Most AI projects don't fail because of the technology. They fail because nobody clarified beforehand where AI actually makes a measurable difference. First a tool gets bought, then a use case is sought to justify it. That's the wrong order.
Gasteiner Heilstollen approached it differently. Before any concrete solution was discussed, one question stood in the room: where exactly in the business does AI truly pay off, and where does it not? That's precisely what we conducted a structured AI process analysis for.
The result was not yet another tool. It was clarity: a complete technical concept with prioritized use cases and a profitability assessment that shows which investment pays off and which does not.
The starting point: when everything arrives at once
Gasteiner Heilstollen is a spa and health facility with a strictly timed business. Every two hours, a group enters the therapeutic gallery. And that is exactly the challenge: business doesn't flow evenly, it comes in waves.
Before every entry, many guests arrive at the same time. Bottlenecks emerge precisely in these windows. Not because the processes are fundamentally bad, but because the same team that is well staffed between entries reaches its capacity limit during the rush. Inquiries and guest care pile up abruptly, the workflows around guest administration become the bottleneck, and time is missing exactly when it matters most.
“The problem isn't the average utilization. The problem is the ever-identical windows in which everything happens at once.”
So the decisive question was not "How do we automate everything?" but "Where exactly does the load hurt the most, and can AI meaningfully absorb it?".
The approach: understand first, build second
Instead of arriving with a ready-made solution, we started with a workshop. Directly with the people who live these processes every day.
In this workshop, we mapped the relevant workflows together with the people involved. Who does what, where do things pile up, which tasks consume the most time during peaks? This step is indispensable because the real bottlenecks are rarely where you'd suspect them at first glance.
Clear focus areas crystallized from this analysis. The biggest potential lay in communication and guest administration, exactly where the timed rush before each entry creates the heaviest load.
The result: a technical and an economic concept
The result of the process analysis was a complete concept. No prototype, no demo, but a solid basis for decision-making. It consists of two parts.
1. A technical concept with concrete use cases. We translated the identified potential in communication and guest administration into concrete, implementable AI use cases. Each one describes what it delivers, how it works technically, and how it fits into the existing way of working. Prioritized by effort and impact, so it's clear where to start.
2. An economic concept. Every use case comes with an assessment of its economic benefit. What does implementation cost, what time savings or relief does it deliver, and when does the investment pay off? This turns a "we could do that someday" into a well-founded yes-or-no decision.
“In the end there is no AI wish list, but a calculation. Which use case delivers how much, and what does it cost.”
This gives Gasteiner Heilstollen something most businesses don't have at this point: a clear, prioritized roadmap showing where entering AI pays off and in which order to proceed.
Why the analysis comes before implementation
It's tempting to jump straight into implementation. A chatbot here, an automation there. The problem: without a sound analysis, you often invest in the wrong place and later wonder why the effect never materializes.
The AI process analysis reverses this order. First, understand where the real bottleneck is. Then, evaluate which solution is economically viable. And only then build.
For Gasteiner Heilstollen this means: when implementation starts, it starts with a use case everyone involved knows will pay off. No blind flight, no lost investment.
What this project shows
The project with Gasteiner Heilstollen is a good example of how the most valuable first step toward AI is often not the technology, but the clarity about where it should be deployed.
The key was the workshop with the people on site and the honest evaluation of which use cases are truly worthwhile. Not every conceivable AI application is a sensible one. Exactly this distinction is what a good process analysis delivers.
That is our approach at Soneo AI. We don't build AI demos. We first create clarity about where AI actually makes a difference in our customers' daily business, and then start exactly there.
Does your business hit its capacity limit during peak times? We'll look at your processes and tell you honestly where AI helps and where it doesn't. No obligation, no buzzwords.
Book a free initial consultationFAQ
What is an AI process analysis?
An AI process analysis examines the concrete workflows in a company and identifies where deploying AI delivers measurable value. The result is a prioritized concept with concrete use cases and a profitability assessment, instead of a hastily purchased tool without a matching use case.
Why is a process analysis worthwhile before implementing AI?
Because most AI projects don't fail because of the technology, but because investment happens in the wrong place. An upfront analysis ensures implementation starts with a use case that demonstrably pays off. That saves time and budget and prevents bad investments.
How does an AI workshop at Soneo AI work?
In the workshop, we map the relevant workflows together with the people who handle these processes daily. We identify where bottlenecks arise and which tasks consume the most time. On this basis, we derive concrete AI potential and evaluate it by effort and impact.
What does the technical and economic concept include?
The technical concept describes concrete, implementable AI use cases including how they work and how they integrate into the existing way of working. The economic concept adds a benefit assessment to each use case: implementation costs, expected relief, and the point at which the investment pays off.
Is AI also suitable for businesses with strictly timed peak periods?
That's often exactly where the potential is greatest. In businesses with recurring rush windows, bottlenecks arise precisely when the same team suddenly has to handle many times the number of inquiries. Because these peaks are predictable and always follow the same rhythm, AI can absorb them particularly well in areas like communication and administration, without permanently increasing headcount.
