Embarking on a Data-First AI Odyssey

Greetings, friends and colleagues. Brad Shimmin here from The Futurum Group. I’d like to share some thoughts on how companies increasingly view their data estate through the lens of AI. The market is absolutely saturated with talk of AI’s transformative potential. Businesses are no longer just dabbling in AI; they’re placing substantial bets on its potential to redefine their future.

During a recent session at IntelliVerse 2025, I shared insights from our Decision Maker Survey, a comprehensive study engaging over 800 senior IT and business leaders. What we discovered was enlightening: Yes, AI’s allure is universal. But at the same time, there’s a chasm that exists between aspiration and implementation. According to this study, building AI apps stands as the most important objective for data teams in the enterprise. And yet, keeping pace with rapid data growth remains the biggest challenge for these practitioners.

The truth is that many enterprise data estates, as they exist today, are not a great fit for the AI era. They are brittle, siloed, and insecure, with data professionals struggling to keep up with data quality crises, architectural bottlenecks, and glaring security vulnerabilities.

For years, the prevailing wisdom was to centralize everything. The playbook was to haul data from its source, dump it into a monolithic lake or warehouse, and let analysts sort it out. That model is now obsolete. It was designed for predictable, batch-oriented analytics, not the real-time, distributed data streams that fuel modern AI. This old approach creates massive latency, data duplication, and an ever-expanding security attack surface. Continuing down this path is not just inefficient; it’s strategically unsound.

Thankfully, there’s hope and a clear path forward. Successful organizations don’t just work harder within old paradigms, they pivot strategically, setting data as the foundation of their AI ambitions. This shift is about more than standing up the latest shiny AI tool; it’s about building a sustainable data estate powered by open architecture and measurable value.

For enterprises ready to make this transition, we suggest a dual-investment strategy. As our data reveals, leaders understand the importance of paralleling AI innovation with foundational data investments. This balanced approach ensures that both the engine and the fuel are in top form.

Furthermore, navigating the data bottleneck calls for adopting a unified hybrid cloud architecture, allowing data to remain in its native environment while being securely accessible and operational across a global file system. This decentralized approach not only enhances efficiency but also fortifies security by ensuring data sovereignty.

Perhaps the most radical but necessary shift is turning the traditional data-for-AI model on its head: instead of rushing to bring data into centralized systems, bring compute power to where the data resides. This modern playbook empowers organizations to harness AI’s full potential without compromising security or control.

From an architectural standpoint, this means moving toward a unified hybrid cloud fabric. I’m not talking about a simple “lift and shift” migration. I’m referring to a global file system that presents a single, coherent view of your data estate, regardless of whether it lives on-premises, in AWS, Azure, or Google Cloud. This enables AI workloads to run securely against data in its native environment, which is essential for maintaining data sovereignty and minimizing risk.

Why pursue these strategies? It’s about more than data enhancement. It’s about accelerating operational efficiency, elevating human capital, and enabling true data democratization. Modern platforms can automate tedious tasks, freeing talented individuals to drive innovation. Our research notably highlights this trend, with 73% of roles shifting toward strategic, business-facing functions—an evolution catalyzed by AI.

Looking forward, I urge you to start with an honest, perhaps even ruthless audit of your data quality, architectural silos, and security weak spots. Balance investment in novel AI tools with foundational data infrastructure. Choose platforms capable of managing a distributed data estate, ensuring AI compute can securely interact with data in situ. The future of enterprise AI is not about a single, all-powerful model in a centralized cloud. It’s about a federation of capabilities that can securely interact with data wherever it resides. Such strategies are key to future-proofing your enterprise and preserving control while accelerating tangible value.

As we forge ahead in this AI-powered landscape, remember this isn’t just about technological evolution, it’s about embracing a new way of seeing, doing, and innovating.

By crafting strategies around these core insights, we can harness AI’s potential to redefine what’s possible across industries. The winners will be those who recognize that in the 21st century, intelligent architecture is the ultimate competitive advantage. Here’s to innovation and transformative journeys ahead!