The enthusiasm surrounding generative AI remains high, yet many organizations are still stuck in experimentation mode. Dietgar Völzke, CIO and Member of the Board at Netfonds, points out that only around half of companies are leveraging AI at a meaningful scale, despite ambitious strategies and growing budgets. For him, AI is not an end in itself; like any technology, it ultimately has to prove its return on investment.
He emphasizes that operationalizing AI requires a solid foundation: a structured AI governance framework, such as one aligned with ISO 42001, robust data governance structures, a data-centric approach, and an interdisciplinary task force involving management, compliance, early adopters, and constructive critics. At Netfonds, these elements are considered essential for successful AI implementation.
From Use Case to Business Case: AI Must Deliver Impact
Völzke believes that AI must go beyond fascination and deliver tangible results, whether in document processing, compliance checks, or content generation. At Netfonds, they start deliberately with a canvas-based approach to determine business value before developing any prototype.
During implementation, the focus is on technical and regulatory clarity, but equally on people. Training, expectation management, and the collaborative development of meaningful use cases are seen as critical success factors.
For Völzke, creating real value with AI requires rethinking fundamental processes. Netfonds follows an “AI by design” approach, aiming not just to add AI to individual tasks but to redesign entire workflows with AI integrated from the outset. This, he explains, enables smarter, more connected processes that deliver long-term value.
AI Requires Cultural Change – and Clear Objectives
Völzke also underlines that achieving innovation and efficiency with AI demands more than strategic declarations and pilot projects. In his view, AI needs to be embedded across the entire organization, supported by enablement and shared accountability.
At Netfonds, the transition from concept to execution happens through exploratory, iterative rollouts, combined with governance, continuous learning, and clearly defined success metrics. For Völzke, the goal is not only to optimize processes but also to unlock new business models.
Ultimately, he stresses that the focus must remain on creating measurable value rather than merely deploying technology, as this is the only path toward sustainable, ROI-driven transformation.
