AI adoption is rapidly accelerating, but data quality and data governance remain two of the biggest challenges to successful implementation. While AI depends on vast amounts of data, the quality and accuracy of the information it processes directly determine its effectiveness. Strong data governance allows organizations to mitigate various threats, while poor data quality leads to inaccurate insights and inefficiencies.
Recent studies show that 98% of organizations have encountered AI-related data issues, including data silos, duplication, and compliance risks. All of them weaken AI outputs and their effectiveness.
Without access to production-ready data, AI projects frequently suffer cost overruns, unreliable results, and lower-than-expected ROI.
Despite these challenges, many companies continue investing in AI without first ensuring that the data is ready. This disconnect highlights a critical issue: Businesses recognize AI’s value but often overlook the foundational data challenges that determine its success.
AI Is Only as Good as Its Data
AI investment is high, but confidence in its implementation is mixed. While 74% of businesses plan to invest in AI this year, only 24% consider it a high priority. Furthermore, less than half of organizations (46%) believe their AI goals are realistic.
Leadership fragmentation also slows AI progress. While CEOs (23%) and CTOs (30%) claim ownership of AI strategy, chief data officers (CDOs) are the most skeptical, with 35% expressing doubts about their organization’s AI initiatives.
This disconnect illustrates the growing need for stronger collaboration between business and data leaders to ensure that AI strategies are built on a solid foundation.
For the 98% of organizations that have encountered AI-related data challenges, these issues include compliance concerns (27%), duplicate records (25%), and inferior data integration (21%). Even more concerning, nearly half (47%) allow employees to use AI in non-private environments, increasing security and compliance risks.
Without strong data foundations, AI will fail to deliver on its promises. Organizations have historically spent too much time preparing data rather than unlocking business value from it. However, all this is changing: Advances in AI-enabled semantic layers, data mesh, and intelligent allowing organizations to shift from manual processes to more streamlined, efficient data practices.
How to Make Data Production-Ready
For AI strategies to succeed, companies must rethink their data management approach. A proactive data strategy built on governance, automation, and cross-functional leadership is essential. These are the actions companies should take:
- Align leadership under a unified AI-data strategy: To help break down operational silos between IT, data teams, and business units, organizations must align their CIOs, CDOs, and CTOs under a unified AI-data strategy.
- Strengthen data governance and security: Currently, 47% of organizations permit AI use in non-private environments, which poses significant compliance risks. Companies must implement stronger governance frameworks and data access controls to mitigate these risks.
- Shift from manual data preparation to intelligent automation: Many organizations still use time-intensive, manual data cleaning and integration processes, creating inefficiencies and slowing AI adoption. Automated data management platforms improve accuracy, reduce processing time, and ensure reliable, production-ready data for AI applications.
- Implement scalable data strategies: Metadata management, data observability, and lineage tracking help organizations stay ahead of evolving AI regulations and compliance requirements, ensuring and maintaining long-term AI readiness.
- Employ the concept of “fit for use”: AI-ready data may not always be in shape for production of AI initiatives, and waiting for that certainty can bottleneck data science and AI experimentation.
The key to this is not treating your AI-ready data as a binary of Yes or No but being able to understand its risk profile and classify it to be fit for specific use contexts. This may include data that is ready to produce AI for experimentation for internal use only or to meet the quality and risk profile that makes it ready for production.
AI’s Real Value Lies in Execution
AI success isn’t just about investment; it requires a well-structured data foundation.
Companies must move beyond reactive data fixes to long-term data governance strategies. To make AI effective, companies should focus on these areas:
- Creating a culture of data literacy, ensuring employees at all levels understand data’s role in decision making
- Clarifying leadership responsibilities and aligning AI strategy with data priorities to prevent fragmentation
- Treating data governance as a core function, embedding governance into operations to ensure reliable, AI-driven decisions
Organizations should also embrace a more virtuous cycle, using intelligent automation and AI to improve data quality, which fuels AI use cases and leads to better, more reliable outcomes. Taking a proactive stance on AI ethics and compliance is increasingly important as AI technologies become more prevalent and powerful. This proactive approach helps organizations stay ahead of regulatory requirements and build trust with stakeholders.
Realize AI Ambitions
The AI-driven enterprise of today requires high-quality, production-ready data to thrive, but don’t let data challenges hold back your AI ambitions. Choosing a unified data platform that offers comprehensive solutions to ensure your organization’s data is production-ready is your best bet for mastering all AI applications.