Data Dialogs (Middle East Edition) - The AI ROI Paradox

Why are so many AI projects struggling to deliver a measurable return on investment? Despite growing enthusiasm for AI, Gartner predicts that 40% of agentic AI projects will be canceled by 2027 due to escalating costs, unclear business value, and operational challenges. For public sector organizations, where every dollar spent must translate into meaningful outcomes, expensive and slow AI is simply not sustainable. In this episode of Data Dialogs (Middle East Edition), Hazem Abu Nameh, Data & AI Expert at Denodo, explores the AI ROI paradox and explains why the key to making AI both powerful and affordable lies in the underlying data architecture. Traditional approaches that rely on copying and moving massive amounts of data can drive up infrastructure costs and limit performance. With a Logical Data Architecture, organizations can access data where it lives, reduce unnecessary data duplication, and scale AI more efficiently. Discover how this approach helps organizations: - Scale predictably: Expand AI initiatives without allowing infrastructure costs to spiral. - Optimize performance: Improve response times by bringing computation closer to the data. - Demonstrate accountability: Connect AI investments to measurable mission outcomes and public value. The bottom line? AI ROI isn't just a financial problem—it's an architectural one. A more efficient data foundation can help public sector organizations build AI initiatives that are scalable, cost-conscious, and focused on delivering real value. 👉 Discover how the Denodo AI Data Layer can help organizations improve the ROI of Agentic AI: https://okt.to/WKHQ2u #DataDialogs #AgenticAI #AIROI #PublicSector #AIDataLayer #DataArchitecture #TrustworthyAI #DataforALL