Beyond Hype: Ramsey Theory Group Survey Uncovers the C-Suite’s Mandate for Controllable, Cost-Transparent, and Governed AI at Scale

The conversation in the C-suite regarding Artificial Intelligence has fundamentally shifted, according to new enterprise customer survey results released today by Dan Herbatschek, CEO of Ramsey Theory Group. Enterprise leaders are moving beyond the debate of whether to deploy AI and are now demanding rigorous, clear answers to four fundamental questions that determine whether AI implementations will create durable value or expose the organization to unmanaged risk.

“The results of our recent enterprise customer survey show executives prioritizing AI that is controllable, cost-transparent, defensible, and operational at scale without sacrificing governance,” said Herbatschek. “The days of isolated, unmanaged pilots are over. Boards and regulators are now demanding institutional control.”

The Four Foundational Questions Driving AI Strategy in 2026

The survey identifies the following four critical issues that CIOs, CISOs, and corporate boards are demanding clarity on before scaling AI initiatives:

1. Who Controls AI at Scale?

The primary concern is the uncontrolled sprawl of AI models, agents, and third-party services. What began as small departmental experiments has proliferated into potentially hundreds of systems influencing critical workflows. Herbatschek notes, “The real fear isn’t technological failure; it’s the inability to definitively answer who owns, oversees, or can shut down an unexpected AI behavior at a moment’s notice.” This mandate is driving a demand for centralized visibility, clear accountability, and enforceable controls across all AI vendors and models.

2. How Much Is It Actually Costing Us?

Excitement over model capability is being replaced by intense scrutiny of unit economics. Enterprises are struggling to reconcile costs that extend far beyond simple licensing—including GPU consumption, inference fees, data movement overhead, and operational staffing—often without a clear tie back to business outcomes. “Many leaders are discovering that AI cost overruns surface months later, when spend cannot be logically attributed to realized value,” Herbatschek explained. This requires a shift toward FinOps-style discipline for all AI workloads.

3. What Risk Are We Taking: Legally, Financially, and Reputationally?

AI introduces a new, multifaceted class of operational, legal, and brand risk that traditional compliance frameworks were not designed to address. Executives require demonstrable proof that AI decisions are auditable, explainable to regulators, and subject to appropriate human oversight. The survey findings confirm that AI governance is no longer an optional feature but is now being treated as a foundational requirement, akin to financial controls or cybersecurity.

4. How Do We Operationalize AI Without Losing Governance?

The most difficult challenge is scaling AI deeply into operations without introducing chaos or sacrificing trust. The goal is to move innovation and governance forward in alignment, not in opposition. This necessity is leading to the formation of centralized AI councils, standardized lifecycle management, human-in-the-loop controls, and continuous monitoring systems to ensure AI remains transparent and aligned with both regulatory and business intent.

Conclusion

Ramsey Theory Group’s survey results underscore a pivotal shift: the enterprise is ready for AI, but only under the strictest terms of control, cost predictability, and accountability. The next phase of AI adoption will be defined not by technical innovation alone, but by the rigor of its governance.

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