AI has long been available in many companies—but its effectiveness varies greatly. Marketing works with variants and analyses on a daily basis, IT tests agents, and other departments limit themselves to occasional isolated trials. Leadership calls for change without consistently leading by example in adopting these new ways of working.

This creates an internal AI divide: not between people with and without licenses, but between departments that integrate AI into their work and those that simply have it made available to them.

Global differences are reflected within the company

The report “Global AI Adoption in 2025” by the Microsoft AI Economy Institute described a growing international divide in the use of generative AI in early 2026. For companies, the internal divide is often more immediate: teams with the same technical resources achieve very different results.

My comparison with global trends is my application of this concept to organizational practice. It shows why access alone does not guarantee productive use.

Typical Patterns in Practice

• Marketing uses AI every day for copywriting, campaigns, analytics, and variations.
• IT is testing code support, agents, and automation.
• Accounting and administrative departments use individual functions, often without process integration.
• Some power users create personal workflows that are largely unknown to the rest of the team.
• Management measures usage, but does not consistently measure quality, turnaround time, or customer value.

This means that productivity gaps arise even within the same organization. These gaps widen when knowledge remains informal and successful work practices are not shared.

Five Steps to Bridge the Internal AI Gap

1. Create transparency

Which teams use which tools for which tasks? Where do time savings, improved quality, or new risks arise? A sober assessment of the current situation is more helpful than mere license statistics.

2. Work with specific use cases

General training provides guidance. Real results come from examining and improving actual processes: quote processing, information retrieval, documentation, analysis, or customer communication.

3. Make governance effective

Data protection, tool selection, data classes, access permissions, and responsibilities must be clear enough so that employees can actually act in accordance with them.

4. Role-Based Training

Management, departments, administrative staff, IT, and controlling each require different skill sets. Not everyone needs to learn the same prompts. However, everyone must understand their responsibilities within the new workflow.

5. Measure Impact, Not Activity

Suitable metrics include, for example, turnaround time, error rate, quality, follow-up inquiries, training duration, and customer benefit. The number of prompts or tokens used primarily indicate activity.

AI Adoption Is Organizational Development

An AI license is infrastructure. Productive use results from clear expectations, the right skills, adapted processes, and visible leadership by example. That is why AI implementation is neither a purely IT project nor a one-time training initiative.

The better question isn't, "Should we use AI?" but rather: Where is AI already creating measurable value—and where is there still a digital void?

This article was inspired by my LinkedIn post from May 12, 2026.