How can organizations help ensure they're optimizing ROI from their AI investments?

Organizations are increasingly investing in artificial intelligence (AI), with global spending projected to reach $2.59 trillion by 2026, a 47% year-on-year increase. However, only 28% of AI projects currently generate measurable ROI, indicating many fail to deliver expected business outcomes. As organizations rush to capitalize on AI, many invest without fully understanding how AI will integrate with existing technology, particularly data storage infrastructure.

Unplanned investments can result in higher costs, technical debt, and compliance complexity, potentially undermining AI's value. At its core, AI is a data system, and the industry often emphasizes performance, computing power, and processing speed. Yet, AI data is evolving and requires ongoing management throughout its lifecycle.

As businesses move from AI experimentation to scale, a robust data infrastructure becomes crucial. To maximize AI ROI, organizations should view storage and data infrastructure as strategic enablers, not just supporting technologies. Unlike historical data storage, AI requires handling vast amounts of data, creating additional data, and the quality of underlying data significantly impacts AI performance.

A tiered storage architecture can optimize infrastructure spending while maintaining access to valuable datasets. Compliance with legal and regulatory considerations, such as GDPR and Data (Use and Access) Act 2025, is also crucial. Organizations should plan for long-term storage needs early to avoid remediation costs. As AI adoption grows and data volumes expand, organizations must proactively forecast storage requirements, explore longer-term capacity planning, and incorporate storage needs into AI business cases.

Investing in cost-effective, scalable, future-ready AI workload optimized storage and data infrastructure is essential for unlocking AI value and supporting future growth.