Without data integrity AI lacks ROI, says Precisely

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Data from Gartner’s Data & Analytics Summit reveals 50% of GenAI projects fail within the first year, with a further 40% of AI initiatives set to be cancelled before 2028.  With the industry facing a critical readiness gap between AI ambition and reality, we talk with Andy Bell, Senior Vice President, Precisely  about how – without data integrity – AI implementation is quickly becoming an expensive experiment.

“A key topic of concern at Gartner’s Data & Analytics Summit was how organizations can ensure their investments in AI initiatives are worthwhile. While we are seeing many leaders rush to deploy and reap the benefits of AI, many are failing to achieve a positive return on investment (ROI). However, these diminishing returns on investment result from the data inputs rather than the technology itself.”

“While AI experimentation is positive in general, successful AI outputs rely entirely on the trustworthiness of the data that underpins them.  So, when organizations neglect data integrity, they cannot truly trust their data inputs and risk consulting or automating inaccurate data.  These inaccuracies can have serious real-world consequences across industries, leading to reputational damage, financial losses, non-compliance and poor ROI.”
 
“In fact, a recent survey found that 2 in 3 (67%) data and analytics professionals do not fully trust the data their organization is using.  Even with this lack of confidence, 1 in 3 (32%) expected to see a positive ROI from AI initiatives within the next 6-11 months.  There is a clear disconnect between confidence and reality, which must be addressed.”
 
“So how do we solve this problem?  By revisiting data strategies and prioritising data integrity to create AI-ready data.  When organizations can fully trust their data, AI implementation progresses from an expensive experiment with unpredictable return to a valuable, strategic business advantage.”