Judith Aquino

The paper’s authors also note that specifying a goal or target is not always enough to convey everything that a human means. “A target may be sufficiently specified for a human collaborator who shares the relevant cultural, situational, historical or social context, yet remain unavailable to an AI system that lacks or cannot reliably prioritize that context,” the researchers write.

The importance of reviewing and verifying AI outputs can’t be overstated, says Merve Unuvar, Director of Agentic Applications and Middleware at IBM. “The better the agent becomes, the more dangerous blind trust becomes,” Unuvar told IBM Think in an interview.

AI is treated as an enterprise-wide priority, but ownership and decision rights are fragmented across the C-suite.¹ Each role advances its own mandate—technology, data, strategy, use cases—yet no single executive is clearly accountable for end-to-end outcomes or empowered to resolve cross-functional trade-offs. As a result, decisions on funding, risk, prioritization and governance linger unresolved and when ROI disappoints, accountability is hard to pinpoint.