AI and compute spending is about to skyrocket
AI has brought with it a fundamental shift in how businesses operate. And like any major shift, it requires serious investment. Over the next two years, companies will more than double their spending on AI-related data storage and processing power—a staggering 224% increase. That’s not a small bump. It’s a clear signal that executives see AI as the next big driver of growth, automation, and competitive edge.
This surge isn’t happening randomly. AI needs a huge amount of data and computing power to function well, especially with large-scale models. Training AI on outdated, limited datasets won’t get far. Companies now recognize that AI is about more than smart algorithms, but rather feeding those algorithms high-quality data at scale and having the compute power to process it in real time.
Most executives are approaching AI as an R&D investment, not an immediate profit driver. That’s smart. Cutting-edge AI systems don’t generate instant revenue—they require patience, iteration, and fine-tuning. But the upside? Massive efficiency gains, smarter decision-making, and entirely new business models.
The AI revolution has a data problem
AI is only as good as the data it’s trained on. If your data is messy, biased, or incomplete, your AI will be too. And right now, most companies are dealing with serious data quality issues. More than one-third of executives surveyed admit they lack confidence in their data quality, and for good reason—IT leaders report that AI outputs are accurate less than half the time. That’s a huge problem.
A big part of the issue? Unstructured data. About 75% of enterprise data is unstructured, meaning it doesn’t fit neatly into databases. Think emails, social media posts, audio files, customer reviews, sensor data—basically everything AI needs to learn from. Without proper data structuring, AI models struggle to extract meaningful insights.
“Despite this, many companies are still operating without strong data governance. Over a quarter of businesses haven’t even implemented basic data quality checks.”
Fixing this means making data a priority, not an afterthought. AI leaders need to invest in better data management strategies, enforce strict quality standards, and ensure that their AI systems are working with clean, unbiased, and comprehensive data. Otherwise, they’re just throwing money at a problem without solving it.
AI ROI is taking longer than expected—but that’s not a bad thing
Most companies investing in AI haven’t seen a return yet. In 2024, less than half of AI projects were profitable. That’s according to IBM’s latest report, which surveyed over 2,400 IT decision-makers. On the surface, that might sound like a red flag. But in reality, it’s just how breakthrough technology works.
Take Tesla. In the early days, electric cars weren’t profitable. R&D costs were high, infrastructure wasn’t ready, and people were skeptical. But with persistence, scale, and technological improvements, EVs became inevitable. AI is following a similar trajectory.
Executives are taking the long view. Over two-thirds of AI-driven companies now treat their AI investments as R&D spending, expecting ROI to materialize within two years. That’s a smart approach. Breakthrough tech doesn’t follow quarterly earnings cycles. It follows a curve of discovery, iteration, and scale.
Companies that stay patient and focused will be the ones that ultimately dominate their industries. AI is about transforming business models entirely. Those waiting for instant ROI will likely be buying from the companies making bold investments today.
AI is fueling an explosion in cloud storage and compute power
AI doesn’t just need data—it needs massive amounts of storage and compute power. We’re talking about an exponential increase in demand. AI models are trained on everything from customer interactions and social media posts to audio files, video footage, and IoT sensor data. The sheer scale of this data is mind-blowing, and companies are quickly realizing their current infrastructure isn’t built for it.
This is why cloud storage is becoming the default choice for AI-driven businesses. On-premise systems just can’t keep up with the scalability and flexibility cloud providers offer. According to Omdia research, the cloud storage market is expected to double by 2028, and AI is a major driver of that growth.
If you’re running large-scale AI models, you need to store, manage, and retrieve petabytes of data efficiently. The old approach (storing data across fragmented, siloed systems) isn’t going to work. Businesses that fail to scale their storage and computing power properly will hit performance bottlenecks, limiting how effectively they can train and deploy AI.
Companies investing in high-performance cloud storage and compute power today are building the foundation for long-term AI dominance.
Trust is the ultimate factor in AI adoption
AI adoption is also a trust challenge. No executive is going to base critical business decisions on AI if they don’t trust the results. If an AI system produces flawed or biased outputs early on, users will abandon it. And once trust is lost, it’s almost impossible to regain.
This is why data quality and transparency are critical. AI models don’t operate in a vacuum—they reflect the data they’re trained on. If the input data is flawed, biased, or incomplete, the AI will make bad decisions at scale. Worse, if users don’t understand why an AI made a particular decision, they won’t trust it.
According to Simon Ninan, SVP of Business Strategy at Hitachi Vantara, trust is the biggest factor in AI adoption. As he puts it:
“The adoption of AI depends very heavily on trust of users in the system and in the output. If your early experiences are tainted, it taints your future capabilities.”
The solution? Transparency and explainability. AI systems need to be designed in a way that allows users to understand how decisions are made. Businesses must also invest in rigorous data validation and governance to make sure AI-generated insights are reliable.
AI investment continues despite IT budget caution
Even as businesses tighten their overall IT budgets, AI spending isn’t slowing down. This tells you everything you need to know about where executives see the future heading.
AI a fundamental shift in how businesses operate. That’s why even companies that are cautious about IT expenses are still greenlighting AI projects. Analysts at ISG found that while executives are being selective with IT budgets, AI remains a top funding priority.
Why? Because AI drives efficiency, automation, and competitive advantage in ways that traditional IT systems simply can’t. It’s focused on unlocking entirely new revenue streams. Companies that hesitate risk being left behind as their competitors scale up AI-powered decision-making, automation, and customer engagement.
This moment is similar to the early days of the internet. Some companies were hesitant to invest, questioning the ROI. Others saw the future and went all in. We know how that played out. The same is happening with AI. The leaders of tomorrow are the ones investing today.
Key takeaways for decision-makers
- AI investment is surging: Businesses are set to more than double their spending on AI-related data storage and compute power by 2026. Leaders should allocate resources now to secure a competitive edge in AI-driven innovation and automation.
- Data quality is critical for AI success: Poor data quality hampers AI performance, with over a third of executives expressing concerns. Companies must prioritize robust data governance frameworks and data structuring to ensure AI models deliver reliable and actionable results.
- Long-term ROI expectations for AI: Despite slow short-term returns, executives are treating AI investments as R&D. Leaders should remain patient, focusing on AI as a long-term growth driver rather than seeking immediate profitability.
- Cloud infrastructure demand will grow: Cloud storage is expected to double by 2028, driven by AI’s growing data needs. Decision-makers should invest in scalable cloud infrastructure now to accommodate the expanding demand for AI data processing.
