Data quality as the basis for AI & automation

Introduction

Data quality as the basis for AI & automation is at the heart of digital transformation. Today, companies are increasingly using artificial intelligence and automation to make processes more efficient, use data better and develop new business models.

What is data quality as a basis for AI & automation?

Data quality as the basis for AI & automation describes methods and technologies that enable machines to perform tasks independently, recognize patterns, make predictions or process language. The aim is to supplement or automate human work.

Relevance in the corporate context

Data quality as the basis for AI & automation enables potential savings, quality improvements and accelerated processes. Used correctly, it strengthens competitiveness and frees up time for value-adding activities.

Typical challenges

• Lack of data quality & data access
• Unclear objectives or missing use cases
• Technology complexity & tool diversity
• Acceptance problems & ethical issues

Practical example

A service provider implemented data quality as the basis for ki & automation to automate invoice verification and document processing. The result: faster processes, a lower error rate and more time for customer service.

Our consulting approach

1. Use case identification & target image definition
2. Data analysis & technology selection
3. Proof of concept & MVP development
4. Scaling & change management
5. Governance & performance measurement

Conclusion

Data quality as the basis for AI & automation is not hype, but a strategic lever for digital efficiency. Clear goals, suitable tools and a step-by-step approach are crucial.