AI transformation between bold ambition and pragmatic execution
For business leaders, artificial intelligence has become one of today's most important drivers of corporate transformation. Yet only 42% of data and AI leaders feel that their function holds a high or very high level of strategic importance within their organisation. Where does this gap between the boardroom and the engine room come from? How well-positioned are companies in Germany when it comes to data and AI capabilities? And what are highly mature organisations — the AI pioneers, or "AI Champions" — doing differently? This study explores these questions in depth. We examine the strategic topics shaping the agenda of data and AI leaders in 2026 and outline the key recommendations that emerge from the current landscape.
“In many organisations, smaller data and AI projects with a clear ROI are moving into focus. However, the financial value contribution often remains hidden, as operational KPIs continue to dominate.”
Inside the study
The challenging reality for data and AI leaders
The data and AI function is currently shaped by high expectations — but also by considerable uncertainty. While the pressure to demonstrate returns on AI investments is rising, 37% of respondents say they are unable to assess the financial value contribution of their work. Among those who feel confident making such an assessment, 24% anticipate an EBITDA uplift of between 11% and 20%. One reason for this lack of clarity: financial metrics still play a secondary role in many organisations. The three most widely used KPIs against which data and AI leaders are measured are process efficiency, improved decision-making, and technological innovation.
Short-term financial goals are moving up the agenda
Over the past five years, 44% of companies invested less than €1 million in data and AI projects — an average of €200,000 per year. However, willingness to invest is on the rise. The share of companies investing between €1 million and €25 million is set to grow from 28% over the past five years to 41% over the next five.
Despite these rising figures, smaller data and AI projects with a short-term return on investment are gaining ground in the market. For half of the data and AI leaders surveyed, the budget currently available remains a key challenge.
“Decision-makers are operating with short-term visibility and prioritising quick wins. But over the medium and long term, this approach risks holding back the broader data and AI transformation.”
Martin Whyte,Partner, Data & AI, PwC GermanyWhat sets AI pioneers apart: agile strategy, Responsible AI and strong data foundations
For organisations with a high level of data and AI maturity, strategy development remains a core activity: 81% of AI Champions plan to define a new — or revise their existing — data and AI strategy in 2026. Among non-Champions, this figure stands at just 42%.
Trust also takes centre stage for AI pioneers: 81% place strong emphasis on Responsible AI or a Trustworthy AI programme. This signals that responsible AI practices are no longer viewed merely as a compliance requirement, but as a genuine business imperative.
AI pioneers also place clear emphasis on building a strong data foundation — a key prerequisite for the effectiveness of many AI approaches. In 2026, 68% of AI Champions are giving high priority to data quality management, and 54% to master data management.
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PwC Study 2026: Success Factors of AI pioneers
How Agentic AI is reshaping the use of LLMs
While generative AI has fuelled broader interest in artificial intelligence, Agentic AI is increasingly shaping how AI models are deployed in the enterprise. For 30% of respondents, implementing AI agents is a high priority. As a result, the transformation of business processes is also moving up the agenda — and these processes now need to be rethought with agents in mind.
When it comes to building a long-term, scalable infrastructure for Agentic AI, however, many organisations are still in the early stages. Few, for example, have a solution in place for orchestrating multi-agent systems.
Innovation and scaling: a balancing act - Reorganisation of the data and AI portfolio
In 2026, 68% of respondents plan to launch new AI pilot projects. At the same time, growing pressure to deliver is shifting attention towards scalable solutions that generate clear business impact. Yet many organisations face significant technical and organisational hurdles along the way. On the technical side, heterogeneous data sources and legacy IT systems make scaling difficult; on the organisational side, functional silos and unclear responsibilities continue to slow broad implementation.
Alongside modernising data platforms, upskilling the workforce is a key focus for many organisations. 66% of the companies surveyed are planning upskilling initiatives, and 56% are turning to external experts to build AI capabilities.
Recommendations for a future-ready data and AI portfolio
- Alignment with business objectives
- Transparent investment packages
- Expanding self-service capabilities
- Multi-agent orchestration platform
Alignment with business objectives
Identify synergies between individual projects and bring them together into a coherent overall framework. Instead of isolated pilots, what's needed is a robust "engine" that links technical foundations — such as data pipelines — directly to business KPIs. Prioritise use cases according to their value contribution, and factor in the necessary enablers from the outset — including data, workforce capabilities, technology platforms and governance processes. In doing so, you transform isolated experiments into an end-to-end engine that delivers measurable business impact.
Transparent investment packages
Connect individual initiatives in a way that makes them pay into your business goals and tell a coherent story. This also means making it clear how the foundations (e.g. master data management) are what enable the headline applications (e.g. an agentic solution) in the first place. A transparent narrative takes the fear out of "black-box IT" and shows that individual investments are not mere cost centres, but essential building blocks of long-term value creation.
Expanding self-service capabilities
Free up your core team by establishing a robust infrastructure for decentralised innovation. Self-service platforms and standardised interfaces should empower business units to develop their own solutions safely and securely. The central team sets the guardrails and oversees overall integration, while self-service accelerates broad-based scaling across the organisation.
Multi-agent orchestration platform
Build a technological foundation that orchestrates autonomous AI agents securely. An agentic platform needs to support governance, monitoring and interfaces to existing systems. Particularly important is the orchestration of workflows in which agents take on tasks and validate results, while humans remain firmly in control and have the final say. Only a standardised platform can minimise security risks and unlock the full operational efficiency of this new generation of AI.
“Active portfolio management is essential for data and AI. The challenge is to balance innovation with the scaling of solutions that deliver clear value.”
Andreas Odenkirchen,Director, Data & AI, PwC GermanyDownload now
Success Factors of AI pioneers
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Our methodology
The study is based on the findings of a survey conducted by PwC Germany in collaboration with an independent research institute, covering 351 participants from companies of varying sizes, sectors and European countries. The sample includes organisations with fewer than 1,000 to more than 30,000 employees, and annual revenues ranging from under €1 billion to over €30 billion. The focus is on medium-sized to large enterprises.
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