As AI adoption accelerates, organizations are elevating the CDO role to align data strategy with business outcomes.
In 2024, it seemed inevitable that AI transformation would deepen the alignment between data and infrastructure. Many expected the percentage of chief data officers reporting to CIOs to rise sharply. Instead, Forrester’s 2025 data revealed the opposite: a 7% decline in CDOs reporting to CIOs, and a 6% increase in those reporting directly to CEOs.
This shift reflects a broader realization. Data strategy is no longer just about enablement—it’s about business design. As AI systems become embedded across functions, organizations are rethinking how they govern, prioritize, and monetize data. Elevating the CDO role is not a symbolic move. It’s a structural response to the growing complexity of data-driven transformation.
1. AI has made data a business asset—not just a technical resource
AI systems don’t just consume data—they reshape how businesses operate. From predictive planning to autonomous decision-making, AI depends on high-quality, well-governed data. That makes data strategy a business issue, not just a technology one.
When CDOs report to CIOs, data priorities often follow infrastructure constraints. But when they report to CEOs, data becomes a lever for growth, efficiency, and innovation.
In financial services, for example, AI-driven credit risk models require data alignment across underwriting, compliance, and customer analytics—not just IT. In healthcare, clinical AI tools depend on unified data from EHRs, billing systems, and care operations. In retail and CPG, demand forecasting agents need synchronized inputs from merchandising, supply chain, and marketing to be effective.
Similarly, in manufacturing, for example, AI-driven supply chain optimization requires data alignment across finance, operations, and logistics—not just IT.
These examples show that AI-driven systems require data strategies that span multiple business functions—not just IT—because meaningful outcomes depend on aligning data across operational, analytical, and decision-making domains.
Takeaway: Treat data as a business capability, not a technical dependency.
2. Infrastructure and data strategy now require separate leadership
As AI workloads grow, infrastructure demands are escalating—compute, storage, orchestration, and security all require focused attention. At the same time, data strategy is becoming more complex: lineage, governance, monetization, and ethical use all demand cross-functional coordination.
Splitting these responsibilities allows each leader to focus. The CIO can optimize infrastructure for scale and resilience. The CDO can shape data policy, quality, and usage across the enterprise. In financial services, this separation helps avoid conflicts between compliance-driven infrastructure decisions and innovation-driven data initiatives.
Takeaway: Clarify leadership boundaries to avoid tradeoffs between infrastructure stability and data agility.
3. Business alignment improves data quality and relevance
When data strategy is driven from the top, it’s easier to align with business goals. That means better prioritization of data sources, cleaner pipelines, and more relevant metrics. It also reduces the risk of building AI systems on incomplete or misaligned data.
Healthcare organizations, for instance, often struggle with fragmented patient data across clinical, operational, and financial systems. Elevating the CDO role helps unify these sources under a common framework, improving both care outcomes and operational efficiency.
Takeaway: Elevate data leadership to improve cross-functional alignment and data quality.
4. AI governance demands enterprise-wide accountability
AI systems introduce new risks—bias, drift, hallucination, and unintended consequences. Managing these risks requires governance that spans legal, compliance, HR, and product teams. That’s difficult to achieve when data leadership is nested within IT.
Reporting directly to the CEO gives CDOs the mandate to build enterprise-wide governance frameworks. It also signals that data ethics and AI accountability are board-level issues. In retail and CPG, where customer data drives personalization and pricing, this shift helps mitigate reputational and regulatory risk.
Takeaway: Position data leadership to drive enterprise-wide AI governance.
5. The “divide and conquer” model is gaining traction
Rather than consolidating AI under a single leader, many organizations are adopting a dual-track model. CDOs focus on data strategy, governance, and business alignment. CIOs focus on infrastructure, tooling, and enablement. This approach reduces bottlenecks and clarifies decision rights.
It also reflects the reality that AI transformation touches every part of the business. No single leader can own it all. By elevating the CDO role, organizations ensure that data decisions are made with full visibility into business priorities—not just technical feasibility.
Takeaway: Use dual-track leadership to accelerate AI adoption without compromising governance or scale.
6. Reporting lines shape incentives—and outcomes
Who a leader reports to influences how they prioritize, allocate resources, and measure success. When CDOs report to CIOs, they’re often evaluated on enablement metrics: data availability, integration speed, and platform usage. When they report to CEOs, the focus shifts to business impact: revenue growth, cost reduction, and risk mitigation.
This shift in incentives drives different behaviors. It encourages CDOs to engage more deeply with business units, challenge assumptions, and push for data-driven decision-making. In financial services, where data is often fragmented across risk, compliance, and customer systems, elevating the CDO role can unlock new value in product design, fraud detection, and regulatory reporting.
In government agencies, where data is often siloed and underutilized, this change can unlock new value in policy design and service delivery.
Takeaway: Align reporting structures with the outcomes you want to achieve.
AI is forcing organizations to rethink how they manage data—not just technically, but structurally. Elevating the CDO role is part of that shift. It reflects a growing understanding that data strategy is central to business transformation, and that it requires leadership with enterprise-wide reach.
What’s one reporting structure change you’ve made—or considered—to improve data strategy alignment? Examples: moving data governance under enterprise risk, elevating the CDO to report to the CEO, splitting AI enablement from data policy.