By John Roese, Global CTO and Chief AI Officer, Dell Technologies
In 2023, we witnessed the Big Bang of technology – a year where AI ignited a new era of innovation and transformation. In 2025, GenAI went mainstream and agentic entered the scene. And most importantly, we saw real ROI start to emerge in major enterprises like Dell Technologies.
In 2026, the AI story picks up speed. In less than three years, the theoretical has become tangible, and the future is arriving at light speed.
This year we’ll see AI reengineer the entire fabric of enterprise and industry. It will drive new ways of operating, building and innovating at a scale and pace unimaginable just a year ago.
Understanding these changes is essential, because those who invest in resilient, adaptable foundations today will be ready to navigate and lead through the rapid changes ahead.
1. A call to action: Governance frameworks for a fast-moving ecosystem
With the acceleration of AI development comes a degree of volatility. While we predict governance frameworks will eventually stabilise the ecosystem, the current reality is a call to action.
Right now, governance is the long pole in the tent—it’s a critical problem that is not making good progress. The industry has rushed to put valuable AI tools like chatbots and agents into production, but we’ve done so without sufficient governance.
This is not just risky; it’s unsustainable. By 2026, the need for robust frameworks and private environments to ensure stability and control will be undeniable. Running models locally—on premises or in controlled AI factories—will become the norm.
But this is more than a prediction. It’s an urgent appeal. We must focus more on real governance. Without it, we will end up with uncertainty that will slow the adoption of practical and valuable enterprise AI.
Our specific ask of both the public and private sector is to develop governance for the enterprise market in collaboration with the actual enterprise market ecosystem (real enterprises and real enterprise technology suppliers).
We cannot assume that governing public AI chatbots or AGI is the same as helping enterprises shape the real-world application of AI to their companies and processes.
Governance is not about slowing down innovation; it’s about building the guardrails that allow us all to accelerate safely and sustainably.
2. Data management: The true backbone of AI innovation
The next great leap in AI won’t come from more powerful algorithms. It will come from how we manage, enrich and utilise our data. As AI systems become more complex, the quality and accessibility of the data they consume are paramount.
In 2026, AI data management and storage will emerge as the undisputed backbone of all AI innovation.
AI infrastructure is different than classic IT systems. It is focused on accelerated compute, advanced AI aligned networking, new user interfaces and, importantly, a new “knowledge layer” of data to fuel the AI outcomes.
Purpose-built AI data platforms will become essential, designed to integrate disparate data sources, protect new data artifacts 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.
3. Agentic AI: The new operations continuity manager
And what comes next is agentic AI. Agentic is evolving AI from a helpful assistant to an integral manager of long-running, complex processes.
In fields like manufacturing and logistics, AI agents will not just assist workers; they will help coordinate them. Using rich, dynamic data streams, these agents will ensure continuity across shifts, optimise workflows in real time and create new levels of operational efficiency.
Imagine an AI agent scaling the capabilities of process managers on a factory floor, adjusting production schedules based on supply chain disruptions or guiding a new employee through a complex task. By positioning AI agents as intermediaries between a team’s goals and its workers, we can elevate team coordination across all sectors to levels never seen before.
These intelligent agents will become the nervous system of modern operations, ensuring resilience and progress. And like every other AI capability, they are powered by enterprise data creating unique knowledge and intelligence assets that will need to be appropriately stored and protected.
4. AI factories redefine resiliency and disaster recovery
As AI becomes embedded in core business functions, continuity becomes non-negotiable.
AI infrastructure will evolve to prioritise resiliency, redefining what disaster recovery means in an AI-driven world. The focus shifts from simply backing up systems to ensuring AI capabilities remain operational, even if primary systems go offline.
In the coming year, the AI factories of the world must become resilient and survivable in a hybrid, multi-cloud, AI environment. Achieving this will require innovation not only from data protection and cyber resiliency companies but also from the core AI technology ecosystem, governments and at-scale AI innovators. To truly make AI resilient for the enterprise, the entire value chain of AI must work together.
5. Sovereign AI accelerates national enterprise infrastructure
As AI becomes critical to national interests, we’re seeing the rapid rise of sovereign AI ecosystems. Nations are no longer just consumers of AI technology; they are actively building their own frameworks to drive local innovation and maintain digital autonomy.
This shift is reshaping AI infrastructure planning, with AI compute, data storage and management playing pivotal roles in safeguarding and localising sensitive information.
Enterprises will increasingly adapt to these sovereign frameworks, scaling their operations within regional boundaries. This creates a powerful new wave of localised innovation that has a real-world impact on citizens and economies.
By keeping data within national borders, governments can shape their public services and enterprises can tap into new indigenous infrastructure while aligning business objectives to national industrial policies. This is a foundational change that moves AI from a global concept to a powerful local reality.
Charting Your Course for 2026
As we look toward 2026, it’s clear the next wave of AI is about more than breakthroughs in innovation. It’s about building a resilient, intelligent backbone capable of supporting real progress.
The difference between success and stagnation will come down to how well we understand and construct this underlying infrastructure and drive core AI innovation. Those who prioritise their data backbone and invest in adaptable, high-performance systems won’t just keep up – they’ll move ahead.
The tools and insights will be here, but leadership and action will define who reaps the real rewards. The future isn’t just approaching – it’s hurtling toward us at light speed. The question is, are you ready to meet it?
