Dell’s Five Tech Predictions for 2026: AI at scale, agents, sovereign clouds and the future enterprise

Dell Technologies’ annual APAC media briefing today offered a lens on where enterprise Artificial Intelligence (AI) is heading and gave a peek into how enterprise IT decision makers across APAC will approach technology in 2026. As 2025 closes, Peter Marrs, President, Asia Pacific - Japan & Greater China, Dell Technologies, set the tone and context for this year’s session with a clear message. AI momentum is accelerating, and APJC is at the centre of it.

Before we delve into the tech predictions for 2026, it is worth noting how Dell performed through the year. It has been a spectacular year for Dell globally, with a record Q3 and revenue up 11 percent, propelled by unprecedented AI server demand. The company recorded USD 30 billion in AI orders this year and holds an AI backlog of USD 18.4 billion. It expects to ship roughly USD 25 billion in AI servers in FY26, which represents about 150 percent year on year growth. More than 3,000 global customers now deploy Dell infrastructure for AI across devices, data centres, edge implementations and cloud.

Marrs highlighted major APJC deployments. SanDisk is using scalable, high performance infrastructure. Zoho in India is delivering AI at global scale with a privacy first approach. GMO Internet has launched Japan’s first GPU cloud. He also pointed to Dell Innovation Hubs, regional hackathons and active government partnerships that are raising AI fluency across the region.

Amid this momentum, John Roese, Global Chief Technology Officer and Chief AI Officer, Dell Technologies, outlined the five predictions that will define enterprise AI in 2026.

1. Why governance becomes the biggest AI accelerator

Roese’s first prediction is direct. In 2026, governance, not models or compute, will determine whether enterprises achieve AI return on investment.

Externally, regulatory fragmentation is becoming unsustainable. Dell alone navigates more than 1,000 government entities with differing AI rules. Asia has taken a more cautious and rational approach than some regions, but enterprises still need coherent and predictable frameworks that allow them to innovate without ambiguity.

Internally, governance is emerging as the strongest predictor of success. Roese noted a direct correlation between disciplined governance, clear rules, prioritisation frameworks and the elimination of distractions, all pointing to measurable business impact. In many organisations, governance will matter more than algorithms in determining AI’s value. This is a major takeaway as the AI wave sweeps across enterprise IT.

2. Why enterprise data needs a new knowledge layer

Roese’s second prediction reframes a widespread misconception. AI does not use traditional enterprise systems of record directly. Instead, AI workloads demand a dedicated knowledge layer, a new architectural tier that converts ERP, CRM and code repository data into vector embeddings, graphs and other mathematical formats.

This layer is always active, highly transactional and requires new approaches to storage, data protection, performance and accessibility. Many early AI deployments treated this casually, bolting on vector search or assuming application vendors would auto convert data.

This is about to change. In 2026, enterprises will realise that knowledge layer design is fundamental to AI performance. This includes cleaning data, unifying access, adopting protocols such as Model Context Protocol (MCP) and creating new pipelines for agents and AI systems.

The bottleneck will shift from compute scarcity to knowledge layer readiness.

3. How autonomous agents will reshape work itself

The third prediction centres on agentic AI and clarifying what it really means. For instance, most agents today are enhanced chatbots. True autonomous agents, Roese explained, have four components.

• An LLM for reasoning and communication
• A knowledge graph for proprietary intelligence and memory
• Tool use capability via MCP to act on digital or physical systems
• Agent to agent communication for collaboration

Once deployed, agents do far more than speed up tasks. In Dell’s factories, they coordinate work across humans and machines, increasing throughput and reducing operational drag. Agents can capture the expertise of top engineers and scale it across teams. They can take on tasks such as CRM clean ups that humans avoided because the cost was too high.

Roese predicts that in 2026, agents will begin reshaping organisational behaviour, not just task flows. Productivity gains will come from how agents and humans work together, not merely from faster workflows.

4. Why AI factories need a radical new approach to resiliency

Resiliency is the fourth major prediction and possibly the most misunderstood. Traditional IT resiliency models depend on replication, where a secondary data centre mirrors workloads. AI factories break this model.

Duplicating thousands of GPUs is impractical and economically irrational. Instead, Roese expects enterprises to adopt hybrid resiliency that uses sovereign clouds, hyperscalers, edge environments, automation and AI driven orchestration.

Resiliency will shift from replicating everything to protecting the interfaces that matter. For Dell’s sales teams, for example, the only system that must remain online is Dell Sales Chat, the AI interface. The underlying systems feeding it can temporarily fail without affecting frontline operations.

Dell’s experience in cyber recovery and cyber vaults positions it strongly as enterprises re engineer resiliency for the AI era.

5. How sovereign AI clouds will expand into new national infrastructure

Sovereign AI was one of 2025’s biggest narratives. Roese’s fifth prediction underscores that sovereign infrastructure will play a far larger role than initially assumed.

Countries are approaching sovereign AI in three ways.
• Building AI for government use
• Enabling AI for industry
• Convening ecosystems through government and industry collaboration

Once sovereign infrastructure becomes operational, new use cases emerge rapidly, such as:
• Robotics back ends for defence, transportation and healthcare
• Agent certification for regulated professions
• Disaster recovery for critical national infrastructure
• Fine tuning smaller models for local enterprises
• Cross border collaboration zones where agents from multiple nations work together safely

Sovereign AI clouds will evolve from compliance driven investments to foundational national utilities that are critical to industry competitiveness, scientific research and digital cooperation.

Why quantum progress still matters to AI leaders

Roese closed with a reminder that quantum computing is not a 2026 disruptor, but its progress is unmistakable. New error corrected qubit systems, growing qubit capacity and smarter software that can reduce algorithmic steps by nearly 98 percent are pushing quantum steadily towards practical reality.

Much of this innovation is happening across Asia and the rest of the world. As quantum becomes viable for mainstream algorithms, AI efficiency will multiply dramatically. Enterprises that monitor this intersection early will be better positioned for the next wave.