Editorial
AI adoption is accelerating, but many organizations are discovering that momentum alone doesn’t guarantee results. Research shows that 55% of organizations have rolled out 100 or more AI use cases over the past year, yet only 19% are operationalizing the impact of AI and can demonstrate the value of AI in driving business goals.
This gap between AI activity and AI impact often comes down to an important barrier: data.
Whether it’s quality issues, silos, or gaps in data, these challenges make it nearly impossible to connect AI use cases and technology with broader business outcomes, actions and value. To get to AI Nirvana, most organizations have to go through Data Hell — and many are getting stuck there. As AI tools and systems proliferate, dismantling these silos and building high-quality, connected data foundations has never been more urgent.
Today's Reality: The Data Crisis Holding Back AI Transformation
Through my conversations with HR leaders, I consistently see organizations rolling out dozens of AI tools without addressing the underlying data quality issues that undermine their effectiveness. Many struggle with fragmented systems, poor data hygiene and inconsistent governance, which severely limits AI’s potential. Furthermore, many organizations simply don’t have enough data to support a given use case or AI model.
Consider knowledge base search — one of the highest volume activities in HR. An employee has a question about benefits, immigration or manager development. If there are three articles with conflicting information, the AI won't know which answer is correct because it's working with flawed data scattered across disconnected systems. This functionality is relatively easy to “turn on,” but won’t deliver value without foundational work to ensure a high-quality knowledge base containing accurate answers to employee questions.
Or take personalization. Organizations frequently try to customize learning experiences or tailor their responses to employee queries based on employee data (location, level, manager status, previous learning, skills). But if any of that information is inconsistent, incomplete or inaccurate across systems, personalization becomes less effective — ultimately delivering a poor experience. Why would an employee receive US answers about benefits when they live in Germany?
This creates a governance nightmare that fundamentally undermines AI's value proposition. Organizations need comprehensive data governance frameworks that establish clear ownership, accountability and quality standards across all data assets. This includes assigning specific ownership of key data bodies to individual teams or employees.
Take our knowledge base example:
- Who is responsible for the benefits knowledge base?
- Do they understand expectations for updates, accuracy and maintenance?
- Do they understand how AI uses this information?
Without clear data stewardship and integrated foundations, organizations find themselves constantly playing catch up, debugging AI outputs or dismissing use cases as “wrong” when they should be scaling AI impact.
Related Article: Is Your Data Good Enough to Power AI Agents?
How to Build Real-Time Data Intelligence
The good news is that organizations can solve these data challenges by building real-time data intelligence, leveraging data governance tools and redefining how HR and IT work together.
Leveraging AI effectively requires systematically preparing data for real-time, intelligent systems. Start by establishing comprehensive data inventory and lineage. It’s essential to know what data you have, where it's coming from and where it's going. This means mapping your entire data ecosystem — understanding dependencies, identifying redundancies and documenting how data flows through your systems. This is substantial work, and it will be one of the fastest-growing job areas in the near future.
Step 1. Examine Current AI Use Case Data
If you don’t know where to start, examine what data your launched AI use cases are using and try to establish governance for those specific data points. For example, if you want to launch AI search for your knowledge base, start by building real-time visibility and governance around that knowledge base. When we began implementing this use case in 2023, we decided to ensure our 100 highest-volume knowledge base articles were accurate and closely managed. Over the last two years, we’ve grown the ecosystem to manage our entire knowledge base. It can feel overwhelming to start, but find the right place to begin and start now!
Step 2. Build a Governance Framework
Next, build a robust governance framework that ensures ethical, secure and compliant AI use. Implement automated validation systems that catch issues before they impact AI performance. At my company, we use robotic process automation (RPA) to continuously monitor data quality, running automated consistency checks across different sources. When employee attributes should align across systems, automated processes check for discrepancies. We're running hundreds of these bots continuously, replacing manual data audits. This ensures that if a connection breaks or data gets out of sync, we know immediately.
Step 3. Establish Real-Time Monitoring
Real-time monitoring of data quality and completeness also becomes critical as AI systems scale. Organizations should implement an AI Control Tower to manage, govern and optimize AI initiatives across an enterprise. This provides enterprise-wide visibility into data quality for senior leaders with clear indicators that directly link data health to business outcomes, ensuring that data quality isn't managed in isolation but is part of the broader AI strategy.
Redefining the HR-IT Partnership
Real-time data intelligence requires more than technical infrastructure. The transformation to an AI-powered workforce demands that HR and IT work together in new ways. Traditional approaches where HR builds isolated solutions miss the broader opportunity for enterprise-wide intelligence. More and more, AI solutions are forcing us to knock down silos, and the most important use cases today cut across functions. We can’t manage these processes function by function.
Instead of building function-specific solutions that result in more data siloes, organizations must build technology that pulls from internal and external data sources across the enterprise. This approach acknowledges that the most valuable insights and AI use cases come from combining workforce data with information from other areas of the business. For example, when organizations want to understand how workforce changes affect technology roadmaps or sales performance, it’s essential that all relevant data is accessible, actionable and connected. Our AI solutions need all this data to function effectively.
This collaborative and centralized approach requires adopting a product mindset for data and treating workforce information as a strategic asset that enables business outcomes across the enterprise, not function by function. Data is THE core business asset — we need to understand that.
The key insight is that HR can't build AI-powered workforces alone. The technical expertise and infrastructure requirements demand true partnership with IT, built on shared understanding of business outcomes and strategic priorities.
Related Article: How Companies Can Prepare for an AI-Augmented Workforce
Charting a Data-Driven Future of Work
The call to action for HR leaders is clear: building competitive data advantages can't wait. Many organizations are jumping to AI without the data foundation, and I believe it's a major reason we hear about AI not delivering value today. Organizations that establish strong enterprise data foundations now will be positioned to leverage AI for genuine workforce transformation. Those that continue with fragmented, low-quality data approaches will find their AI investments delivering limited returns and declining adoption.
Your data will never be perfect, but each improvement in quality, connection and data coverage will expand the ROI of your AI investment. Don’t let perfection get in the way of progress, but completely ignoring data issues is not the right path forward.
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The human renaissance we're working toward — where AI amplifies human potential — depends on superior data foundations. When we have comprehensive, high-quality data about workforce capabilities, business needs and strategic priorities, we can design AI systems that genuinely enhance human work.
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