AI’s potential to transform customer experiences is undeniable, reflected in growing enterprise investments across industries. Yet a disconnect persists: according to a recent Informatica survey of over 300 IT and business leaders exploring current perceptions of AI, CX and data, only 33% of business professionals view AI as critical to customer experience. This is despite 87% of data leaders ranking AI as a board-level priority. Even more concerning, fewer than half of organizations can demonstrate measurable impact from their AI initiatives.

This gap underscores a fundamental truth for CDOs: success with AI depends squarely on the readiness of underlying data. Without high-quality, well-governed data, AI-driven insights and personalized experiences remain unattainable. Yet confidence in data quality varies widely — 69% of IT teams rate their data quality highly, while barely half of business users concur — highlighting a critical misalignment.

As organizations rush to expand AI capabilities, overlooking data readiness jeopardizes investments, limits actionable insights for business teams, and perpetuates fractured customer experiences.

For CDOs, this represents both a significant risk and an unprecedented opportunity to demonstrate strategic value by ensuring customer data is ready for AI agents and whatever AI innovations come next.

The hidden cost of “good enough” data

Data decays at a rate of up to 35% annually, presenting a relentless challenge for AI initiatives demanding continuous, accurate inputs. A global pharmaceutical company’s $10 million data cleanse was a short-lived fix; even then, leaders acknowledged data would degrade again, emphasizing that manual efforts are insufficient.

When poor data feeds AI models and AI agents, biased or inaccurate responses are generated, undermining customer trust and ultimately compromising the integrity of CX strategies. For CDOs, this is a critical risk that demands a shift from episodic data clean-up projects to sustainable, scalable data management capabilities.

4 Foundational practices for AI-ready customer data

Most organizations struggle to move beyond pilot AI projects, resulting in a gap between AI potential and customer impact. Bridging this divide requires deliberate action across four critical areas that elevate customer data from a liability to a competitive asset.

Practice 1: Silo-busting data architecture

The Informatica CX, AI, and data survey identified integration, accuracy, and consistency as the top three data readiness concerns – symptoms of siloed systems and fragmented data that prevent organizations from achieving a comprehensive view of customers. 

A unified data architecture consolidates CRM, support, marketing, and sales data, enabling seamless cross-functional insight. This is a complex business transformation spanning people, processes, and technology. When done right, it creates the foundation for real-time personalization and consistent customer experiences.

Practice 2: Automated data quality and governance

Manual data management simply can’t scale to AI’s demands. CDOs must champion AI-powered data validation, lineage tracking, duplicate detection, mastering, and governance workflows that preserve data integrity continuously.

This practice ensures AI models receive clean, consistent inputs while enabling a true 360-degree view of customers across all touchpoints. It shifts data quality from a periodic project to an ongoing capability that supports business agility.

Practice 3: Democratized data access with security controls

A striking 57% of business professionals don’t understand what “AI-ready” data means. This knowledge gap creates a barrier to adoption and limits the business value of data investments.

Self-service data marketplaces, AI-powered interfaces, and data literacy programs address the challenge of making AI-ready data accessible to non-technical users. By leveraging advanced metadata intelligence, automated data profiling, and enterprise governance, these solutions enhance data understanding without compromising security.

Business teams gain faster time-to-insight while CDOs maintain control and oversight — balancing empowerment with governance.

Practice 4: Continuous monitoring and feedback loops

Data readiness is not a milestone but a discipline requiring real-time monitoring aligned to business outcomes. Effective CDOs drive collaboration between IT and business teams through shared accountability and metrics that link data health to tangible business impact.

While project delivery timelines or system uptime is a standard, Informatica’s IT team, for example, measures success by monitoring business outcomes that track to data quality. This includes “pipeline dollar trending from enriched contact records” and “email bounce rates.”

As their CIO puts it, “I don’t care whether your project was done on time if we didn’t achieve the business objective.” These metrics create immediate feedback loops that drive continuous improvement.

Breaking down the collaboration barrier

The research reveals an even deeper problem: a collaboration crisis. Only 30% of business users feel they work effectively with IT on data initiatives, while 74% of IT professionals believe they collaborate well with business teams. This misalignment hampers data initiatives’ success.

Forward-thinking organizations are redefining how their data leaders engage with business functions. Leading CDOs proactively embed data expertise into marketing and sales operations, prioritize projects by measurable customer impact, and facilitate joint planning, shared KPIs, and ongoing dialogue. This cultural evolution is essential to align data investments with revenue goals and accelerate AI’s time to market.

By facilitating these connections and demonstrating how data excellence drives customer outcomes, CDOs can establish themselves as indispensable partners in organizational growth.

Measuring what matters

Success hinges on tracking both technical and business metrics. Technical benchmarks — data accuracy, timeliness, and completeness — are critical, with examples like email bounce rates reflecting data health.

Business outcomes such as pipeline generation, customer retention, and revenue per customer demonstrate data’s strategic impact — with top performers seeing up to 40% of their pipeline from automated AI-driven prospecting.

A case in point: Brazilian financial services company Rodobens surpassed its ROI target by 182% within six months of adopting unified customer data management, generating an estimated $40 million in additional annual revenue and reducing IT maintenance hours by 50%. This success illustrates how strong IT-business collaboration — anchored by relevant measurement — accelerates AI initiatives.

Your strategic next steps

CDOs face a pivotal opportunity. By ensuring data is truly ready for AI — not just available, but trusted, integrated, and governed — they can accelerate AI adoption across the organization. Poor data quality doesn’t just limit AI effectiveness; it erodes confidence in AI initiatives entirely, slowing adoption when speed to market matters most.

Start by auditing the gap between IT data confidence and business realities. Choose one critical customer journey for end-to-end data integration, aimed at clear business outcomes. Build measurement frameworks that tie data investments to customer experience metrics.

Most importantly, remember that data AI-readiness isn’t a destination, but an ongoing capability. Organizations embedding these practices will transform customer data from an operational input into a true competitive advantage.

The AI revolution is moving forward, whether your data is ready or not. Some organizations are still planning their AI future, while those with mature AI-ready data foundations are already reaping benefits in customer experience and business growth. 

The question for CDOs is: Which group will your organization join? 

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This article draws insights from Informatica’s comprehensive research on AI readiness and customer data management. For a deeper dive into the survey findings, including detailed implementation frameworks and additional case studies, download the full white paper: “From Data Silos to AI-Enabled Customer Engagement: What 300+ Business and IT Leaders Reveal About Managing Data for AI & CX,” authored by Kerry Bodine, Bodine & Co. 

About the Author:

Monica Mullen is Director of Product Marketing at Informatica, specializing in Master Data Management (MDM) and Customer 360 within the Intelligent Data Management Cloud (IDMC) platform. She leads the Customer Experience Optimization go-to-market program, demonstrating how strategic data initiatives enhance customer insights and drive CX improvements.

Mullen collaborates with clients and partners to develop data strategies that tackle complexity and foster customer centricity, engagement and growth. With 25 years in data management, she champions reliable data as essential alongside people, processes and technology. She advocates that companies leverage their data’s unique strategic potential to empower teams — from the front line to executive offices — consistently achieve optimal outcomes regardless of the challenge.