Practical questions
Make sense of AI and automation
Clear explanations help you assess AI and automation options. Take these questions to your team to discuss a workflow, an answer or a planned test with a clearer purpose.
When are fixed rules enough?
In brief
When a task has clear identifying features, fixed rules can sort it in a traceable way. AI may be an option when freely written content is difficult to express as rules. Unclear and incomplete inputs need a visible path for review.
The seven-fictional-message example was run with fixed rules. Every entry remains open for human review; no live mailbox or automatic replies are involved.
Check rules and exceptions
Start with the categories and features of your workflow. Even a clearly categorised entry may need review. Suitable cases and requirements for the result help determine whether AI fits more difficult content.
Check with your team
- Which categories does the work actually need?
- Which features identify each category clearly?
- Which cases match several categories or none?
- Who reviews these exceptions and decides the next step?
How can you check a document answer?
In brief
A citation helps when the passage supports the statement in its proper context. Read the source alongside the question and check its currency and relevance. Missing support needs to stay visible so your team can decide how to use the answer.
The prepared document case uses five fictional documents. Two sources support a maintenance answer; two further questions remain open without sufficient support.
Check sources and context
A cited page alone does not make an answer correct. Compare the statement with the passage and consider its time period and circumstances. When important information is missing, a clarification is more useful than an apparently certain answer.
Check with your team
- Does the cited passage support the specific statement?
- Which qualifications or conditions appear in the same context?
- Are the documents current and approved for this question?
- What remains unsupported, and who will resolve the gap?
What should be clear before a pilot?
In brief
A useful test starts with a clear task and an outcome people can use. Typical cases, difficult exceptions and clear criteria make its findings useful. An initial assessment prepares that basis; the scope and delivery of a technical test are agreed separately.
A reasoned assessment considers data, benefit, effort, review and operation.
Prepare a pilot
Describe the current process and desired outcome. Clarify access, responsibilities and exception handling early. This makes it possible to bound a technical test and assess what its results mean together.
Check with your team
- Which task should be supported, and who will use the result?
- Which typical cases and difficult exceptions should be considered?
- Which documents or data may be used, and are they in a usable form?
- Who reviews the results, and what would count as useful?
- Which effort, corrections and open questions need to be visible?
Use AI locally, in the cloud or in a hybrid setup?
In brief
Whether AI runs locally, in the cloud or in a combination depends on the task, required quality, hardware, operation and data requirements. Suitable local models can handle many simple or well-defined tasks and process data that should remain internal. Hybrid means combining local processing selectively with an external service. That service is used only when desired, permitted and approved for the specific data. Local AI is not automatically cheaper than a cloud service; hardware, setup and ongoing operation belong in the comparison.
Potential benefits: Local processing can keep sensitive data within the controlled environment, reduce avoidable external transfers and lessen dependence on an internet connection. For more demanding analyses, suitable external models can be added selectively when their use is wanted and approved for the data concerned. This avoids sending every task to an external service. A demanding task does not automatically require the cloud; local processing also needs appropriate access controls and safeguards.
One possible example: search documents locally and prepare an initial draft. If useful and approved, a suitable excerpt can be sent to an external service for further analysis. If transfer is not permitted, the task stays local or is handled by a person.
Combine local and external AI deliberately
Use relevant cases to check whether local models meet the required quality. Agree explicitly when an external service may be used and who approves it. Local systems also need maintained models, controlled access and reliable operation. An outage or missing approval must never lead to a silent switch to the cloud.
Check with your team
- Which tasks and models deliver adequate output quality locally?
- Which data may leave the controlled environment, and for which services?
- Who authorises transfers, access and logging?
- What hardware, maintenance and operational responsibility are needed?
- What happens if an external service is unavailable or transfer is not permitted? Does the task stay local or wait for human handling?
What would you like to clarify?
Briefly describe your task, the current workflow and what you want to find out. An early idea is welcome too. Please do not send confidential documents in the first message.
What we discuss in our first conversation
We work remotely across the DACH region. Scope, terms and invoicing are set out in the individual offer.
See engagement formats and the focused entryEmail: contact@kevinklein.ai