Artificial Intelligence (AI) is poised to radically transform government services. From automating routine tasks to enabling smarter decision-making, AI promises to revolutionize public sector service delivery, improve citizen experiences, and drive efficiency across agencies.  

But a sobering trend is quietly emerging: many government AI projects stall or fail to deliver meaningful results. The reason isn’t a lack of vision or funding; it’s the reality that most agencies lack the data management foundation necessary for AI to succeed. 

Between 70% and 85% of AI projects fail to meet their expected outcome. Public sector and enterprise initiatives consistently face even higher risks when data management is lacking. 

Why data management is crucial to government AI projects

Government agencies must confront the state of their data to deliver on the promise of AI. They can do this by addressing:  

• Siloed and fragmented information: Decades of legacy systems, paper records, and disconnected databases have created fragmented data environments. AI systems require unified, accessible, and high-quality data to function effectively. 

• Data quality challenges: Poor data quality, redundancy, and outdated records undermine the accuracy and reliability of AI-driven insights. As industry experts note, “garbage in, garbage out” is especially true for machine learning and analytics. 

• Compliance and security risks: Sensitive government data must be managed with strict attention to privacy, compliance, and security. Without robust data governance, AI initiatives risk exposing agencies to regulatory and reputational harm. 

The lesson is clear: no matter how advanced AI technology is, it cannot compensate for disorganized, incomplete, or inaccessible data. For public sector leaders, investing in a comprehensive data management strategy is not a nice-to-have strategy; it’s the foundation for any successful AI initiative. 

Why AI needs clean, accessible data 

For AI to deliver on its promise in government, agencies must ensure their data is high-quality, well-governed, and readily accessible. Even the most advanced commercially available AI systems run off the rails when relying on poor-quality data, providing inaccurate analysis or even creating hallucinations.  

The foundation: AI’s dependence on quality data 

• Training and automation: 
  AI and machine learning models require large volumes of accurate, well-structured data to learn, adapt, and automate processes. If data is incomplete, outdated, or siloed, AI systems can’t function effectively or deliver meaningful results. To deploy AI and derive insights from government data, it’s important to train the tool against accurate information sets. 

• Bias and reliability: 
  Poor data quality can introduce bias, errors, or blind spots into AI-driven decisions, undermining trust and potentially leading to costly mistakes in public sector operations. 

Over 80% of government departments experience data silos, leading to inefficiencies in service delivery and complicating cross-agency initiatives. 

Building a data management strategy for AI  

To unlock the full potential of AI in government, agencies must develop and implement a robust data management strategy. This helps you ensure that data is organized, accessible, and reliable, with key prerequisites for effective AI training, automation, and decision-making. 

 Data inventory and classification: Know what you have and where it lives

• Conduct a comprehensive data inventory to identify all data assets across departments, systems, and formats. 

• Classify data based on sensitivity, usage, and relevance to AI initiatives. 

Understanding the data landscape helps agencies prioritize efforts, reduce duplication, and ensure compliance.  

Expert Tip: Manually classify early-stage data, and let trusted tools automatically categorize high-volume data sets.

Archiving and retiring legacy data: Freeing up resources and reducing noise 

• Identify inactive or obsolete data that no longer needs to be in active systems but must be retained for compliance or historical purposes. 

• Archive or retire legacy data to reduce storage costs, improve system performance, and minimize data clutter that can confuse AI models. 

• Implement policies for defensible deletion where appropriate to manage the data lifecycle responsibly. 

Expert Tip: Some agencies have funded AI initiatives by shifting operating funds from contractors supporting legacy applications after archiving the system data. 

Digitizing and integrating paper records for AI-readiness 

• Convert paper-based records into digital, searchable formats to make them accessible for AI analysis. 

• Integrate digitized records with existing digital data repositories to create a unified data environment. 

This step is critical for public sector agencies with extensive paper archives, enabling AI to leverage all available information. 

Expert Tip: Do not simply scan paper records into electronic images. Rather, use intelligent capture tools to automatically route the scanned document into workflows or archives, where the data can be readily accessible with no further action. 

Continuous data quality monitoring and improvement 

• Establish ongoing processes to monitor data quality, accuracy, and completeness. 

• Use automated tools and AI-powered data cleansing solutions to detect and correct errors. 

• Regularly update data governance policies to adapt to evolving compliance requirements and technological advances. 

Government agencies lay the groundwork for AI success by building a comprehensive data management strategy and powering AI initiatives with clean, accessible, well-governed data. 

Expert Tip: Establish a data governance board comprised of agency, legal, technology, and mission personnel. 

The role of modern content management 

Intelligent content management systems help agencies organize, classify, and govern their information, turning “dark data” into actionable insights. These platforms automate data pipelines, ensure compliance, and provide the foundation for AI-driven innovation in government services. Agencies that have adopted AI-powered content management report improved efficiency, better decision-making, and enhanced citizen experiences. 

How OpenText can help 

Before launching any AI initiative, take a critical look at your agency’s data quality, accessibility, and governance practices. This does not require a thorough assessment from a consulting firm; a simple in-house evaluation can make a major difference. This foundation ensures your AI projects are built on solid ground, maximizing impact, minimizing risk, and setting your organization up for long-term digital success. OpenText Core Content Management is designed to help public sector organizations: 

• Inventory and classify data: Centralize information across legacy systems and paper archives, making it searchable and AI-ready. 

• Retire and archive legacy data: Move inactive or obsolete records to secure and compliant archives, reducing costs and operational noise. 

• Digitize and integrate paper records: Convert paper documents into digital formats, enabling seamless integration with AI and analytics platforms. 

• Enforce data governance: Apply robust policies for data quality, security, and compliance, ensuring your data is trustworthy and accessible for AI initiatives. 

By partnering with OpenText, your agency can accelerate its journey toward effective AI adoption, confident that your data foundation is secure, compliant, and optimized for innovation. Learn more about how OpenText can support your mission to deliver smarter, safer, and more cost-effective public services. 

The post How AI in government is doomed to fail without a data management strategy  appeared first on OpenText Blogs.