AI in everyday work: Which uses help?

Start with a frequent, manageable task whose result you can assess. A clearly defined draft is easier to review at the outset than a complex decision with many unknown assumptions. The following selection concerns generative AI, which creates or edits content.

If reviewing the result takes longer than doing the task in the existing way, the use case may be unsuitable or may need different preparation. A technically impressive result is not yet a good work process.

Describe the task, source material, target audience and desired result. Add which details must remain unchanged and what the AI should do when information is missing. An instruction such as ‘Write a good email’ leaves key decisions open.

A fictional example for an internal update is: ‘Use these approved bullet points to write a short message to our project team. State the decision, the reason and the next step. Do not add facts. Mark information that is still missing as open points.’ Then add only material whose use is permitted in your chosen system.

Work in manageable steps. Check the content and structure first, then the wording. If an answer is wrong, consider whether information was missing, the task was too broad or the system is not reliable enough for the purpose. Adding more text to an instruction does not fix every problem.

Clarify this with the responsible people in your company for the specific systems and tasks you use. A general rule such as ‘AI may be used’ says nothing about whether customer data, internal documents or personal information may be entered.

For initial exercises, you can use fictional or explicitly approved content. Do not make superficial changes to real data and then simply declare it anonymous. Context can also make identification possible. Also consider who can access the content entered and the results generated.

The webinar series from the Bavarian Association of Chambers of Commerce and Industry connects practical applications with topics including data protection and checking results. These questions should also form part of your workflow when introducing AI. This article does not replace a legal or security review of your specific use.

Before the experiment, define what makes a result usable. For an internal message, that might mean accurate facts, clear language and an unambiguous next step. For a summary, what matters is whether crucial qualifications are retained. Polished wording alone is not a sufficient review criterion.

Record the whole process: preparation, input, follow-up questions, revision and expert approval. A quick first draft can become costly again through extensive rework. Conversely, an experiment that initially takes considerable effort can be useful later if a reliable process develops.

Compare tasks that are as similar as possible. If the task done with AI was easier than the previous one done without it, the time difference tells you little. Document the difficulty and the quality required as well. A transparent comparison is enough to get started; you do not need a promise of savings with false precision based on a few individual experiments.

Access only answers the question of whether someone can open a tool. Other conditions must be in place for use: a suitable task, permitted data, sufficient confidence in using it and time to experiment.

One person may be convinced of AI's value and still find no suitable task. Another knows the use cases but does not know which information they are allowed to use. Someone else has tried it, needed to correct a lot and returned to their previous way of working. These situations call for different actions.

That is why ‘Why aren't you using AI?’ is often too broad a question. Ask more specifically: Which task did you try? Where did uncertainty arise? What rework was needed? What would make the next attempt easier? Avoid a climate in which every instance of non-use is treated as resistance. For some tasks, the existing approach is simply more sensible.

Match the action to the obstacle. If rules are missing, another tool demonstration will only help to a limited extent. If people know the rules but lack suitable examples, a joint exercise using a typical task may help. If individuals get stuck, they may need a specific person to contact.

For a Lunch & Learn, consider what people should be able to do differently afterwards. ‘Everyone has heard a presentation’ describes attendance. ‘Participants can independently prepare an approved task and check the result’ describes a desired ability in the workflow. Only then can you determine what you need to observe later.

Keep the use case present afterwards without overwhelming employees with constant appeals. A short review of a real experiment can be more valuable than the next collection of general AI news. Name unsuitable use cases too. This helps the team understand the limits of use.

Distinguish three levels. First: Which prerequisites have been put in place, such as access and rules? Second: Which ways of working are actually being tried or used repeatedly? Third: What changes are occurring in quality, effort and the support people experience?

Team feedback complements technical usage figures. A login does not yet show helpful use. Conversely, a positive assessment does not yet show measurable time savings. The guide Measuring AI adoption in your team describes suitable questions and these limitations in more detail.

For a simple overview, you can record the following for each use case: task, responsible person, permitted material, review step, observed benefit and next point to clarify. Track developments at the level needed to manage the rollout. A rollout needs direction, not a ranking of individual employees.

Choose a manageable use case and first review together whether it is suitable. Clarify approval, define a usable result and try the task with appropriate material. Document helpful results as well as errors and rework.

Use the review as a decision point: continue, change the process or end the experiment. You can then choose a second use case or share the first example with the team. The sequence should follow work needs, not the number of features available in a tool.

If you introduced AI some time ago, you do not need to start from scratch. Collect the use cases so far, narrow down the biggest obstacles and review the next action specifically. The AI adoption page shows how regular feedback can accompany this process.

EngageBeat Fokus is not an AI assistant for texts or tasks. It helps those responsible accompany the rollout with recurring team feedback and review actions more specifically. Manage an AI rollout with feedback or try EngageBeat Fokus: after 28 free days, your chosen subscription begins unless you cancel in time.

If tools are available but hardly used, the decision guide for low AI use helps distinguish clarity, capability and opportunity.