We lay the foundations for scalable, reliable AI

Future-proof data architecture for AI

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Nico Reichen

Nico Reichen
Partner at PwC Germany
Tel: +49 171 8179355
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Securely integrating data, AI platforms and regulatory requirements

Without a solid foundation, artificial intelligence (AI) remains just a promise. Insufficient data, limited availability and security risks slow down the implementation of AI initiatives in practice. PwC's study 'Success Factors of AI pioneers' shows that 65% of data and AI managers struggle with data quality, 62% with IT or cyber security and 46% with a lack of data availability.

For AI to work reliably, architecture, data and agentic capabilities must work together from the start. We create this trustworthy basis: a modern data architecture, an LLM-agnostic system landscape, quality-assured data, data governance and an enterprise-wide framework for the secure deployment of autonomous agents. In this way, we move AI solutions from pilot to reliable operation.

“AI pioneers safeguard their ability to act by relying on solid data and a flexible architecture. Companies that build a reliable foundation today can switch models and providers if geopolitical risks restrict access.”

Nico Reichen,Partner at PwC Germany

Our services for AI-ready data and a scalable AI architecture

We develop your target architecture for AI: scalable, LLM-agnostic and based on a vendor-neutral assessment.

We safeguard the quality of your data and knowledge and make both easy to find and usable for AI in a controlled way.

We create a framework in which agents operate securely, enabling scalable AI instead of standalone solutions.

We make verified data usable for business functions and AI agents and enable tailored analyses.

We assess your key use cases and show whether your data is ready for AI.

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Is your data AI-ready yet?

Our experts identify where you stand and what is missing.

Technology, data and operations seamlessly integrated

We bring together all the building blocks for AI readiness in an integrated foundation, combining technology expertise with data, platform and process know-how. The resulting architecture and operating principles are designed to scale from the first use case onwards, rather than requiring sustainable structures to be added later.

Our team of more than 600 AI experts develops a target vision that fits your company's current situation and level of maturity. Not every organisation needs the same platform. What matters is aligning data, technology and the AI operating model in a way that meets today's requirements and prepares for future scaling.

The outcome: data, corporate knowledge, models and agents interact via defined interfaces. Reusable components and shared monitoring prevent separate standalone solutions from emerging. Changes can be tested and rolled out in a controlled way at any time.

New data architecture for a global corporation

Since 2024, we have been supporting a global corporation in introducing a cloud-based data platform. Previously, every request had to go through IT, and business functions resorted to Excel exports or their own models. With our support, the company now organises its data foundation into data products. Today, more than 50,000 users access an integrated platform with around 500 data products. This provides business functions and IT with a unified, AI-ready data foundation for analytics, planning and AI agents.

Frequently Asked Questions 

Many companies struggle with insufficient data quality. Even the best LLM cannot deliver reliable results if it draws on isolated, contradictory or poorly accessible data. A strong foundation enables high data quality, efficient analyses and secure AI applications.

An LLM-agnostic architecture is not tied to a specific model or provider. Models can be switched without rebuilding the entire system landscape. This keeps costs under control, reduces dependencies and allows AI capabilities to be extended flexibly.

Generative AI (GenAI) needs more than structured data: corporate knowledge must be easy to find, versioned and available in a way that is controlled by access rights. Orchestration, monitoring, data governance and human-in-the-loop controls become an integral part of the architecture.

A data warehouse structures verified data for standardised analyses. A data lake stores structured and unstructured data flexibly. A data lakehouse combines both approaches and creates a central, scalable basis for analytics and AI applications. Which architecture is right for your AI initiatives depends on data quality, data integration and your use cases.

We connect your legacy systems step by step via data integration and controlled interfaces. This means your existing systems remain in use, while key data becomes available for AI applications and functions.

The framework defines how autonomous agents access data and systems securely within an organisation, how their interaction is orchestrated and who approves them. As soon as more than one agent is used in production, this shared framework becomes essential. It enables your teams to steer and develop agents efficiently.

Not every organisation needs the same platform. Our team of more than 600 experienced AI experts develops a target vision that aligns data integration, technology and the operating model with your specific starting point – based on a vendor-neutral assessment.

“Companies often invest in AI models before they know whether their data and architecture can support them. We turn this around and first create the conditions that allow AI to deliver lasting results.”

Nico Reichen,Partner at PwC Germany

AI-Accelerator-Workshop – Book now!

Set priorities. Assess potential. Achieve measurable added value. We help you set the right course for a successful AI transformation right from the start. Our AI Accelerator Workshop provides clarity: together, we assess where AI delivers real added value, what is realistically achievable and how you can get off to a sensible start.

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Nico Reichen

Nico Reichen

Partner, AI Portfolio Leader, PwC Germany

Tel: +49 171 8179 355

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