The pressure for data and AI initiatives to deliver demonstrable business value is intensifying. At the recent BARC Data & AI Summit, CEO Dr. Carsten Bange identified this as the defining challenge for leaders heading into 2026.
Across industries, organizations are facing difficult economic conditions and rising cost controls. Top management is asking legitimate questions: Where is the return on our investments in AI, analytics and data management systems?
For data and AI leaders, 2026 marks a turning point. Justifying initiatives based on technological innovation alone is no longer sufficient. Leaders must demonstrate measurable business value, establish reliable data foundations and scale AI from pilot projects to enterprise-wide implementation.
To guide data leaders toward achieving this success, Dr. Bange’s keynote outlined five core priorities. These recommendations are based on BARC’s analysis of high-performing organizations and our ongoing research into current industry trends.
When BARC surveyed practitioners for the Data, Analytics & BI Trend Monitor 2026, two priorities emerged at the top of the rankings: data quality management and data security.
Data sovereignty has also become a key concern, with 84% of organizations now considering it a priority to maintain control over where their data resides and who can access it. This is driven by global political developments, including changes in U.S. leadership. In response, organizations are adopting hybrid strategies, increasing their use of regional cloud providers, and, in 19 % of cases, expanding on-premises deployments.
These foundational priorities send a clear message: before pursuing AI initiatives, organizations must establish a stable and trustworthy data foundation.
Technology alone cannot drive transformation; organizations must focus more intensely on people. A successful cultural shift requires progress on three dimensions:
Culture cannot be mandated. It must be cultivated by changing how people think and behave. The BARC Data and AI Culture Framework identifies key areas for influence: strategy, leadership, governance, empowerment, communication and data accessibility.
The third priority is to move AI from experimentation to scaled deployment. Many organizations have built prototypes and some have reached production. However, the question from leadership persists: “Where is the impact?“. Personal productivity gains from tools like GenAI do not automatically translate to clear business value.
The critical challenge is to identify and scale AI initiatives that deliver genuine organizational impact. This requires operational strategies that focus on processes where automation can produce meaningful, measurable effects. A fundamental principle is that process automation must begin with process transformation.
Data products enable organizations to scale their data initiatives through controlled decentralization. When more people are involved in building and sharing data products, the effects are far greater than when a central team manages all data flows.
The key to this approach is federation—balancing central control (governance, technical platforms) with decentralized responsibility (product development). The degree of decentralization should be based on the organization’s maturity.
Executing this requires three practical mechanisms:
Data and AI leaders face mounting pressure to demonstrate business value. In challenging economic times, the question from top management is urgent: Where are our investments paying off?
Organizations must measure and communicate this value while controlling costs. Tracking expenses for software, cloud providers and data platforms has become as critical as measuring ROI. Building a compelling value story requires both quantitative metrics and qualitative stakeholder feedback.
Decentralization strengthens this value case. The more people who are integrated into value creation, the clearer the impact becomes. In response, some data and analytics units are evolving from centers of excellence to internal service providers that use charge-back models to demonstrate their contribution.
Without a value story, data and AI initiatives remain IT projects and risk being treated as such.
These five priorities form a roadmap from foundational stability to measurable business value. Data and AI are shaping the future. The organizations that succeed will be those that establish trust through solid data foundations, exercise responsibility through effective governance and cultural transformation and demonstrate impact through operational excellence and clear value stories.
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