"Evidence suggests it's tough to generate value from AI projects, but one thing we can be sure of is that successful initiatives require data -- and lots of it. Whether it's running a generative AI rollout or exploring agentic AI, the language models that power emerging technology solutions require access to vast informational resources. As businesses scale up their AI efforts through 2026 and beyond, having access to the right data assets has never been important."
"Paul Neville, director of digital, data, and technology at The Pensions Regulator (TPR), said his public body is "very thoughtful" about collecting data for emerging technology projects. "The result from AI depends on the data it's looking at," he said. "So, if you give it rubbish data, it'll give you rubbish back. That's very clear." Neville told ZDNET that the foundational elements -- good data practice, governance, and ownership -- help ensure his organization turns the right information into insight."
Generating business value from AI is difficult without substantial, appropriate data. Large language models and other AI systems require access to vast informational resources to perform effectively. Organizations must adopt thoughtful data collection strategies that prioritize high-value, accurate inputs while maintaining strong data practice, governance, and clear ownership. Businesses should monitor external model developments and be prepared to adapt as models and business requirements change. Strategic focus on the right data assets enables better insights, supports scalable AI efforts through 2026 and beyond, and reduces the risk of poor outcomes driven by low-quality data.
Read at ZDNET
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