Posted by Mumbai: In the era of artificial intelligence (AI), where enterprises and organisations are trying to become AI ready, a new report finds that AI projects are getting delayed or derailed due to data concerns of organisations.
86% of organisations have experienced AI project delays or cancelled due to data concerns in the past 12 months, reveals new research from Iron Mountain, a global information management and storage company.
The study point to a rising gap between AI ambitions and readiness as organisations and enterprises are aiming to move towards autonomous AI.
Delays or Cancellations of AI Projects
The report titled, The Information Supercycle: Aligning Data, Infrastructure and Capital for AI Scale, in its findings show, 95% of organisations are using data to improve business outcomes, which is a top priority for them. However, 86% of organisations have suffered delays or cancellations of AI projects because of data concerns in the past 12 months.
Certainly, there’s a direct co-relation between the status of AI projects and data concerns, which mainly dependents on how organisations are managing the data for AI. The scale of work still required to make information usable for AI remains significant, as per the report.
Data Preparation
Four in 10 organisations spend at least 41% of their AI project timelines on manual data preparation, as per the report findings. And only 39% fall into ‘digitised and structured’ category, where dark data is centrally managed, indexed and readily usable for analytics and AI.
Fragmented and Legacy Information
Besides, how organisations managed their data, they struggle with old and incomplete information for AI projects. The report point that fragmented and legacy information remains a major barrier. More than half (56%) of organisations say information spread across disconnected systems or departmental silos, while 53% point to legacy formats or physical archives, making AI projects challenging.
Those gaps become more consequential as businesses move beyond generative AI towards agentic systems capable of acting across workflows. As per report findings, 70% of organisations say they cannot effectively track the lifecycle (origin or journey) of the data used in their AI models.
Beyond delayed or derailed AI projects and models and business outcomes, fragment information or data can expose organisations to security breaches, cyber incidents, compliance and audit risk, higher discovery costs and loss of institutional knowledge.
“AI readiness” according to Varun Gupta, Iron Mountain‘s SVP – Global Product and Solutions Marketing, is about much more than deploying the latest technology. “Enterprises need to know that the information powering their AI is accessible, current and governed, with clear ownership and permissions,” said Gupta.
“As AI agents move from generating answers to taking actions across business workflows, these foundations become essential to scaling AI with confidence,” added Gupta.