Survey of senior leaders at 500 large organizations worldwide highlights AI-readiness as critical to organizational ambitions in the next 12 months – with ‘responsibly-sourced’ data key to delivering this goal.
New research from Iron Mountain, in partnership with FT Longitude, finds that nine out of 10 organizations globally have seen revenues and profitability grow over the last 12 months as a direct result of investing in their information management systems and strategies – a total revenue gain globally of $72 trillion or the equivalent of $1.9 billion in average revenue growth per organization.
The survey of senior leaders at 500 large organizations worldwide highlights AI-readiness as critical to organizational ambitions in the next 12 months, with ‘responsibly sourced’ data key to delivering this goal.
However, nearly two-thirds (64%) report that their organization’s AI readiness is not as effective as it should be.
Organizations that are not fully investing in trustworthy AI-ready data have lost an average of $389,000 over the past 12 months due to data integrity flaws, equating to a $14 billion loss globally.
Managing data responsibly and effectively for the AI age can give organizations a strong competitive advantage, but many are failing to harness this successfully, despite AI-readiness being a key strategic priority.
The research examined how 500 large organizations are leveraging their information and datasets to be ‘AI-ready’ and identifies a group of leaders with a blueprint for building AI-ready information management capabilities.
The good data dividend
Nearly 90% of respondents reported revenue and profitability growth over the past 12 months as a direct result of their organization’s information management practices, including data collection, storage and analytics capabilities.
This ‘good data dividend’ equated to a total global revenue gain of $72 trillion – or average revenue growth of $1.9 billion per organization.
Data integrity is critical to being AI-ready
Iron Mountain’s research also found that, on average, an organization had lost $389,000 over the past 12 months because of data integrity flaws, equating to a $14 billion loss globally.
Over a third of respondents (34%) identified AI-ready data as the information management focus area that will have the greatest impact on achieving their organization’s strategic ambitions over the next 12 months.
However, most organizations are battling capability gaps and only 35% said their information management strategies in AI-readiness are consistently generating value.
Nearly two-thirds (64%) reported that their AI readiness is not as effective as they would like, and 60% are concerned about their current levels of data integrity and their ability to source information.
Additionally, 70% said they can’t integrate data sources quickly enough for real-time analytics to support their decision-making and 76% said the cost of reworking systems not fit for current needs had blocked some of their AI initiatives in the past 12 months.
Narasimha Goli, Chief Technology Officer, Iron Mountain, said: “With the rise of open-source and specialized AI models, data integrity, transparency and trust are more critical than ever. At Iron Mountain, we are investing in solutions such as our Iron Mountain InSight Digital Experience Platform (DXP) to help our customers get their information ready for use in generative AI and other AI-powered applications. This enables organizations to illuminate dark, unstructured data by automating the processes for extracting and organizing meta data at speed and scale, and with a high degree of accuracy.
“By leveraging technology like this to ensure their data is being sourced responsibly, organizations can harness the full potential of their information to drive intelligent decision-making and unlock new growth opportunities.”
Lessons from the leaders: an AI-ready data blueprint
Iron Mountain’s research showed that over the last 12 months, the organizations surveyed achieved an average of $1.9 billion in revenue growth as a direct result of enhancements from new information management systems and strategies.
A small group of leaders who are experiencing the most revenue and profitability increases have more data integrity and accuracy provisions in their workflows to ensure the data used in AI outputs are sourced responsibly:
- 100% have processes for eliminating redundant, obsolete or trivial (ROT) data and for automating data extraction.
- 96% are using AI dashboards to explain outcomes and data lineage to non-technical stakeholders.
- These leading organizations are 16% more likely to have adopted AI nutrition labels to verify data quality, and 55% of them are looking to AI technologies themselves to improve their unstructured data sources so they are more AI-ready.
Mithu Bhargava, Executive Vice President and General Manager, Digital Solutions, Iron Mountain, said:“Smart information management is key to capitalizing on the growing AI opportunity and Iron Mountain’s research shows that a commitment to responsibly sourcing the data for AI models is a hallmark of leading organizations. With AI fast becoming a necessity, this data quality-first mindset is now essential for every organization.”
Other key findings from the research:
- Cybersecurity threats and compliance risks (34%); data quality and accuracy (31%); and talent shortages (28%) are seen as some of the biggest barriers by organizations to achieving AI readiness.
- Slower agility (34%) increased operational costs (33%) and poor customer experience (31%) are cited as the most common issues relating to data integrity problems.
