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AI adoption exposes gaps in information management practices, Info-Tech Research Group finds
Fragmented data systems and outdated retention policies are blocking AI value, according to new Info-Tech Research Group research. The firm released a four-phase framework to help CIOs fix information management before scaling AI.
AI Reveals Information Management Gaps Holding Back Business Value
Organizations adopting AI are discovering that their information management practices can't keep pace. Info-Tech Research Group's latest research shows that fragmented systems, outdated retention policies, and siloed approaches to data and content are blocking the value AI can deliver.
The firm published a blueprint this week outlining a four-phase framework to help IT leaders and CIOs build information management practices ready for AI. The work addresses a real problem: many organizations still treat structured data, unstructured content, and organizational knowledge as separate domains, even as AI erases those boundaries.
Where Current Practices Fall Short
Three challenges stand out for IT leadership. First, the sheer volume of information makes it hard to identify where AI can create the most value. Second, data teams, content managers, and knowledge leaders operate with different principles and terminology, preventing a unified strategy. Third, leaders struggle to quantify the business case for improved information management, making it difficult to secure funding.
Poor classification and inaccessible information also create compliance, security, and reputational risks. When organizations can't reliably find or trust their information, AI tools built on that foundation inherit the same problems.
A Structured Approach to Modernization
Info-Tech's framework moves through four phases. In Phase 1, CIOs and information leaders define key business areas, establish a unified framework, and identify which information assets matter most. Phase 2 applies AI to solve high-impact problems through automation, search, and decision support.
Phase 3 focuses on building a business case. Finance leaders and cross-functional teams quantify efficiency gains, cost savings, and risk reduction to prioritize initiatives. Phase 4 activates the approach with clear timelines, KPIs, and executive alignment.
The blueprint includes a prioritization matrix, an ROI tool, and a C-suite presentation template designed to secure buy-in from executives.
What This Means for Management
For managers overseeing information systems or data operations, the takeaway is straightforward: AI amplifies existing information management problems rather than solving them. Organizations need to address fragmentation and governance before scaling AI initiatives.
IT leaders considering how to position information management for AI growth should review AI Learning Path for CIOs or explore broader AI for Management resources to understand how information strategy connects to organizational AI readiness.