Teradata Study Shows Only 7% of Firms Operationalize Agentic AI, ROI Gap Persists
TDC•Teradata’s study of 1,000 global tech leaders finds only 7% of enterprises have operationalized agentic AI while 68% remain in experimenting or developing stages. Ninety percent of senior leaders plan to increase agentic AI investments yet 63% report only small or emerging ROI, citing context fragmentation that leaves 77% of enterprise data insufficiently described for autonomous agents.
1. Study Highlights Maturity Index
Teradata commissioned a Wakefield Research survey of 1,000 senior technology and data leaders across six markets to create an Agentic AI Maturity Index. The four-stage framework—Experimenting, Developing, Building, Operationalizing—reveals just 7% of enterprises have reached the final stage while 68% remain in early phases.
2. Investment vs. Returns Discrepancy
Nine in ten senior leaders plan to boost agentic AI spending over the next 12 months, yet 63% report only small or emerging positive returns on their current investments. This investment–ROI gap underscores a misalignment between ambitions for enterprise AI and the data infrastructure required to deliver measurable outcomes.
3. Data Context Fragmentation Barriers
Context fragmentation is identified as the core obstacle, with 77% of executives stating that 20% or less of enterprise data is sufficiently contextualized for AI agents. Top barriers include missing metadata and fragmented systems that prevent real-time data unification, causing 40% of AI pilots to stall before production.
4. Strategic Recommendations
Teradata’s Autonomous Knowledge concept advises companies to select high-value data subsets for agile contextualization, rather than attempting to govern entire data estates. By prioritizing data with clear lineage, governance, and agent readiness, organizations can accelerate multi-step workflow automation and achieve the ROI that executives expect.
