The next major advance in AI in 2026 will be driven not by more powerful algorithms, but by how data is managed, enriched and used, according to Dell Technologies.
John Roese, global CTO and chief AI officer at Dell, said data management and storage will become the backbone of AI innovation as systems grow more complex.
Roese was commenting on the contents of a Dell report titled From Big Bang to Light Speed: The AI Revolution Continues.
“As AI systems become more complex, the quality and accessibility of the data they consume are paramount,” Roese said, adding that purpose-built AI data platforms will be essential to integrate disparate data sources, protect new data assets and deliver high-performance storage.
Roese said data will increasingly be used during inference, not just training, enabling real-time intelligence.
He added that purpose-built AI data platforms will become essential, designed to integrate disparate data sources, protect new data artefacts and provide the high-performance storage needed to support them.
“The ability to efficiently feed clean, organised and relevant data into AI models is critical. But as we enter the agentic age, this data will no longer be used solely to train large models. Instead, it will be a dynamic asset during inference, enabling the generation of real-time, evolving knowledge and intelligence. This underlying data layer is the launchpad for everything that comes next.”
Dell expects agentic AI to evolve from a support tool into a manager of long-running, complex processes. In sectors such as manufacturing and logistics, AI agents will help co-ordinate workers, optimise workflows and ensure continuity across shifts.
“These agents will become the nervous system of modern operations,” Roese said, driven by enterprise data that must be securely stored and protected.
Roese also pointed to the rapid rise of sovereign AI, as governments seek greater digital autonomy. He said countries are building national AI ecosystems through government-led initiatives, partnerships with industry or a combination of both.
Enterprises are expected to adapt to these frameworks by scaling operations within regional boundaries, enabling localised innovation while aligning with national industrial policies.
“This is a foundational change that moves AI from a global concept to a powerful local reality,” said Roese.
Data and AI bubble
Good data is inextricably linked to beneficial outcomes of AI, according to experts.
In a recent blog, Johan Steyn, AI expert and founder of AIforBusiness.net, spoke on the issue of an ‘AI bubble’ – the notion that the emerging technology is a replay of the dot-com crash of the early 2000s.
He stated: “In the decade I have spent working in the AI field, I have not once seen a project fail because the technology was not good enough. Every failure I have witnessed came down to people: misaligned expectations, poor leadership, weak planning, bad data or a refusal to change how work is actually done. That is why I worry less about a financial AI bubble and more about the stories we are telling ourselves. When executives or politicians talk about AI as if it were magic, they are inflating expectations that no algorithm can ever meet.
“The truth is that AI amplifies whatever it is plugged into. Insert it into a well-run system with clear goals, good data and capable people, and it can deliver impressive gains.”
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