Finance-Background in verschiedenen Tech-Companies haben. • • Aufgrund Ihrer Erfahrung würde ich gerne Ihr Feedback zu unserem Ansatz und Ihren Herausforderungen einholen (es wäre kein Verkaufsgespräch!). • • Wir entwickeln eine KI-Finance-Software für das Monthly Closing mit dem Ziel: Closing-Zeiten und manuellen Aufwand um ~50% reduzieren: • a) Fehlende Rechnungen & Informationen automatisch intern & extern einsammeln (basierend auf Reconciliation) • b) Approvals risikobasiert anstatt mit starren Freigabeprozessen • c) Dem Approver immer den kompletten Kontext mitgeben (Budget, Zweck, Historie, Abweichungen, etc.), damit Entscheidungen in Sekunden getroffen werden können • • Wir automatisieren heute bereits die End-to-end-Verbuchung von Eingangsrechnungen (inkl. 4-Way-Match, Kostenstellen, Formal- und Fraud-Check). • • Reply: • das hört sich gut an - können wir gerne machen. • Ich hätte spontan diesen Donnerstag zwischen 9 und 12 Uhr Zeit. Würde dir da was passen?
Notes: • Notes: • Break -> no job atm • Intro to the current team of KONUS possible • Challenges: • AI is very fragmented -> no broad solutions • KONUX: • Accounting was super good -> she did not have to do a lot • AP: one person and a working student -> not a big pain point at KONUX - but can still be improved • Other companies: AP accounting was • Different systems -> difficult to get data from all systems -> data is fragmented • Sometimes different data for the same KPI -> difficult to find the reason • Solution: • HR tools (e.g. Personio); Kartenmanagement; AP invoices • Controlling: • Most teams work with Excel -> tools are too standardised -> there is a lot of potential • Scenario analysis • It takes very long to update Excel models - especially if they are large • Relevant data pools for Actuals: • Main: Accounting (from Buchhaltung Tools) • HR data • Input from different teams: • Revenue -> you need to understand it well (you talk to them OR template) • Cost splits • Cash data (treasury system like Agicap) • Sometimes qualitative KPIs • Konux: mix of finance and business • Budgets: • • Finance should work strategically • Leverage n8n for integration between our APi and external API • Approval flows & PRs • Starre Prozesse • Suddenly person has to approve everything • Important: only the things that have to be approved should wait for approval - apart form that it should go through to not have a roadblock -> makes the process faster • E.g. CEO has to approve -> takes a long time • Konux: • Script that was written internally • Spendesk has an approval process • Objections for Slack process: • Which requirements for companies with ISO certification • For Employee is good • Head of Finance has to be user friendly • 5 to 10 per week - rest was done by the team • No Problem • Always compare approval with budget -> if yes: its fine; if not: ask questions • If sth is approved in budget you can direct approve • Automation: • Accounting: a lot of Grundrauschen -> a lot of manual work • She also does Controlling and Reporting • Biggest objections / challenges in his daily life? • • Most valuable flows for him? • Automation • Insights -> faster gathering of data and create insights • In a supportive way • Context: team setup? internal and external? • • Which tasks are the biggest pain? With magic wand -> what would he solve? • • Which tasks would he focus on? • • Do they already have automations? Which ones? How well do they work? • • Which companies would he target? • • General feedback to pitch? •