Why This Is Now a Leadership Issue
For a long time, many organizations treated data quality, governance, and master data as largely technical matters. They were important, but still easy to push down into IT, analytics, or systems teams. That framing is under more pressure now because AI magnifies whatever discipline already exists, or does not.
The current research is lining up around the same point. McKinsey found that organizations seeing more EBIT impact from generative AI are redesigning workflows as they deploy it, and that workflow redesign has one of the strongest relationships to value creation. IBM found that 72% of CEOs see proprietary data as key to unlocking generative AI value, while 50% say the pace of recent investment has left their organizations with disconnected technology. Deloitte’s 2026 CDAO survey found that 94% of chief data and analytics officers expect their influence to grow over the next year, and 78% say AI has increased their power as decision makers.
72%
CEOs: Proprietary Data is Key
IBM 2025 CEO Study: proprietary data is essential to unlocking generative AI value
50%
Disconnected Technology
CEOs report that the pace of AI investment has left their organizations with fragmented, disconnected technology stacks
94%
CDAO Influence Growing
Chief data and analytics officers expect their organizational influence to expand over the next twelve months
78%
AI Elevating Decision Power
CDAOs report that AI has already increased their authority and influence as decision makers
That matters because trusted data is no longer just a technical foundation. It is increasingly a business operating issue. If leaders cannot clearly answer who owns key data domains, who resolves conflicts across functions, or who decides which standards govern critical workflows, then the problem is not only architecture. It is leadership.
The Amplification Problem
AI makes that harder to ignore. It scales decisions faster. It extends bad definitions further. It exposes duplicate ownership, weak governance, and loose operating discipline. What used to create friction can now create amplified error, inconsistent outputs, and confidence problems across customer, product, supplier, and operational workflows.
A common mistake is assuming the answer is better tooling. Better tools can help, but they do not resolve unclear ownership or weak accountability. An organization does not become AI-ready because it bought a platform or stood up a governance council. It becomes AI-ready when the business is disciplined enough to define what matters, own the rules, govern exceptions, and reinforce the behaviors required to keep data trustworthy over time.
The Leadership Shift AI Is Forcing
This is where leadership has to shift. Business leaders do not need to become data engineers, but they do need to own the choices that shape how data is defined, governed, escalated, and used. Without that, governance becomes ceremonial and AI becomes another expensive layer sitting on top of unresolved operating problems.
The organizations that create more value here will connect data ownership to real operating decisions, redesign workflows around trusted information, and treat governance as part of execution discipline. McKinsey’s survey makes that point especially clearly through the importance of workflow redesign in AI value creation.
The practical question for leadership is straightforward: where does the business still not trust its own data, and who is accountable for fixing that in a way that survives real operating pressure?
AI amplifies whatever discipline already exists, or does not.
What Strong Trusted-Data Leadership Requires
Clear Business Ownership
Business ownership is clear, not just technical stewardship.
Data Quality as a Prerequisite
Data quality is treated as a prerequisite to AI deployment, not something to address later.
Governance with Real Accountability
Governance creates real accountability for data decisions, not just oversight of data assets.
Sustained Executive Attention
Leadership understands that trusted data is not a one-time initiative. It is an ongoing discipline that deserves sustained executive attention, much like financial controls or operational risk.
The KB Royce View
KB Royce Group works with organizations at the intersection of trusted data, AI readiness, and business-side governance. Our view is straightforward: AI investments built on weak or ungoverned data do more than underperform, they introduce compounding risk. The organizations most likely to realize durable value from AI are the ones that treat trusted-data leadership as a strategic priority from the beginning.
About the Author
Karen Baker is Principal of KB Royce Group, founded in 2015. KB Royce helps organizations lead trusted-data, digital, and transformation programs with the governance, alignment, and execution discipline required to make change stick.
Sources
- McKinsey, The State of AI: Global Survey 2025.
- IBM Institute for Business Value, 2025 CEO Study.
- Deloitte, 2026 Chief Data and Analytics Officer Survey.
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