The AI revolution is delivering real, measurable results for businesses worldwide. But behind the headline wins lies a critical infrastructure crisis that too few IT leaders are adequately prepared for. The data coming out of 2026 is sobering: the majority of enterprises are generating genuine returns from AI investment, yet most lack the storage backbone to sustain that momentum. If your organisation falls into that 62% gap, this is the wake-up call you need.

AI ROI Is No Longer a Hypothesis — It's a Business Reality

For years, sceptics questioned whether AI investment would ever produce tangible returns. That debate is effectively over.

Drawing on independent research conducted by Recon Analytics among 2,712 enterprise technology decision-makers across seven global markets, nearly nine in ten organisations (86%) report moderate or significant ROI from AI investments, including one-third (33%) reporting significant measurable ROI.

Among respondents, 86% said their organisations had already seen moderate or significant return on investment from AI spending. Within that group, one-third reported significant measurable returns, suggesting that many businesses now view AI as a source of commercial value rather than an experimental tool.

The shift is also happening at the executive level.

Productivity gains, the default justification for GenAI investments throughout 2024 and 2025, fell from 23.8% to 18.0% as the #1 ROI metric — and in its place, CFOs are demanding hard P&L accountability.

AI has grown up. And with maturity comes a much harder question: does your infrastructure have what it takes to keep up?

The Storage Crisis Hidden Behind AI Success

Here's the paradox no one is talking about loudly enough.

Nearly all organisations (99%) expect AI to increase storage requirements over the next three years, yet only 38% of organisations say they are fully prepared for AI's long-term data demands.

That means a staggering 62% of businesses are racing towards an infrastructure cliff edge.

Nearly one-third (32%) of organisations expect their storage needs to increase by more than half

— and that's in just three years. The volume problem is compounded by a value problem.

As AI becomes more embedded in business operations, organisations are facing a dual shift: the volume of data they need to store and manage is rising, while the potential value of that data is also increasing. The findings suggest data is becoming more than an input; it is a long-term asset that organisations need to preserve, manage and use effectively to create business value over time.

Ninety-eight percent agree that AI is transforming data storage from a back-office function into strategic business infrastructure — a finding that signals a change in priority: storage decisions now directly influence how quickly organisations can put data to work and how well they can preserve its value.

What's Actually Blocking AI Deployment?

It's tempting to assume that GPU shortages or compute capacity are the primary bottlenecks to scaling AI. The data tells a different story.

While much of the AI infrastructure conversation has centred on computing power, businesses are increasingly confronting challenges around how data is stored, accessed, governed and retained.

More than half of respondents (53%) identified data quality and readiness as a leading obstacle to AI deployment, while 43% cited storage infrastructure challenges — concerns that ranked ahead of compute availability, named by 27% of respondents, and energy constraints, cited by 24%.

The results indicate that for many organisations, the bottleneck in scaling AI lies less in access to processors and more in how data is stored, governed and prepared for use.

This reframes the entire conversation. Winning the AI race isn't just about buying more GPUs — it's about building a data infrastructure that can feed them reliably.

Adding further pressure,

organisations that need more storage capacity can't just throw dollars at the problem — they face soaring equipment prices and supply chain disruptions that require new approaches to expanding capacity.

The Energy and Sustainability Dimension

Storage infrastructure doesn't just have a capacity problem — it has an energy problem.

Globally, 77% of respondents said sustainability or energy concerns had delayed or changed an infrastructure expansion.

That's an extraordinary statistic. Sustainability is no longer a CSR footnote; it's actively shaping IT investment decisions.

Power demand in data centres continues to rise as organisations make room for power-hungry AI workloads, with US data centres on track to require 22% more grid power by the end of 2025 than a year earlier.

For IT leaders, this means the cost of running AI storage at scale is climbing in two directions simultaneously: more data to store, and more energy to store it.

Organisations that fail to model energy consumption realistically risk underestimating the true cost of AI initiatives, which can distort financial forecasts and strategic planning.

Why Legacy Storage Architecture Can't Keep Up

Recent industry analysis highlights a sobering reality: 84% of enterprises report their data storage systems are not fully optimised for AI workloads.

The reason is structural. Most enterprise storage was designed for predictable, application-specific workloads — not the continuous, distributed, and data-hungry nature of modern AI.

AI workloads are inherently distributed. Training, inference, and data preparation increasingly occur in different environments, driven by cost, performance, and sovereignty requirements.

Legacy systems simply weren't built for this.

When storage cannot saturate a high-performance GPU cluster, the financial impact is immediate: approximately $30,000 per node in annual capital and power costs is wasted on idle cycles.

One of the clearest messages from recent studies is that many organisations no longer treat archived data as dormant. Instead, they are recalling historical datasets to support newer AI tasks, changing how they think about the boundary between active and inactive information — a trend that could have practical consequences for spending priorities across corporate IT.

Practical Tips: How IT Leaders Can Close the Storage Gap Today

The good news? This is a solvable problem — if you act with intention. Here are the most actionable steps IT leaders can take right now:

1. Conduct an AI storage readiness assessment immediately.

IT leaders need to think beyond individual infrastructure purchases and focus on longer-term data centre transformation and AI requirements. In many cases, the storage and memory shortage is serving as a catalyst for broader infrastructure modernisation, encouraging organisations to assess whether their current environments can support the performance, scale, and resiliency demands that AI initiatives will require.

2. Implement a tiered storage architecture.
Not all data deserves the same treatment.

Data tiering seeks the best possible end-user experience by moving hot data to a high-speed storage tier, while cooler, less frequently used data is moved off to the cheaper but slower high-capacity tier.

One real-world example:

migrating to a hybrid, tiered storage architecture that differentiates between hot and cold data cut one company's storage costs by 60% with millisecond response time on its AI engine.

3. Align storage decisions with long-term AI data strategy.

A short-term approach focused only on immediate capacity can leave information fragmented, inaccessible or expensive to reuse. A longer-ranging view can connect retention, access and governance decisions to the future value the organisation expects AI to create.

4. Invest in data quality alongside storage capacity.
Since 53% of organisations cite data quality as their top AI blocker, storage investment must be paired with data governance.

Long-term success depends on deep workload visibility, high-quality data, and strong security protocols — and leaders who prioritise these capabilities may be better positioned to avoid costly missteps and unlock the full potential of AI.

5. Factor energy costs into your storage ROI model.

Enterprises are looking to generate power savings by reducing the use of energy in their enterprise data infrastructure, and this requires the adoption of more power-efficient storage systems and related technologies.

6. Embrace AI-driven storage management.

In 2026, AI integration into storage appliances is becoming a standard, with administrators increasingly using AI for tasks such as tiering, migration, optimisation, provisioning and even backups and failure predictions.

Let AI optimise the very infrastructure that powers it.

The Bottom Line

AI ROI is real, proven, and accelerating. But the infrastructure gap is equally real — and it's widening.

The findings point to a widening gap between the pace of enterprise AI adoption and the data infrastructure needed to support it.

Businesses that treat storage as an afterthought will find themselves unable to scale the very AI programmes delivering those returns.

Primary storage is entering a pivotal phase where performance, flexibility and intelligence are no longer optional — they are foundational.

The organisations that win the next phase of the AI era won't just be those with the best models. They'll be the ones whose data infrastructure can feed, sustain, and scale those models without breaking. The 62% gap isn't a statistic to observe — it's a strategic problem to solve.

Is your storage infrastructure truly AI-ready? If you're not certain, now is the time to find out. Conduct a full AI infrastructure readiness review, benchmark your storage architecture against the demands of your current and projected AI workloads, and build a roadmap that treats data storage as the strategic asset it has become. The businesses acting on this today will be the ones leading tomorrow.