Why companies need to invest in data and AI now

Many companies are currently investing in AI and are still disappointed with the results. Forecasts remain unreliable, reports contradictory and operational decisions take too long. The reason is rarely in the AI itself, but almost always in the lack of data and data & AI strategy.

This e-book shows why successful AI initiatives must first have data structures, quality, and governance. It makes visible where fragmented data landscapes are becoming a business risk, which developments require action now and how companies are already benefiting measurably: faster decisions, less manual effort and a reliable basis for analytics and AI. Anyone who relies directly on AI without building up data properly risks expensive bad investments.

Why is it worth downloading for your Data & AI strategy?

✅ Data & AI quick check: How data-mature is your company?
See how well your organization is doing with data, analytics, and AI today, from central data sources and automated pipelines to forecasting and AI deployment. The quick check clearly shows where there is a need for action.

✅ The key developments that require action now
Fragmented data landscapes, increasing requirements for forecasting, growing compliance pressure and the increasing use of AI are fundamentally changing the demands on companies. The e-book classifies these trends and shows why data & AI is becoming a management task today.

✅ From data fragments to a clear data & AI strategy
Learn how organizations are unifying data from disparate systems, reducing manual exports, and creating a unified foundation for reporting, analytics, and AI—rather than siloed point solutions.

✅ Practical examples with measurable results
Based on ATP Architects Engineers and Progroup, the e-book shows how modern data architectures work: faster data updates, less IT effort and more reliable decisions in day-to-day business.

✅ Concrete orientation for the next step
The e-book summarizes what really matters now: clear data models, automated flows, self-service analytics, and an architecture that can grow from reporting to AI. Practical and realistically implementable.

Would you like to learn how to future-proof your Data & AI strategy?

👉 Book an appointment!