Tips for Admins & Managers

This guide helps you support your organization safely and effectively in its first steps with AI.

1. Create structures for exchange

A successful AI rollout depends on knowledge spreading quickly throughout the organization. That doesn’t happen on its own — you need places and formats where employees can ask questions and share results.

AI channel (Teams/Slack): A central hub for questions, tips, prompts, examples, and success stories. Employees immediately see: “I’m not alone,” and benefit from each other. Ideally, the channel is moderated by power users or the AI team.

Regular Q&A or show-and-tell sessions: Short video meetings (e.g., 20–30 minutes every two weeks) where employees can ask questions live. Spontaneous learning often happens here: one team shows how they automate meeting notes, another how they improve customer emails.

AI knowledge space in Confluence or Notion: A small knowledge base with internal prompts, best practices, guidelines, or example workflows. The clearer the documentation, the easier it is for other teams to adopt AI.

Optional: a small AI core team: 2–4 champions from different departments who exchange insights, gather issues, organize trainings, and serve as visible internal contacts.

2. Identify typical use cases

The best way to start is with clear, everyday tasks that deliver immediate value. This motivates teams and reduces hesitation.

Quick wins in daily work:

Reduce routine work: Standard texts, meeting notes, minutes, checklists, translations.
Improve communication: Emails in the right tone, polished customer responses, internal announcements.
Boost creativity: Brainstorming, campaign ideas, text refinement, social media posts.
Analysis & structure: Organizing content, extracting information, creating summaries.

Concrete examples by department:

HR: Writing job ads, creating interview guides, comparing applicant profiles.
IT: Explaining code, analyzing error messages, documenting scripts, generating unit tests.
Project management: Structuring tasks, deriving to-dos from meetings, evaluating project risks.
Sales & marketing: Drafting proposals, describing customer segments, generating campaign ideas.
Customer service: First drafts for replies, simplifying knowledge base articles.

👉 Tip: Ask every team: “Which tasks annoy you the most?” These pain points often hide the biggest AI leverage.

👉 Tip 2: Create 2–3 example prompts and assistants for each department. This dramatically lowers the barrier to getting started.

3. Success principles for everyone

A few simple rules help employees use AI safely and confidently.

Think critically: AI is an assistant, not an autopilot. Results need to be reviewed — especially numbers, legal content, or sensitive information.

Work iteratively: The first answer is rarely perfect. Ask follow-up questions, provide examples, adjust perspectives — that’s how quality emerges.

Build routines: The more employees experiment, the more natural AI becomes in daily work. “Aha moments” (“This saved me 90 minutes!”) often come only after a bit of practice.

Make successes visible: Share short stories in the AI channel like “10 minutes instead of 2 hours” or screenshots of effective prompts. This boosts momentum.

Start small, think big: Improve a few clear use cases first — then expand step by step. This avoids overwhelm and unrealistic expectations.

4. Strengthen motivation and culture

Cultural aspects often matter more than technology.

Support power users: Some people quickly discover creative applications. Use them as multipliers through small internal workshops, demos, or tips in the channel.

Competitions or challenges: A playful start can generate a lot of energy: “Who builds the best assistant?” “Who automates the most annoying process?” Small prizes are enough — recognition often matters even more.

Leadership goes first: When leaders test prompts themselves and share examples, it sends a clear message: AI is a tool for everyone — not just for tech enthusiasts.

Encourage a healthy error culture: No one is perfect on day one. People need a space where “bad first tries” are totally fine. This encourages experimentation and creativity.

5. Communicate security — build trust in the KI-Workplace

Many employees hesitate to use internal data in AI workflows. Admins can play a key role by actively explaining why the KI-Workplace is a safe environment for sensitive and personal information.

Clear message: The KI-Workplace is designed so that sensitive data is protected and internal policies are followed. No one can see user conversations or assistants — not other users, not admins, not the KI-Workplace team, and not any external provider.

What kind of data does this include? Examples of sensitive or personal information commonly used in everyday work:

Names of employees or customers
Contract data and offer details
HR data such as salaries or résumés
Descriptions of products and services
Code
Financial data
Plans and strategies

Conclusion

A successful AI introduction is less a technical project and more a cultural one. If you foster exchange, highlight small wins, and support people in taking incremental steps, AI quickly moves from an “experiment” to a productive everyday tool.

With clear structures, pragmatic use cases, and motivating leadership, AI becomes a sustainable asset that brings real relief and measurable value.