Enterprise AI demand is surging, much of it driven by FOMO rather than a defined business problem. As organizations rush to prove that pilots work, dashboards, access requests, and generic asks often get mislabeled as use cases.

In the second part of this video interview series, Nadiem von Heydebrand, CEO & Co-Founder of Mindfuel, joins Justin Heller, Editorial Board Member at CDO Magazine, to unpack why so much AI demand fails to translate into results. Most organizations, Von Heydebrand explains, see only a fraction of their real business problems because those rarely reach data and AI teams directly. Closing that gap requires business and data leaders to jointly qualify each use case by its problem space and solution space, not its technology.

Once qualified, a use case’s structure determines how its value is measured. Von Heydebrand notes that project-based delivery still has a clear place in execution, but product thinking is required for continuous monitoring to deliver value based on the problem being solved throughout its lifecycle, not at a single delivery milestone.

Key discussion themes include:

• Use Case Clarity: Value starts with defining the problem space, not with dashboards, access requests, or a rush to do AI.
• Demand Management: Data and AI teams see only a fraction of true business need unless leaders surface it jointly.
• Project vs. Product Thinking: Projects have a clear role in execution, but sustained AI value depends on continuous, lifecycle-based ownership and cost tracking.

Stay tuned for the last installment in this three-part interview series.

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