What Happens When the Business Stops Trusting Your Data? 

AI is raising the stakes for enterprise data

AI is raising expectations across every industry. Business leaders want faster decisions, better customer experiences, more automation, and measurable returns from AI investments.

But there's a problem.

Organizations are trying to accelerate AI adoption while still struggling with issues that have plagued data teams for years: inconsistent definitions, disconnected systems, shadow pipelines, governance gaps, and growing mistrust in enterprise data.

Teams spend more time validating data than acting on it

Business users build workarounds. Governance becomes something people bypass. And AI initiatives inherit the same data problems organizations have never fully resolved.  

In a recent CIO.com webinar sponsored by Rocket Software, Ray Sullivan, Vice President of Product Management for Rocket's DataEdge portfolio, and Greg Wilson, Director of Sales Engineering, shared what they're seeing firsthand as they work with businesses modernizing data architectures, integrating legacy systems, and preparing for AI. Rather than discussing theory, they explored the practical realities facing data teams today, and why those challenges become more visible, and more costly, in the age of AI.  

One insight from Ray Sullivan cuts straight to the heart of the issue: 

When business users stop trusting centralized data and start building spreadsheets, local extracts, shadow analytics workflows, and unsanctioned AI processes, the issue is no longer data quality. It's a loss of organizational trust.  

Five challenges data leaders continue to face

The webinar explores five challenges data leaders continue to face: 

• Why trust in enterprise data remains difficult to establish and maintain. 
• Why getting to insights often takes longer than the business expects. 
• How shadow IT and pipeline sprawl create hidden costs and operational risk. 
• Why governance often feels like friction instead of enablement. 
• What happens when organizations try to scale AI before their data is ready.  

The discussion on AI is especially timely. As organizations rush to demonstrate AI results, many are discovering that AI doesn't solve underlying data issues—it amplifies them. As Greg Wilson explains:

Why this matters for data leaders

For data leaders, architects, engineers, and governance professionals, that's the real challenge. AI assumes you've already solved trust, accessibility, governance, lineage, and data quality—but most organizations are still working through those foundations.

If your team is being asked to deliver trusted data faster, modernize legacy environments, support AI initiatives, and improve governance—all at the same time—this conversation is worth your attention.