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AI can make individual tasks in everyday work easier. Start where a concrete benefit can be assessed. What matters is protecting your company data, achieving reliable results and ensuring that the effort is proportionate to the benefit. Initial experience shows which further steps make sense.

A sensible starting point focuses on clearly defined, recurring tasks. Examples must fit the company, the data situation and the people responsible. They are areas of application—not blanket performance promises or customer references.
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Create initial drafts for recurring texts and then review them from a professional perspective.
Structure and summarize recurring content and prepare it for further processing.
Confidential content must not be transferred to external AI services without control or used for third-party training purposes. Before making a decision, the operating location, data flows, external interfaces, access rights, as well as storage and logging must be clarified. Which solution is suitable depends on the specific operating model reviewed.
Identify a suitable, clearly defined task with manageable risk.
Document current effort, quality and desired success criteria.
Test the solution within a limited scope with responsible people.
Compare quality, time required, risks and necessary controls in a transparent way.
Only decide on adaptation, expansion or discontinuation on the basis of the results.
Savings and quality improvements must be weighed against the costs of implementation, licenses, operation, training and ongoing monitoring. The comparison is based on the same task before and after deployment. Time that becomes available is not automatically a cash-effective cost saving; what matters is how it is actually used.
An initial conversation can narrow down the task, data environment, success criteria and possible next steps.
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