Eyes on Data: The AI Readiness Gap — Why Data Strategy Is Now a CEO-Level Issue

Artificial intelligence has quickly become a boardroom priority.

Across industries, CEOs and executive teams are asking how AI can improve productivity, reduce costs, strengthen decision-making, and create new sources of competitive advantage. Organizations are making significant investments in AI platforms, infrastructure, and talent while accelerating efforts to move from experimentation to enterprise-wide adoption.

But a critical question is often missing from the conversation:

Is the organization actually ready to scale AI?

The 2026 EDM Association Global Data Management Benchmark Report suggests that, for many organizations, the answer is not yet.

Based on responses from more than 435 organizations across more than 50 countries, the report reveals a widening gap between enterprise AI ambition and the organizational foundations required to support it. While AI adoption is accelerating, many companies continue to struggle with strategy, accountability, funding, governance, architecture, and workforce readiness.

The implication for CEOs is straightforward: AI readiness is an enterprise data management question.

AI Is Exposing Weaknesses That Already Existed

Artificial intelligence is often described as a transformational technology. But in many organizations, its most immediate impact is diagnostic.

AI is exposing weaknesses that have existed for years: fragmented data, unclear ownership, inconsistent definitions, weak governance, disconnected technology environments, and limited workforce fluency.

These issues may have been manageable when data was used primarily for reporting and traditional analytics. They become significantly more consequential when organizations attempt to use that same data to power AI systems and automate business decisions.

AI increases the need for strong data foundations.

The 2026 benchmark shows that fewer than one-third of organizations report advanced maturity in data strategy and business-case development. At the same time, companies are aggressively pursuing AI investments.

This creates a strategic imbalance.

Organizations are making decisions about how to deploy AI before fully answering more fundamental questions about the role data should play in the business:

• What data is strategically important?
• Which data assets create competitive advantage?
• What information is required to support critical decisions?
• Who is accountable for data quality?
• What investments are necessary to support long-term value?

These are not questions for the IT department alone. They are questions for the executive team.

The Biggest AI Risk May Be Strategic Misalignment

One of the central findings of the benchmark is that strategic foundations remain the weakest area of data management maturity.

This is a problem because technology investment without strategic alignment tends to produce fragmentation.

Different business units pursue different AI initiatives. Technology teams make platform decisions without clear enterprise priorities. Data programs compete for funding. Governance is introduced after systems are already in production.

The result is an organization that may be doing more with AI but not necessarily creating more enterprise value.

For CEOs, the lesson is important: The challenge is not simply to encourage more AI experimentation. It is to ensure that AI investments are connected to the organization’s most important business priorities.

That requires a shift from asking:

“Where can we use AI?”

…to asking:

“Where can AI create measurable strategic advantage, and what capabilities are required to make that possible?”

The first question produces a pipeline of use cases. The second creates an investment strategy.

The CDO Is Now mainstream — Executive Accountability Must Catch Up

The growing presence of the chief data officer reflects the increasing importance of data to enterprise strategy.

According to the benchmark, 72% of organizations now report having a CDO or equivalent role. The position has become a mainstream feature of the modern enterprise.

But the existence of a CDO does not automatically create effective data leadership.

The report finds evidence of leadership turnover in 37% of responses. It also identifies a significant decline in role alignment over time, falling from 79% for CDO roles less than one year old to just 39% for roles established for more than five years.

That trend should concern executive teams.

A data leader cannot be expected to drive enterprise transformation without a clear mandate, sufficient authority and sustainable resources. Nor can responsibility for data be isolated within one executive function while the rest of the organization continues to operate in silos.

AI makes this even more important.

The technology crosses traditional organizational boundaries. It affects operations, technology, risk, compliance, legal, cybersecurity, finance, and virtually every business function.

As a result, data accountability cannot remain an issue delegated entirely to the data organization.

The CEO and executive team must establish who is accountable for the organization’s data and AI capabilities, how decisions are made, and where responsibility ultimately sits when those systems fail.

The Workforce May Determine Whether AI Creates Value

Technology investment is visible. Organizational readiness is harder to measure.

Yet the benchmark suggests that workforce readiness may be one of the biggest barriers to AI adoption. Only 19.1% of organizations report advanced maturity in analytics education and adoption. That means many organizations may have access to advanced data and AI capabilities without having fully equipped their employees to use them.

AI changes how people make decisions, perform tasks, and interact with technology. Employees need to understand how to use AI tools, evaluate outputs, and identify potential errors. Managers need to know how to incorporate AI into workflows. Executives need to understand both the opportunities and limitations of AI-generated information.

Without this organizational fluency, AI adoption can remain concentrated in small pockets of the enterprise. The technology may be available, but the organization is not ready to use it at scale.

For CEOs, this creates a broader leadership challenge: AI transformation requires not only new technology, but also new skills, new processes, and potentially new definitions of work.

The CEO Agenda for AI Readiness

The findings from the 2026 benchmark point to several priorities for executive leaders.

1. Connect AI investment to business strategy. AI should not be pursued simply because the technology is available. Organizations need to identify where it can create measurable value and what capabilities are required to support those priorities.
2. Establish clear accountability. Data and AI responsibilities must be clearly defined across the executive team. A CDO can be a critical leader, but data accountability across the entire enterprise cannot rest with one function alone.
3.  Fund the operating model, not just the technology. Sustainable AI requires ongoing investment in governance, architecture, data quality, monitoring, talent and change management. One-off or isolated investments will not contribute long-term value.
4.  Treat workforce readiness as a strategic capability. Employees need the skills and confidence to use AI effectively to support business objectives. Adoption cannot be assumed simply because tools have been deployed.
5.  Measure maturity, not just activity. The number of AI pilots, models deployed or employees given access to tools is not the same as business impact. Executive teams should  start with strategic goals, and then measure whether AI capabilities are reliable, scalable, adopted, and connected to measurable outcomes.

The Next AI Advantage Will Come from the Foundation

The first phase of enterprise AI was largely about experimentation and the next phase will be the execution phase. Organizations will increasingly be judged not by whether they have launched AI initiatives, but by whether they can scale those initiatives responsibly and consistently across the enterprise.

The 2026 EDM Association Global Data Management Benchmark Report makes clear that many organizations are not starting from zero. Significant progress has been made. Data management is increasingly recognized as an enterprise-wide discipline, governance structures are becoming more established and AI is accelerating investment and attention.

But ambition alone is not enough. Organizations that want to scale AI must strengthen the foundations that make scale possible.

That means better strategy. Clearer accountability. Sustainable funding. Stronger governance. More connected architecture. Greater workforce readiness.

The companies that win the next phase of AI adoption may not necessarily be those that move first. They will be the ones that build the strongest foundation.

For CEOs, that means the most important AI question may not be, “How quickly can we deploy it?”

It is: “Have we built an organization capable of making it work?”

Learn More

• Download the EDM Association 2026 Global Data Management Benchmark Report and learn more about our benchmarking initiatives
• Get involved with the EDM Association’s Artificial Intelligence Forum to collaborate on best practices, use cases, and more
• Explore EDM Association best practice frameworks, including DCAM and CDMC
  • Please note: DCAM is available exclusively to EDM Association member organizations. Not yet an EDM Association member? Learn about the benefits of membership or contact the team.

This quarter’s column contributed by: 

Jim Halcomb, Chief Research & Development Officer, EDM Association

Jim Halcomb is a strategy, data management, and cybersecurity executive with 30 years of international business experience. Jim leads EDM Association’s Communities of Practice, Best Practices Frameworks (DCAM & CDMC) and Training & Certification programs.