IBM CDO Study 2025: The AI Multiplier Effect - Analysis & Insights

80/20 Summary: What Really Matters

A late 2025 IBM study surveyed 1,700 data leaders across 27 countries and reveals a fundamental shift in how organizations must think about data. The central thesis is simple but profound: in an AI-first world, data is no longer just an asset to manage—it's fuel for competitive advantage. The CDOs who understand this are pulling ahead fast. The study identifies a critical gap. While 92% of CDOs know they must focus on business outcomes, only 29% have clear measures to determine the actual business value their data creates. This is the fog most organizations are navigating in, they know data matters, but they can't articulate exactly how much or where.

The research reveals five focus areas where leading CDOs are concentrating their efforts: Strategy (deploying data on a mission, not just collecting it), Scale (giving AI agents fast access to data), Resilience (building unbreakable data pipelines), Innovation (delivering data to every desk), and Growth (spotting breakthroughs in proprietary data). Organizations excelling in these areas deliver measurably higher ROI on both data and AI investments.

What separates leaders from laggards? Leaders are 25% more likely to clearly articulate how data priorities facilitate business outcomes. They're 20% more likely to view employee data access as less risky than limiting access. They're also more likely to have integrated their data strategy with their technology roadmap and infrastructure investments.

The study finds that 81% of CDOs now prioritize investments that accelerate AI capabilities, and 13% of IT budgets now go to data strategy (up from just 4% in 2023). This represents a massive reallocation of resources toward data infrastructure. But here's the catch: only 26% are confident their data capabilities can support new AI-enabled revenue streams. The gap between ambition and execution remains wide.

The Pain Points: Where Organizations Are Struggling

The report exposes several acute pressure points that are holding organizations back from realizing AI's potential.

The Measurement Problem: CDOs are flying blind when it comes to ROI. While nearly everyone agrees they need to be outcome-focused, fewer than three in ten have the metrics to prove what's working. This creates a vicious cycle where data investments can't be defended with hard numbers, making it harder to secure future funding.

The Data Quality Crisis: The top barriers to using enterprise data for AI are accessibility, completeness, integrity, accuracy, and consistency. These aren't new problems, but AI amplifies their consequences. When a human analyst encounters incomplete data, they work around it. When an AI agent encounters the same gap, it perpetuates and scales the error across thousands of decisions.

The Talent Drought: This one is getting worse, not better. 77% of CDOs say they're struggling to fill key data roles, and only 53% say their recruiting delivers the skills they need—that's down sharply from 75% just one year ago. What's more, 82% are hiring for data roles that didn't exist last year related to generative AI. The skills landscape is shifting faster than organizations can adapt.

The Silo Trap: Finance has their data. HR has theirs. Marketing, supply chain, and legal each operate in isolation with no common taxonomy, no shared standards, and no end-to-end visibility. As Ed Lovely, IBM's VP and Chief Data Officer notes in the foreword, every AI initiative in this environment becomes a six-to-twelve-month data cleansing project. Teams spend more time hunting for and aligning data than generating meaningful insights.

The Unstructured Data Wall: Only 26% of CDOs are confident their organization can use unstructured data in a way that delivers business value. This is a massive blind spot because unstructured data, emails, call transcripts, documents, images, often contains the richest context. A CRM system tells you what a customer bought. An email tells you they were frustrated or delighted.

The Access Paradox: Organizations know they need to democratize data (80% say it helps them move faster), but they're terrified of the security implications. Only 67% say their role is more focused on enabling use than preventing misuse. This tension between openness and control is paralyzing decision-making.

Why This Matters: The Stakes Are Higher Than Most Realize

This study matters because it documents a winner-takes-most dynamic that's already underway. Organizations that crack the code on data-powered AI won't just perform slightly better than competitors, they'll operate in a fundamentally different way. The research shows that AI agents are becoming primary data consumers, not just tools. 83% of CDOs say the benefits of deploying AI agents outweigh the risks, and 77% say they're comfortable with their organization relying on outcomes from AI agents. This represents a major psychological shift. Two years ago in the 2023 study, only 44% said their leadership trusted the data the organization collected.

But here's why speed matters: the gap between leaders and laggards is widening, not narrowing. Organizations that deliver higher ROI on AI and data investments are already 25% more likely to articulate how data facilitates business outcomes. They're building compounding advantages, better data leads to better AI, which generates better insights, which attracts better talent, which builds better data products. Meanwhile, organizations stuck in the silo trap are spending six to twelve months on data cleanup for each AI initiative.

The study also reveals that proprietary data is becoming the new moat. 78% of CDOs say leveraging proprietary data is a top strategic objective to differentiate in the market, and 72% of CEOs say proprietary data is key to unlocking the value of generative AI. As general AI capabilities commoditize, the unique datasets each organization possesses become the differentiator. You can't buy your way out of this problem—you have to build it.

The talent crisis adds urgency. If organizations are already struggling to fill roles, and those roles are evolving every twelve months, the window to build capability is narrow. The organizations that solve for talent now will be positioned to execute faster for years to come.

Aha Moments: Insights That Reframe the Conversation

Several findings in this study challenge conventional wisdom and should prompt organizations to rethink their approach.

Aha #1: Data Strategy Is Now AI Strategy: The research shows 81% of CDOs say their data strategy is integrated with the technology roadmap and infrastructure investments, up from just 52% in 2023. This isn't incrementalism—it's a wholesale reframing of what data teams do. The CDO role has evolved from custodian to product partner. CDOs must now know which AI use cases are most valuable and what data powers them, then ensure that data can be used repeatedly to drive targeted outcomes across the enterprise.

Aha #2: Bring AI to Data, Don't Move Data to AI: 81% of CDOs now say they bring AI to data rather than centralizing data for AI. This is a major architectural shift. The old model was to create a data warehouse or lake where everything lives. The new model recognizes that moving data creates costs, latency, and security risks. Instead, AI should be deployed where data already sits. This requires a completely different infrastructure approach, often involving hybrid cloud architectures.

Aha #3: The Risk of Limiting Access Exceeds the Risk of Granting It: Leading CDOs are 20% more likely to say the risk of limiting employee access to enterprise data is greater than the risk of giving employees broad access. This inverts the default posture from "deny unless necessary" to "enable unless risky." It's a profound cultural shift that requires sophisticated governance, but organizations that make this leap are moving faster.

Aha #4: Data Democratization Is an AI Accelerant: 80% say giving employees access to data helps their organization move faster, and 82% say they're wasting data if people can't access it to make decisions. The connection to AI is direct: when employees can access data freely, they can train and deploy AI agents faster. When data is locked behind request processes, every AI initiative becomes a negotiation.

Aha #5: Unstructured Data Is the Untapped Gold Mine: While only 26% are confident they can extract value from unstructured data, this represents the biggest opportunity. Irene Yusta Martín, CDO of MasOrange, notes that technology now allows them to treat unstructured data "almost like structured data, including customer communications from call centers and WhatsApp or messages." Organizations that master this can build AI with far richer context than competitors working only with structured databases.

Aha #6: AI Agents Need Data Products, Not Raw Data: The study emphasizes that leading organizations are creating "data products", packaged, reusable data assets that solve specific business problems. For example, a customer health score that combines payment history, purchase volume, and renewal patterns becomes a product that both humans and AI agents can consume. This is more efficient and more governable than giving everyone access to raw transactional data.

Aha #7: Ecosystem Data Multiplies Proprietary Value: 83% say strategic partnerships enhance data capabilities and promote innovation, and 82% say partnerships accelerate AI initiatives. The insight here is that proprietary data becomes even more valuable when combined with partner, supplier, or complementary business data. A retailer's sales data combined with a supplier's inventory data creates insights neither could generate alone.

Recommendations: What Organizations Should Do Now

The study offers both explicit recommendations and implicit guidance based on what's working for leaders. Here's what organizations should prioritize.

1. Establish Clear Business Value Metrics Immediately The 92% who know they need to be outcome-focused but the 29% who have clear measures represent the most urgent gap. The report recommends establishing KPIs that link data initiatives directly to business outcomes like increased sales conversions, reduced customer churn, or operational cost savings. Implement regular ROI reporting that quantifies the financial impact of data projects. This isn't just about justifying budgets—it's about creating feedback loops that tell you what's actually working.

2. Optimize Your Data Estate for AI, Not Just Storage The recommendation is to understand what data is required within each workflow, then assess that data's quality and whether AI can access and use it. Use predictive analytics to anticipate future data needs. The insight here is that most organizations still have data architectures designed for human consumption and batch reporting. AI needs real-time access, clean schema, and the ability to work across silos. Audit your data estate through an AI lens.

3. Build a Scalable Enterprise Data Architecture Invest in a unified data ecosystem with modern composable platforms that integrate core data sources with real-time access and governance. The study points to hybrid-by-design cloud strategies that let you deploy AI workloads wherever needed—cloud, on-premises, or edge. The goal is to create data pipelines, not data warehouses. Data should flow to where decisions are made.

4. Develop Data Products with Clear Ownership Design data products that address business unit pain points and decision-making needs. Establish clear ownership, user feedback loops, and continuous improvement cycles. The study emphasizes treating data products like software products—designed to attract users, drive adoption, and deliver intuitive value. This shifts the mindset from "data warehouse everyone pulls from" to "curated products that solve specific problems."

5. Create an AI Agent Marketplace Establish a centralized screening process for AI agents before releasing them for widespread use. This helps organizations scale productivity gains while avoiding the proliferation of agents that could duplicate work, diverge from governance guidelines, or share sensitive information beyond intended scope. The study notes that organizations with centralized or hub-and-spoke AI operating models see 36% higher AI ROI than those with decentralized models.

6. Deploy AI Agents to Monitor and Improve Data Quality Phase in AI agents that continuously monitor data quality, flag anomalies, and suggest improvements. Integrate their findings into your governance framework. The study recommends creating a feedback loop where AI identifies issues that data stewards review and mitigate. This turns data quality from a periodic audit into a continuous improvement process.

7. Make Every Role a Data Role Promote an organizational mindset focused not just on how to use AI tools, but how best to apply them. Power every job with data and make it integral to how tasks are executed and decisions are made. Invest in intuitive data interfaces and user-friendly analytics tools that simplify interaction for non-technical users. The study found that organizations with broader data literacy move faster on AI initiatives.

8. Address the Talent Crisis Strategically Make your organization a talent magnet by fostering a data-driven culture. Promote data literacy programs in recruitment materials and show candidates with crucial data skills how they can progress on a clear career path. The study notes that 82% are hiring for data roles that didn't exist last year, this means you need to build talent pipelines, not just fill requisitions. Partner with universities, create apprenticeship programs, and build internal training.

9. Unlock Insights from Unstructured Data Deploy natural language processing, computer vision, and machine learning tools to automatically extract meaning from text documents, images, videos, and audio files. Use optical character recognition and document intelligence platforms to digitize and analyze paper-based records, contracts, and forms. This is where the biggest delta exists between current capability and potential value.

10. Establish Data Sharing Frameworks with Ecosystem Partners Create clear data sharing and collaboration frameworks that define terms and conditions with ecosystem partners, including governance, security, and compliance. The study found that 83% say partnerships enhance data capabilities, but these relationships need structure. Define what data can be shared, how it will be secured, who owns derived insights, and how value will be split.

11. Address Data and AI Sovereignty Proactively The study found that 82% view data sovereignty as critical to risk management strategy. Organizations are introducing data encryption and access controls (32% operational), engaging legal counsel for compliance (35% operational), establishing data retention and deletion protocols (31% operational), conducting regular audits (31% operational), and implementing localized data management policies (33% operational). These aren't optional, regulatory pressure is increasing globally.

12. Partner Closely with CISOs on Security by Design The study emphasizes that CDO-CISO collaboration is essential. 87% of executives say effective data security is essential for AI investments, but only half can protect sensitive data in most AI use cases. The recommendation is to ensure data strategies are secure by design, balancing accessibility, governance, and protection. This builds stakeholder trust and reduces regulatory and reputational risk.

Final Thoughts: The Window Is Narrow

This study documents a critical inflection point. The organizations that build integrated data architectures, democratize access responsibly, develop strong data products, and attract top talent over the next 12-24 months will establish advantages that compound for years. Those that continue operating in silos, treating data as an IT problem rather than a strategic asset, will find themselves in a position where catching up becomes prohibitively expensive.

The most important meta-insight is that this isn't primarily a technology problem, it's an organizational design problem. The technology exists to integrate data across silos, to govern access intelligently, to extract insights from unstructured content, and to deploy AI agents at scale. What's missing is the operating model, the culture, the metrics, and the talent to execute on that technology.

The CDOs who succeed won't be the ones with the most sophisticated tools. They'll be the ones who can clearly articulate how data creates business value, who can work across functional boundaries to break down silos, who can attract and develop talent in a scarce market, and who can shift their organization's mindset from data ownership to data stewardship. That's the hard work ahead, and this study provides the roadmap for how to begin.