5 Ways Businesses Can Collect Data for High-Value AI Results
As enterprises accelerate AI adoption, data quality is emerging as one of the biggest determinants of success. Industry leaders say that while AI systems require massive datasets to function, the real challenge is identifying, collecting, and preparing the right information. Poor inputs — “rubbish data in, rubbish data out” — remain a major blocker to AI ROI, especially as organizations scale toward more complex AI and agentic systems through 2026 and beyond.
Experts highlight five strategic practices for building AI-ready data foundations:
- Paul Neville of The Pensions Regulator stresses “thoughtful” data collection supported by strong governance and ownership. With AI models evolving rapidly, organizations must adapt processes as platforms like OpenAI and Azure change their outputs.
- RAC data leader Ian Ruffle argues that teams should avoid hoarding everything and instead concentrate on information tightly tied to core business processes, especially for forecasting, customer service, and operational automation.
- PageGroup CIO Dominic Redmond notes that the data required for AI today may differ from what’s needed in one or two years. Successful companies maintain a clear plan while remaining agile as AI capabilities and market needs shift.
- Joseph Joseph’s supply chain chief Sacha Vaughan emphasises using granular customer insights — such as reviews and complaints — to drive product design and operational refinements.
- Boomi CEO Steve Lucas explains that businesses often have adequate data, but lack the semantic structure needed for AI to interpret patterns. Cataloging, tagging, and metadata provide the context models need to deliver accurate results.
Across all insights, leaders agree: storing everything is neither realistic nor useful. AI success hinges on strategic data curation, flexible planning, and semantic structure — not volume alone.
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