Kevin Petrie
VP of Research at BARC US
Free Study
Harnessing Unstructured Data for AI Innovation: Problems, Practices, and Principles for Success
This BARC study examines how organizations make unstructured data usable for AI, and assesses the overall state of AI adoption. Based on 225 global responses, it shows what sets mature adopters and successful projects apart.
What this BARC study answers for data & analytics leaders:
- At what stage are organizations regarding critical AI planning/process initiatives?
- How do organizations measure the AI readiness of unstructured data?
- What are the main obstacles to success with managing unstructured data for AI?
- In which ways do organizations measure the success of unstructured data management activities that support AI?
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Pietro Grammatico, Director Service Management BI, Vorwerk
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Further information
This is the fourth edition in BARC’s global research series on AI maturity, conducted in February and March 2026. It is based on 225 cleansed responses from a globally distributed sample of data, analytics, and AI leaders across Europe and North America, spanning all relevant industries and company sizes.
The study builds on the prior BARC research Optimizing Your Architecture for AI Innovation (2024), Lessons from the Leading Edge (2025), and Preparing and Delivering Data for AI (2025) and now focuses on the next frontier of AI readiness: unstructured data.
Author(s)
Merv Adrian
Senior Analyst Data & AI
Drawing on four decades of experience that began in programming and evolved to roles in research leadership and market analysis, he provides strategic guidance on databases, cloud platforms, and AI/ML.
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With 70 percent of organizations able to discover and use less than half of their unstructured data for AI, this study shows leaders how to fix data quality and governance before their AI initiatives stall.