Preparing for the Age of AI: The Fundamentals of an AI-Ready Data Infrastructure

Read our VP of Technology Innovation & Operations, Srihari Udugani’s key insights from the Hitachi Vantara State of Data Infrastructure Global Report 2024

An AI-powered drone delivering groceries, a new crop of generative AI models creating realistic music compositions, or a virtual assistant solving customer queries in real time—there’s something extraordinary happening with AI-driven innovations almost every day.

But beneath the surfeit of AI advancements lies a challenge growing faster than anyone expected: a scalable and reliable data infrastructure. How do we keep up with the tsunami of data these technologies generate every second?

The Hitachi Vantara State of Data Infrastructure Global Report 2024 dives deep into this pressing question, revealing insights every IT leader needs to hear. At Borderless Access, we are, as always, eager to unpack these findings and explore how AI is transforming not just our expectations, but the very foundation of how we manage data.

The Data Explosion: From Doubling to Tripling 

Think about this for a moment: last year, IT leaders anticipated their data storage needs to double in two years. Today, that projection already feels like an understatement. The average large organization now holds 150 petabytes of data—and by the end of 2026, that number will exceed 300 petabytes. As mentioned in the report, that is enough data to store every film ever made worldwide since 1950 nearly 200 times over in 4K resolution.

What’s fueling this surge? AI—and more specifically, the incredible leaps in generative AI (GenAI). From CEOs to customers, GenAI is sparking excitement and delivering real returns. A Google Cloud study found that 86% of AI early adopters gained an average of 6% in revenue. This indicates the more we lean into AI’s potential, the more strain we put on critical assets like data storage and processing power.

AI Maturity: The New Competitive Benchmark 

Every company, according to Hitachi Vantara’s study, has adopted AI to some degree. Yes, every single one. But the maturity levels vary greatly. While 76% of organizations have moved beyond limited adoption, only 37% say AI is already critical to their business.

Geographically, the picture gets even more interesting. Adoption rates are highest in places like Singapore (57%) and China (53%), while the U.S. (32%) and U.K. (27%) lag behind. Still, one thing is clear: whether piloting use cases or integrating AI as a core business function, companies of all sizes are racing to stay ahead of AI innovations.

To make AI a core strategic advantage, companies must lay the groundwork now with a strong data collection strategy. The complexity and cost of infrastructure will keep rising, especially in cloud environments. This demands proactive decisions around security, GDPR compliance and data privacy. Yet, few organizations are truly prepared with a comprehensive data strategy.

The Big Bottlenecks: Data Quality, Skills, and Sustainability 

The rewards of AI are undeniable, but so are the challenges. From my conversations with IT leaders across industries, it’s evident that businesses are hitting some critical roadblocks on their AI journey.

1. Data Quality 

While AI is only as good as the data you feed it, ensuring that data is accurate, relevant, and complete is a constant struggle. It’s not just about collecting more data—it’s about collecting better data. Without quality input, even the most advanced AI models are bound to underperform.

2. Talent Shortage

The talent gap is real, and it’s widening. More than coders or data scientists, we need specialists who can build, train, and fine-tune AI systems at scale. With demand far outstripping supply, businesses are competing for the same shrinking pool of skilled workers.

3. Data Storage

IT leaders report their storage needs have tripled in just two years, and budgets are straining under the weight. This makes it harder to add data storage capacity without compromising performance.

4. Processing Power

AI’s appetite for computational resources is relentless. Traditional infrastructure wasn’t designed to handle this kind of load, and organizations are scrambling to upgrade scaling processing power without disrupting operations.

5. Sustainability

This is a challenge not talked about enough. As AI scales, so does its environmental footprint. Energy-efficient data centers and sustainable practices have become imperative. The question isn’t whether we can scale AI; it is about if we can do it responsibly.

Turning Risks into Rewards 

The current pace of AI-driven transformation offers umpteen opportunities—but only for those willing to act decisively. Just like any equation, the output depends on the inputs. It is with the right groundwork that organizations can turn AI into a core strategic advantage—not just for tomorrow, for decades to come.

So, it is time to ask yourself: are you building a launchpad capable of supporting your AI ambitions? If not, there’s no better time to start.