The top 7 mistakes in introducing a

Introduction: The Potential and Fall Knitting

The introduction of a AI [knowledge database](/services/ki knowledge database) is a transformative project that can raise the efficiency and intelligence of a company to a new level. But the way there is paved with potential drop knitting. Many companies fail not in technology itself, but in strategic and organizational failures during implementation.

Error 1: Unclear goals and missing business case

The most common mistake is the launch of an AI project, just because it is technologically in the trend to define without clear business objectives. Without a solid business case, the project lacks the strategic basis.

How to avoid it:

• Define SMART Objectives (Spezifish, Measurable, Accepted, Realistic, Terminated)

• Create an ROI plan with quantified benefits

Error 2: Bad data quality ("Garbage In, Garbage Out")

An AI is just as good as the data it is fed with. Outdated, irrelevant or false information leads to equally bad answers.

How to avoid it:

• Perform a content audit

• Establishing You have a content-lifecycle process with clear responsibilities

Error 3: Lack of employee acceptance

The best technology fails if the employees do not accept it. The introduction is often treated as a pure IT project and the human component is ignored.

How to avoid it:

• Early and transparent communication

• Create incentives and name champions

Bug 4: The wrong tool selection

A frequent error is the selection of a tool based on a single function or price without considering the overall picture.

How to avoid it:

• Create a request catalog with Must-haves and Nice-to-haves

• Start a pilot phase (Proof of Concept)

Error 5: Reliability of privacy and security

In the DACH area, the disregard of the GDPR can lead to sensitive punishments.

How to avoid it:

• "Privacy by Design" – integrate data protection officer from the start

• Watch the server location in the EU

Error 6: No clear role and authorization concept

If all employees can access all information, this leads to chaos and security problems.

How to avoid it:

• Implement the "Need-to-know" principle

• Define clear roles and groups with granular permissions

Error 7: Missing success measurement after the Go level

Many companies fail to systematically measure according to the Go-Live whether the initially defined goals are achieved.

How to avoid it:

• Define KPIs before start

• Create regular reports and analyze the data

Conclusion: Strategic planning is the key

The successful introduction of an AI knowledge database is less technical than a strategic un