Gartner Recommends Establishing an AI Central Bank and Value-Risk Flywheel to Bridge the Enterprise AI Governance Gap
At the 2026 IT Symposium/Xpo, Gartner proposed three coordination mechanisms: a value-risk flywheel model, a virtual control platform for an AI central bank, and the cultivation of AI entrepreneurs. Its survey shows that while 71% of enterprises plan to deploy AI agents, only 21% possess the corresponding governance capabilities; this gap makes it difficult for the speed of value creation to outpace risk accumulation.
At the Gartner IT Symposium/Xpo conference held in Florida, USA, in October 2026, Gartner presented Chief Information Officers (CIOs) with a specific contradiction: 71% of enterprises plan to deploy AI agents within the next 12 months, yet only 21% have established governance capabilities for AI agents. Kristin Moyer, Distinguished Research Vice President at Gartner, pointed out that this gap makes it difficult for the speed of value creation to exceed the accumulation of risks. Meanwhile, a Gartner Q3 survey of 303 CIOs and IT risk management executives showed that only 25% of CIOs can effectively coordinate technological risks with business risks.
The first mechanism proposed by Gartner is the value-risk flywheel model. The flywheel features two core forces: momentum, which accelerates the realization of results, and drag, which may slow down progress. Daryl Plummer, Distinguished Research Vice President at Gartner, emphasized that momentum is not entirely beneficial; appropriate drag can also yield positive effects. Excellent CIOs should know how to create benign momentum, strategically introduce benign drag, while reducing negative momentum and negative drag. A governor located at the center of the flywheel is needed to decide when value acquisition justifies taking risks, which values should take precedence, and how to ensure organizational safety.
The second mechanism is the establishment of an AI Central Bank. This organization serves as a virtual control platform for internal AI decision-making within the enterprise, capable of establishing accountability systems, implementing technology governance, managing economic activities, and forming shared wisdom. Moyer explained that its purpose is not centralized management, but rather achieving common goals, integrating coordination, and fostering cross-departmental collaboration to ensure transparency, coordination, and accountability mechanisms between humans and machines. Plummer simultaneously reminded that the AI Central Bank itself cannot eliminate risks or directly create value; it is still people who truly accomplish these things.