senn-techHow AI-ready is your company?
Seven dimensions, five minutes, honest clarity. The evaluation runs entirely in your browser — no data is stored or sent.
100% computed in the browser — no data transfer.
How available, structured and maintained is your data?
Do you have concrete, prioritized use cases for AI?
How much AI and IT know-how is in your team?
How modern and flexible is your IT infrastructure?
How clearly are data-protection and compliance requirements defined?
How well are your processes documented and digitized?
Is there budget and leadership backing for AI?
What the check measures
The AI Readiness Check looks at seven dimensions to gauge how ready your company is for putting AI into production: the quality and availability of your data, concrete prioritized use cases, the know-how in your team, the state of your IT infrastructure, how clearly data-protection and compliance questions are settled, the maturity of your processes, and leadership commitment. You rate each dimension on a scale of 1 to 5; from that, an overall maturity score in percent is calculated. One thing matters most: AI readiness is not purely a technical question — clean data and clear use cases decide success at least as much as GPUs and models do.
The four maturity levels
Your result falls into one of four levels. Each one tells you what to work on next:
Lay the foundation (0–39%)
The basics aren't in place yet: data is scattered, unstructured or poorly maintained, and there are no clearly prioritized use cases. For an SME, that means tidying up first — consolidating data sources, documenting processes and defining one or two realistic use cases. Only then does the technology pay off. We help you make a clean start before budget flows into pilots that fail on the data layer.
Ready for a pilot (40–64%)
The base is there: data is partly usable, first use cases are visible, and there's basic understanding in the team. Now is the right time for a focused pilot use case with measurable value — small enough to go live quickly, but relevant enough to show impact. The goal is a solid proof point that builds trust internally.
Ready to scale (65–84%)
Your data base, infrastructure and processes are robust, and you have leadership backing. At this stage it's no longer about whether, but how: rolling AI out into production, integrating it cleanly into existing workflows and setting up reliable operations. What matters now is standardization, monitoring and a clear plan for turning one pilot into several production applications.
AI-mature (85–100%)
Your company is well ahead — AI is no longer an experiment but part of day-to-day business. Now it's about optimization, in-house operation and data sovereignty: running models and the stack on your own hardware, keeping cost and latency under control and holding sensitive data in-house in a GDPR-compliant way. This is exactly where corporate LLM stacks on your own GPUs come in.
The check is no substitute for consulting, but it gives you an honest snapshot in five minutes. If you want to interpret your result or plan the next concrete step, we're happy to discuss it directly.