AI is no longer a moonshot. It's an operational mandate. In companies across every sector, generative AI (Gen AI) and automation are redefining how decisions are made, how teams interact with data, and how value is delivered to customers. However, this shift doesn't happen by itself; it requires leadership.
That's why the role of the Chief Data and Analytics officer (CDAO) is quickly becoming one of the most essential seats at the executive table.
Co-Founder and VP of Product at Savant Labs.
According to a recent data and AI leadership survey, 73.7% of organizations now report having a formal CDO or CDAO role, up from just 12% a decade ago. However, visibility alone doesn't guarantee influence. The pressure is rising for CDAOs to deliver tangible business outcomes, not just pipelines or dashboards.
Gartner predicts that by 2026, 75% of organizations will operationalize AI, up from 10% in 2020. By 2027, 75% of CDAOs who fail demonstrate AI’s positive impact will be reassigned or removed from the C-suite. It’s obvious that enterprises are no longer investing in data for data's sake.
They're investing in AI to move faster, act smarter, and compete harder – and it’s the CDAOs job to lead that charge.
From data management to strategic AI enablement
The original charter for many CDAOs centered on improving data hygiene and governance, which are important goals, but mainly reside behind the scenes. Today's mandate is broader and more visible: accelerate innovation through AI while managing risk, complexity, and cost.
CDAOs must go beyond operational analytics and drive enterprise-wide alignment around AI strategy. They're responsible for embedding intelligence into core workflows, bridging technical and business priorities, and setting the guardrails that make scalable AI possible.
In short, CDAOs are now the architects behind AI-powered systems, and the connective tissue between ambition and execution.
Three reasons the CDAO role matters more than ever
AI requires context, not just data
Gen AI and automation tools are only as valuable as the context they're given. A model might summarize documents or recommend actions, but its suggestions fall flat or create risk if it lacks awareness of business definitions, process logic, or compliance thresholds.
CDAOs play a critical role in building the connective infrastructure that makes Gen AI worthwhile. This includes integrating structured and unstructured data, encoding institutional knowledge into models, and ensuring that outputs reflect not just patterns, but priorities.
By aligning AI systems with business goals and making those goals machine-readable, CDAOs make AI relevant, not just powerful.
AI needs governance at scale
As intelligent agents gain autonomy in decision-making, governance becomes more complex and more critical. Legacy controls, like static permissions or centralized sign-offs, don't scale when decisions happen in real time across multiple systems.
Modern CDAOs embed governance into the workflows themselves, codifying policies, enforcing data quality standards, and enabling audibility within the tools business users rely on every day.
This shift, sometimes called "governance as code," ensures that Gen AI systems remain traceable, explainable, and compliant, even as they operate at speed and scale.
Gen AI adoption is not just technical; it's cultural
One of the biggest blockers to successful Gen AI deployment isn't model accuracy; it's organizational trust. Business teams need to believe that the systems they're using are accurate, fair, and aligned with their goals.
CDAOs act as translators between the data science team and the business, shaping expectations, aligning metrics, and helping frontline users understand how Gen AI can support, not replace, their work.
By promoting a culture of data fluency and transparency, CDAOs enable adoption, which matters most in the day-to-day decisions made by people across the enterprise.
Moving from automation to intelligence
Many organizations began their Gen AI journey by automating manual tasks like report generation, data classification, and reconciliation. While this is a foundational starting point, the next phase involves using Gen AI to support reasoning, prioritization, and decision-making.
This shift towards "agentic intelligence," where systems act based on context and goals, creates new expectations for data leadership. CDAOs must now design environments where intelligent agents don't just move data from point A to point B but understand relationships, surface relevant insights, and take action responsibly.
That requires more than technical tooling. It requires orchestration- connecting APIs, data layers, and institutional logic into workflows where agents can operate effectively.
Rethinking the data stack for AI success
To support Gen AI at scale, CDAOs are modernizing their ecosystems. That means consolidating siloed tools, eliminating redundant manual processes, upgrading legacy systems limited to ETL, and building flexible infrastructure that can support diverse use cases.
Rather than relying on sprawling spreadsheets or custom-coded workflows, many are adopting platforms that offer intuitive, no-code interfaces.
This allows analysts to contribute without relying on scarce engineering resources. These platforms often incorporate natural language prompts, built-in governance, and real-time data connectors, enabling teams to act on insights quickly and safely.
CDAOs enable faster experimentation without compromising control, allowing analysts to automate without coding requirements while making governance achievable.
Metrics that matter: measuring the CDAO's impact
As CDAOs take on more strategic responsibilities, measuring success becomes more complex. It's not just about uptime or dashboard usage anymore. Impact must be evaluated through business-aligned KPIs, such as:
Time-to-decision across business units
Reduction in manual reporting hours
Accuracy and explainability of AI-generated outputs
Data literacy levels across non-technical teams
Volume of AI-enabled processes launched and governed
These metrics help reinforce the CDAO's value and provide a roadmap for iterative improvement.
The future of the CDAO is strategic
As enterprises scale their use of Gen AI, the most successful CDAOs will be those who move beyond research to operationalization. That means defining what "good" looks like in Gen AI adoption, creating measurable impact across functions, and embedding intelligence into the organization's operating model.
CDAOs have the opportunity to influence strategy with the CEO and leadership, driving accountability across the enterprise.
It's time to move from theory and research to action and operationalization. Many agentic AI and automation vendors offer trials and pilots to help validate proof before wider adoption.
Gen AI will empower organizations to compete better through efficiency, scale and governance. And someone needs to own how it's benchmarked, implemented, governed, and measured.
That someone is the CDAO.
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