AI in Microsoft 365 (part 1): Seizing Opportunities Without Losing Control

AI in Microsoft 365 Offers Unprecedented Opportunities. With tools like Copilot and AI agents, you can automate repetitive tasks, gain insights faster, and give your team more time for strategic work. But while many organizations dive in enthusiastically, significant risks often lurk beneath the surface. So how do you ensure AI not only adds value but also remains safe and manageable?

The Reality Behind the AI Promise

AI truly feels magical — and that feeling is largely justified. A well-configured AI agent can generate reports in seconds that would normally take hours. Copilot can perform complex data analyses that previously required specialist knowledge. That "magic" is real and powerful. 

But here’s the catch: that magic only works optimally for processes that are repetitive and structured. Think monthly reports, standard proposals, or status updates. That’s where the biggest gains lie, because AI excels at recognizing patterns and applying consistent logic.

The challenge is that many organizations want to deploy AI without first identifying which processes are suitable. They expect a single prompt to deliver a perfect result, regardless of the quality of the underlying data or clarity of instructions.

The Reality: What Can Go Wrong?

Scenario 1: The Confidential Salary List

An employee asks Copilot: "What does colleague X earn?" and actually gets an answer. Not because that person officially has access to HR documents, but because an old salary list from three years ago is sitting in a forgotten SharePoint folder.

Scenario 2: The Misleading Report

An AI agent needs to create a quarterly report but uses outdated draft files. The result: a professional-looking report full of incorrect figures. The client receives wrong information, and trust in AI declines.

Scenario 3: The Failed Adoption

After a year of Copilot licenses, it turns out many employees hardly use the tool. Early experiences were disappointing due to slow responses and irrelevant results. Once disappointed, people revert to their old ways of working.

Conclusion: You miss a crucial opportunity to truly embrace AI as an organization if you don’t have these basics in place.

Our Approach

At Harbers ICT, we start every AI project with a thorough analysis. We ask critical questions:

• Process Analysis: Which tasks do you repeat regularly following fixed patterns?
• Data Inventory: Where is which information stored, and how up-to-date is it?
• Access Rights: Who is really allowed to see what, and why?
• Value Potential: Where are the biggest time-wasters and frustrations?

We also guide employees with practical training and coaching so they learn how to use AI tools effectively in their daily work.

We focus primarily on repetitive processes such as monthly reports, status updates, or standard quotations—areas where AI can have the greatest impact. But the difference between success and disappointment often lies in how you instruct AI.

• Bad prompt: "Create a report"
  Result: A generic 2-page document without structure.
  
  
• Good prompt: "Create a quarterly report for client ABC with KPIs revenue, margin, and customer satisfaction according to our Q3 template, based on data from SharePoint site 'Client Reports 2025'"
  Result: A professional 8-page report with current data, charts, and concrete recommendations—ready to send.

Practical Roadmap

Step 1: Start with a thorough data audit and process analysis

Step 2: Define governance policies and access rights

Step 3: Begin with pilots on specific processes before scaling up