Start AI assessment 
Many mid-sized companies want to use AI, but not at any price. What holds them back is rarely a lack of openness. It is risk: buying a tool, starting a pilot, investing weeks, and in the end still asking, what was the real payoff?
This is exactly where an AI potential analysis helps. It is not an AI project. It is a decision framework: where AI actually pays off, what delivers quick relief, and what should consciously be left out.
Typical pain points in SMEs
If you take an honest look, the same time drains show up in many companies:
- Recurring questions block skilled staff (“Where is this documented? What is the status? Who owns this?”).
- Knowledge is fragmented (emails, folders, ERP, individual people) instead of quickly accessible.
- Routine tasks consume expensive time: follow-ups, coordination, documentation, summarization.
- Unclear entry point: “Let’s test AI,” but without goals, measurement, or guardrails.
- Uncertainty around accountability and data protection: What can be used? What must be reviewed?
The result is often not no AI usage, but bad AI usage: tool sprawl, frustration, and no clear business value.
What is an AI potential analysis?
An AI potential analysis is a structured assessment of your workflows with one clear objective: make the strongest AI and automation levers visible and prioritize them.
Not: “What is possible?”
But: “What should we do first in your company to get real value?”
This focuses on processes, data flows, ownership, and typical bottlenecks — exactly the factors that decide success or failure.
What you get as concrete outcomes
A strong potential analysis does not end with “you could do this.” It delivers concrete outputs:
- Top 5 initiatives prioritized by impact and effort.
- Quick wins that create fast relief.
- A roadmap with sequencing, prerequisites, and next steps.
- Guardrails: which data, which approvals, which human checkpoints.
- A decision basis for management: invest or skip, based on clarity instead of gut feeling.
That is the difference between trying AI and using AI with purpose.
Short case study: Bringing email orders into ERP
A mid-sized manufacturing company received orders by email — sometimes plain text, sometimes with attachments, often missing required fields. Result: copy-paste work, follow-up questions, rework, and during peak periods everything depended on a few individuals.
In the potential analysis, the team did not start by searching for a tool. They first broke down the process: Which information is usually missing? Where do follow-up loops happen? Where is human review required?
This led to a prioritized action plan:
- Standardize required order information to reduce follow-up loops.
- Structure emails and attachments with explicit review checkpoints.
- Create a clean ERP handover instead of manual transfer.
The point: better decision confidence and visible relief, because the process is clarified before automation starts.
Conclusion
An AI potential analysis is the pragmatic way to start AI correctly in SMEs: not with tools, not with hype, but with clarity.
It answers the key question: Where does AI create real relief in your company, and what is the best first step?
If you want to identify where AI has the biggest leverage in your organization, start with a short initial consultation for a potential analysis. You share your biggest time drains, and we provide a first assessment of whether and where a potential analysis will pay off.