The Data Advantage: Why Systems of Record Will Win the AI Race

The shift from AI experimentation to AI operationalization

Most organizations have moved beyond experimenting with AI and are now focused on scaling it across the enterprise. Yet many initiatives stall when teams attempt to move from pilots to production. The challenge is rarely the AI model itself. More often, it is access to trusted data, integration with existing systems, and the ability to maintain governance, security, and compliance requirements.

Organizations face common barriers when scaling AI:

• Security, privacy, and regulatory concerns
• Limited access to trusted enterprise data
• Integration challenges across existing systems and workflows
• Complexity within mainframe and other core systems
• Data quality and governance gaps that impact AI outcomes  

These challenges highlight an important reality: AI readiness depends on data readiness.  

Why systems of record matter more than ever

For years, many organizations viewed core systems as legacy technology that needed to be replaced. Today, that view is changing. Systems of record contain decades of business knowledge, transactional history, operational context, and trusted data that AI systems need to deliver meaningful results.  

Trusted enterprise data creates competitive advantage:

• Historical transaction data provides critical context for AI-driven decisions.
• Mainframe systems contain some of the most accurate and reliable business data within the enterprise.
• Data governance, lineage, and auditability help organizations build trust in AI outcomes.
• Connected data across mainframe, distributed, and cloud environments enables more complete business insights.  

Organizations that successfully connect their systems of record to AI initiatives will be better positioned to turn AI investments into measurable business outcomes.  

What makes data strategic

Modern AI requires more than large volumes of information. Organizations need data that is trusted, accessible, and governed.

Rocket identifies five characteristics that transform data into a strategic business asset:

• Context: Understanding what data represents and how it supports business processes.
• Lineage: Knowing where data originated, how it changed, and how it is used.
• Governance: Applying security, compliance, and policy controls to support trusted AI.
• Relationships: Connecting data across systems to create a complete operational view.
• Historical Records: Leveraging years of transaction and operational history to improve AI accuracy and decision-making.  

Building an enterprise data advantage

Organizations do not need to replace core systems to become AI-ready. Instead, they can extend the value of existing investments by making trusted operational data available across the enterprise.

Rocket Software helps organizations:

• Connect data across mainframe, distributed, and cloud environments.
• Improve data accessibility while maintaining governance and control.
• Support AI initiatives with trusted, high-quality enterprise data.
• Modernize without disrupting mission-critical systems.
• Enable secure, scalable access to systems of record.  

The goal is not simply to move data, but to make trusted data available when and where it is needed to support AI, analytics, and business innovation.