Dell Technologies’ “AI Predictions 2026” briefing distilled five strategic points every IT leader has to consider as they plan their AI projects for the new year. Dell Tech’s global CTO and chief AI officer John Roese described them as predictions “… that maybe people aren’t fully paying attention to or are aware of, but are likely going to be significantly impactful around AI and its adoption.”
Here are Dell’s predictions in no particular order and with each point followed by a quote (or two) from the CTO, which EITN included to lend depth and context.
1. Governance becomes the gating factor for AI success
Quote: “This year, I actually feel like the word governance is going to play a much bigger role, and we know this firsthand from the work we’ve done in Dell to activate our company and get to ROI… After spending time with hundreds of CIOs and chief AI officers, the number one complexity of moving fast and moving forward is to establish a governance structure, a set of rules that people understand how they can follow, a way to prioritise what is important, a way to not do a lot of things that will distract you.”
Quote: “A company like Dell has over 1000 governmental entities around the world with independent AI policies telling us how we should do AI. That is not sustainable. And so, we’ve been very clear with most governments around the world that while regulation and governance is important, fragmented and chaotic governance rules are not helpful.
“I think Asia has been more cautious than maybe Europe and other areas. That’s good, but we need to all collectively work with our governments to make sure that we have efficient and rational governance frameworks, so enterprises trying to do real work have clarity about what the rules are.”
The summary? Regulatory clarity and disciplined internal rules will determine which organisations can scale AI safely and deliver measurable ROI. Fragmented governance rules create operational friction; without internal governance, projects proliferate without priority or production outcomes.
2. Tremendous amount of activity around the knowledge layer
Quote: “AI systems do not really use your traditional systems of record… they move it into what many of us call a knowledge layer… a different set of tools that convert that legacy and traditional data into math, whether it be a vector database, a graph database, a knowledge graph.”
Quote: “(The knowledge layer) will require new storage, new data protection, new performance levels, new tools and new processes and professional services to make sure it’s accurate and accessible.”
Beyond GPUs and model hosting, enterprises must build an explicit “knowledge layer” (vector/graph stores, knowledge graphs) that converts systems-of-record into AI-ready math, with specialised storage, performance and data hygiene processes. The knowledge layer is a separate layer that enterprises must design for and optimise before they convert it into a different representation or implement protocols like MCP (model context protocol).
3. Autonomous agents will re‑shape organisational design and productivity
Quote: “What is coming is a technology called an autonomous agent that is much more sophisticated than just a feature in a large language model. It is a software system (that) has four components.”
These four components include an LLM, a capability to specialise and incorporate knowledge like a knowledge graph, the ability to interact with the rest of the world and perceive external data sources, and the ability to talk to other agents.
Quote: “And so when agents are actually incorporated into organisations, a very interesting thing happens. They start to change the way the organisation looks and functions. They are not just tools that exist within an organisation that operates the same way it always has.
“One of the things they’re great at is coordinating people. Using AI, and in this case, agents to act, not just as the tools people use, but to give them the task, to make sure that the team of both people and machines do work in the right way, accomplish a goal collectively, is able to make progress, is able to deal with hand offs… We are doing that in our factories today, and we are seeing significant improvement in human productivity.”
In summary, agents are not just chatbots – they are multi-component software systems with LLMs, specialty knowledge, tool access and inter-agent protocols; when embedded in workflows they change how teams coordinate, capture expertise of talented technical employees, and automate previously uneconomic work.
4. Rethinking resiliency for AI factories
Quote: “The industry hasn’t quite figured out how to make AI factories resilient… because they don’t look like traditional IT systems.”
Quote: “If you try to just make a data centre resilient in general, you just create a copy. But if you are doing an AI data centre, and you use all the tools at your disposal, the logical capability of AI, the agents themselves, CSPs and sovereign infrastructure, you can come up with incredibly different and optimised resiliency architectures. The other piece, though, is as enterprises become more AI-enabled, and we’ve seen that at Dell, the actual things that need to be resilient, shrink.”
Traditional DR and simple duplication won’t scale for AI. Moving forward, resilience strategies must consider other tools; sovereign infrastructure, CSPs, edge, virtualisation and AI-driven automation; organisations will need to radically rethink about what “must be resilient” at the business interface level.
5. Sovereign infrastructure’s role expands beyond training
Quote: “Once you have large scale, aggregated infrastructure available in your country, there are many, many other things you can use it for. It is far more valuable than just running your government and providing training infrastructure.”
For example, robotics for defense with a back office AI framework, AI leasing, resiliency, fine tuning of smaller models, and inter-working zones where agents from multiple countries work together.
Quote: “Where the robots’ back end system lives, is as important as the robot itself, and in many cases, strategic to the national interest. You cannot imagine a country having the back office of its robotic fleet of drones for defense purposes… having that infrastructure anywhere other than in the country.”
In other words, national-scale, country-tethered AI infrastructure will be used for more than model training – robotics back end systems, certified agent services, disaster recovery, fine-tuning model ecosystems, and secure cross-border “inter-working” zones will all make these platforms strategic assets for a country.
6. Be ready for quantum
John Roese emphasised this bonus prediction, saying, “We are getting closer… When we can run viable algorithms on a quantum system… it will disrupt whatever AI is that day by making it orders of magnitude more efficient and effective than it was the day before. So again, I don’t think we will have giant quantum disruption in 2026 but you’d all need to pay attention to it, because there’s significant forward progress, and to be perfectly honest, much of it is happening in Asia, in countries like Singapore and Australia.”
In conclusion
Dell’s 2026 outlook reframes artificial intelligence: compute is necessary but not the only component to think about. CIOs must invest in governance, sculpt a durable knowledge layer, pilot autonomous agents with clear guardrails for unplanned changes, redesign resiliency for AI-native infrastructure and operations, and map sovereign strategy to business-critical workloads.
