Info-Tech Research Reveals Four Key Data Initiatives to Strengthen AI in 2026

As organizations enter 2026, many face mounting pressure to deliver measurable value from AI initiatives while managing tighter budgets and navigating increasing regulatory scrutiny. According to Info-Tech Research Group, unresolved foundational issues around data quality, governance, and literacy continue to slow progress and undermine confidence in analytics and AI-driven decision-making.

To help leaders address these challenges, Info-Tech Research Group has released Data Priorities 2026, a new report providing CIOs and CDOs with a structured roadmap of the most critical data initiatives needed to improve execution, clarify accountability, and build trust in data as AI adoption grows.

"Feed AI and automation noise, and they'll scale confusion; feed them clarity, and they'll scale intelligence," says Pooja Khandelwal, Senior Research Analyst at Info-Tech Research Group. "Organizations that focus on trustworthy data, clear governance, and an outcome-based data culture will see faster returns and reduced risk. Without these data foundations, even the most advanced AI and IT initiatives will stall or fail to deliver value."

Key Insights from Data Priorities 2026

Info-Tech's report emphasizes that AI and automation scale the condition of the data they rely on, increasing the operational and strategic impact of poor-quality or poorly governed data. The firm's Future of IT survey data shows that 40.9% of leaders cite improving data governance as one of their top data priorities for 2026, even beyond AI-specific initiatives. The report also reflects broader pressures shaping data agendas in 2026, including economic uncertainty, regulatory change, and the continued growth of enterprise data environments. Together, these factors are raising expectations for stronger discipline and clearer ownership across data programs.

To help enterprises move from fragmented data efforts to consistent, value-driven execution, Data Priorities 2026 identifies four essential priorities for IT leaders:

1. Enable Enterprise-Wide Accountability
   Implement a unified governance framework for data and AI to reduce fragmentation, clarify responsibility, and support consistent decision-making across the enterprise.

2. Embrace Customer-Centricity for Data
   Treat data as a reusable, outcome-focused asset, enabling faster delivery, improved scalability, and stronger alignment with business objectives.

3. Build a Trusted AI-Ready Data Supply
   Strengthen data quality and reliability to ensure analytics and AI initiatives are powered by reliable, transparent, fit-for-purpose information.

4. Cultivate Your Data Champions
   Foster data and AI literacy, encourage collaboration, and embed data-driven thinking throughout the organization to build a more adaptable, resilient workforce.

"Turbulence in the economic and regulatory landscape will continue into 2026," adds Khandelwal. "But through strategic data leadership, CIOs and CDOs can position themselves to proactively minimize risks, optimize costs, and drive innovation and transformation so that IT delivers measurable value."

The report also provides frameworks, operating models, and practical guidance to help organizations move toward more consistent, value-oriented data practices. By applying these insights, IT and data leaders can build trust in data, support responsible AI adoption, and enhance enterprise-wide decision-making.

Source: Info-Tech Research Group

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