Linking AI to measurable value
Edited by Aesvorn GmbH · 29 October 2026 · AV-F05
How to assess an AI use case through total effort, output quality and responsibility. A practical starting point for a limited pilot in an SME.
The current development
The OECD’s 2026 D4SME survey examines the opportunities and barriers associated with AI in SMEs. It draws on a non-representative sample of more than 2,000 SMEs across twelve OECD countries. Adoption is growing, while time, ongoing costs, skills and responsible integration remain challenges. It does not establish a Swiss adoption rate.
The Swiss Federal Data Protection and Information Commissioner explains that data protection law applies when AI processes personal data. The choice of a tool does not remove a company’s responsibilities.
Aesvorn’s assessment
The useful question is which clearly defined task AI can improve under controlled conditions. A faster first draft is not enough if checking and correcting it consumes the saving. Consider the entire workflow, acceptable quality and responsibility for approval.
Start with a limited, repeatable task and define who will use the result. Establish the permitted data before testing. Do not use confidential or personal information until the relevant requirements and permissions have been clarified.
A fictional time comparison
Without AI, preparation and drafting take six minutes and review takes two: eight minutes in total. With AI, preparation and drafting take three minutes and review takes four: seven minutes in total. The saving is one minute, not three.
The figures are fictional and do not predict productivity in another business. Quality must also be compared. A result that requires more rework, creates errors or cannot be approved may offer no useful benefit.
Define the pilot before choosing the tool
Task and expected benefit
Specify the starting material, desired output, user and measurable improvement. Compare with the current process on the same basis.
Data and provider
Identify what information may be used. Check access, storage, retention, deletion and any use of inputs to improve a provider’s models. Involve the appropriate data protection, legal or information security specialists where needed.
Human review
Name the person who checks factual accuracy, professional quality and suitability. Define which outputs require approval before internal or external use.
Success and stop criteria
Measure preparation, production, review and correction time together with output quality. Agree when the pilot will end, be adjusted or return to the previous process.
Evaluate and decide
Use a limited test period and a manageable set of tasks. Record the tool, configuration, inputs, reviewer and result so comparisons can be understood. Keep only information that may lawfully be retained.
At the review, decide whether the use case offers sufficient benefit at acceptable quality, cost and risk. Options include stopping, revising the workflow or expanding gradually with clear responsibility. Related checklist: AV-A06, Preparing and evaluating an AI pilot.
Sources and editorial responsibility
OECD: Empowering SMEs in the age of AI, 2026 D4SME Survey – published 13 April 2026; an international, non-representative survey.
FDPIC: AI and data protection – application of Swiss data protection requirements.
Sources accessed on 29 October 2026. The example and recommended approach are Aesvorn’s editorial content, not recommendations issued by these institutions.
Using this information
This article provides general information and non-binding practical guidance. It does not replace individual legal, tax, financial or social insurance advice and has not been reviewed by a lawyer. No guarantee is given for completeness, currency, accuracy, suitability in a particular case or legal effectiveness. Users must check and adapt the content to their circumstances and applicable requirements. To the extent permitted by law, Aesvorn GmbH excludes liability for losses arising from use. Mandatory liability, including for intent and gross negligence, remains unaffected. This notice does not guarantee a complete exclusion of liability. Examples are fictional business situations, not documented client cases.
Further resources
Free checklists and editable tools (documents in German)
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