AI Solutions Day Vienna: What Enterprise Teams Are Actually Asking About AI
Elevon.io joined AI Solutions Day in Vienna, hosted by weXelerate, to present our approach to autonomous AI operations in front of enterprise leaders from banking, telecommunications, defence, and other regulated industries.
Martin Horvath (CEO) and Sofia Charvátová presented Elevon's platform and methodology in a format designed around real problem statements, not keynote theory. The session included roundtable discussions with immediate follow-ups and clear next steps.
Here is what we took away.
The data exists. The connections don't.
Customer service was a central theme. CRM, helpdesk, billing, product analytics, live chat. Enterprise teams already have the systems. The data is there. It is scattered across tools that do not talk to each other.
Martin demonstrated a concrete approach: combining these sources into a single operational view so that a customer service agent has everything ready before a call starts. Customer history, current situation, possible risks, recommended next steps. No switching between tools. No manual lookups.
The reaction in the room confirmed something we see repeatedly. Most teams do not lack technology. They lack a way to connect what they already have so it makes sense in real time.
AI projects stall when the process underneath is broken
One pattern came up in nearly every conversation. Companies select a use case because it performs well in a demo. Six months later, they are dealing with missing documents, incomplete data, and processes that were never properly documented in the first place.
Before any deployment, we push clients to answer three questions:
- Where exactly does this process start and end?
- Why is it failing today?
- How should it run years from now?
Only after these are answered does automation make sense. Skipping this step is the most common reason AI initiatives lose momentum.
ROI must be defined before you start
This was a recurring theme across the roundtables. Saved FTEs. Reduced processing time. Fewer errors per month. If the expected outcome is not specific before deployment begins, the investment becomes difficult to defend when leadership reviews results.
The enterprises in the room were not asking abstract questions about AI potential. They were asking practical, ROI-driven questions about specific processes.
Knowledge transfer is becoming urgent
Several companies raised a challenge that goes beyond efficiency. Senior experts are approaching retirement. Institutional knowledge leaves with them. For these organizations, AI is about preserving operational capacity while the workforce changes.
This reframes the conversation entirely. It moves from cost-cutting to continuity, from headcount reduction to knowledge retention.
What this signals for Elevon
The Austrian market showed us exactly the kind of environment where governed, production-ready AI delivers measurable value. Enterprise buyers here are specific in what they need, skeptical of hype, and focused on outcomes.
For Elevon, this reinforced a key principle: understanding local market context is essential, not optional. Austria is a strategic part of our Central European expansion, and the conversations in Vienna confirmed that the problems enterprises face here align directly with what Elevon Platform is built to solve.
Thank you to weXelerate for the space, the format, and for bringing the right people into the room.
If your organization is working through similar questions about deploying AI into real operations, get in touch.