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AI Help and Consulting: From Problems to Solutions
Artificial intelligence can speed up work with information, documents and repetitive tasks. The outcome, however, depends on choosing the right technology, the quality of the data and how responses are checked.
A tool for occasionally summarizing a document is not selected in the same way as one for regularly analysing thousands of records or automating workflows connected to company systems. Start with the task, not the model name.
Start with the actual problem
For an ambiguous task, first establish the input format, expected output and evaluation method. “We want to use AI” specifies neither an acceptable quality level nor an error tolerance. For correspondence, you can measure the time spent drafting a response. For document analysis, you can measure the share of information identified correctly.
Effective AI consulting begins by distinguishing three situations: an off-the-shelf tool can do the job, a data integration is required, or the task is better handled without a generative model. This prevents deploying a complex system for a simple operation.
The tool, the data and result verification
A language model produces responses from the context provided. It is not a factual database automatically updated whenever a document changes. Where an answer must rely on internal materials, a mechanism is needed to retrieve relevant passages and identify their sources.
Choosing AI tools requires assessing more than response quality: data limits, cost per task, export options and access controls also matter. Testing on a realistic sample is more informative than an impressive demonstration using a single question.
AI adoption among businesses
Eurostat reported that in 2025, 20.0% of European Union enterprises with at least 10 employees used AI technologies. In Poland, the share was 8.4%, a difference of 11.6 percentage points. These figures measure adoption, not effectiveness or the position of microbusinesses.
When considering AI implementation, distinguish the use of a tool from measurable outcomes such as time savings, more complete data or fewer manual corrections.
From experiment to repeatable process
A basic test needs typical cases, edge cases and an unambiguous definition of failure. After changing a model or instruction, repeat the same cases to distinguish a genuine improvement from a lucky result.
In sensitive workflows, action approval, decision logs and the ability to revert to an earlier procedure are important. Automation can be introduced in stages: recommendation first, then partial execution, and only later actions without manual approval.
AI in European Union enterprises
Sources
- Eurostat, “20% of EU enterprises use AI technologies”, 11 December 2025, dataset isoc_eb_ai.