Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Steve Carlini, VP of Data Centers and Innovation at Schneider Electric
The AI disruptions of the past few years have only been the prologue to what’s coming in 2026: AI’s full integration into data center processes and builds. It’s a moment we’ve been building up to ever since OpenAI’s ChatGPT brought Artificial Intelligence into the mainstream in late 2022, sending shockwaves through everything from academia and mental health care to every type and size of business. But a more profound transformation will begin to take hold in 2026 as AI becomes ever more ingrained in every aspect of life and the focus shifts from Large Language Models (LLMs) to AI inferencing. In some ways, 2026 will be the year the rubber truly hits the road when it comes to AI.
The data center industry is bracing for denser AI workloads that demand advanced cooling, increased retrofitting of existing facilities, greater focus on building successful AI factories and expanded use of digital twins for greater efficiency. With geopolitical uncertainty and rapid technological change, resilience and adaptability will remain critical for data centers. Let’s look at what we can expect in 2026 and beyond.
AI Transforms Functions and Companies
AI is playing a role in many business functions, and its use is increasing. According to McKinsey’s annual survey on the state of AI, 78% of organizations use AI in at least one business function, compared to 72% in early 2024 and 55% the year before. It’s most often used in sales and marketing, according to the McKinsey survey, but its use will expand into other arenas, including manufacturing and supply chains, medical facilities, financial institutions and data centers.
- Manufacturers using AI to support demand forecasting, a longstanding challenge in the industry, are able to improve their forecast accuracy by a median 30 percentage point.
- Hospitals increasingly use predictive AI to simplify or automate billing procedures, help with appointment scheduling and identify high-risk outpatients for follow-up care.
- Financial institutions are using AI to enhance fraud detection, payment optimization, and risk management for businesses.
- In data centers, AI-driven cooling systems use predictive analytics to minimize overheating and energy waste. AI can also enhance grid efficiency, integrate renewable energy sources, and lower carbon emissions by analyzing vast amounts of data to predict and balance electricity supply and demand.
As adoption continues to grow, businesses will not only rely on AI, but see it transform their companies and industries themselves. AI agents operating with little, or no supervision will become core to business processes. These agents will rely on various models working together and require significant processing power and data center capacity or an AI factory.
The Rise of AI Factories
An “AI factory” is essentially a data center that outputs intelligence, rather than simply storing and processing data. We are moving beyond AI training models to inferencing, which is where businesses will see ROI from AI. An AI factory trains a model, fine tunes it and then performs inference, generating valuable intelligence that can be sold or used to gain a competitive advantage.
While typically requiring less power per server than training, inferencing workloads are increasingly varied and pervasive. They now range from simple chatbot prompts to complex real-time analysis in healthcare, retail, and other industries using autonomous systems and agentic agents. Depending on the deployment and workload, inference environments can range from less than 20kW for compressed or tuned models, up to 140kW per rack for more advanced agentic use cases.
By 2030, we expect the data center market to reflect this diversity:
- 25% of new builds will be <40kW/rack, primarily inference-focused.
- 50% will fall into the 40-80kW/rack range for mixed inference and training workloads.
- 25% will exceed 100kW/rack, dedicated to large-scale training clusters.
To keep up with AI, operators must continue to look to the next and most advanced GPUs, for example, the NVIDIA Rubin CPX scheduled to release in late 2026. Coupled with NVIDIA Vera CPUs and Rubin GPUs inside the new NVIDIA Vera Rubin NVL144 CPX platform, this integrated NVIDIA MGX system packs 8 exaflops of AI compute to provide 7.5x more AI performance than NVIDIA GB300 NVL72 system.
Robotic Functions Get Advanced
Robots have been helping keep people safe for years by performing tasks like radiation detection and bomb deactivation, and more recently there have been advancements in robotic lawn mowers and vacuum cleaners. In 2026, AI will extend automation to virtually everything that moves, such as flying drones. They will make major leaps in functionality in areas that include deliveries, surveillance, search and rescue, disaster response, wildlife monitoring, firefighting, healthcare and agriculture and even passenger transportation.
Behind the scenes, these advancements will require extraordinary processing and network capacity in AI factories because they rely on high-definition video as an input. Data centers themselves are also embracing the benefits of robotics and automation. Their uses include security and environmental monitoring, server installation and maintenance, cable organization, hard drive replacement, and operating and optimizing liquid cooling systems.
Digital Twins Will Permeate Design of Almost Everything
In 2026, we will see the rise of digital twins as processing power continues to evolve in AI data centers and advanced platforms are developed, like NVIDIA’s Omniverse and Cosmos. Data center operators will use digital twins to achieve greater efficiency and accelerate development by designing and simulating highly complex physical objects, systems, and processes. Take for example a data center’s power system itself. ETAP sophisticated modeling technology can create a virtual replica of a data center’s electrical infrastructure through integration with NVIDIA Omniverse.
Liquid Cooling Goes Mainstream
Traditional cooling solutions are no match for the processing power required for AI. In 2026, rack densities will reach 240 kW per rack. 2028 will see densities reach 1 MW rack, and research is underway to determine whether 1.5 MW per rack is possible. With ultra-high densities on the horizon, advanced cooling techniques are required to keep these systems operating. This means liquid cooling will transition from an outlier to the norm for data center cooling solutions.
Retrofitting Data Centers for AI
As AI becomes central to business strategy, organizations of all sizes need access to next-generation data centers. While hyperscalers will pursue greenfield builds as they have the need and the resources for large-scale investments, smaller companies will eye brownfield retrofits as a viable strategy for creating AI-ready facilities. Infrastructure providers have developed solutions to upgrade existing facilities, including larger IT racks, higher power PDUs, and liquid cooling “bolt on” rear door heat exchangers specifically designed for accelerated compute AI server racks and clusters. These components help ensure that AI is feasible for a broad spectrum of companies, not just the biggest ones in the world.
Continued Focus on Sustainability Concerning Power Demands
Power sourcing will persist as a central theme in 2026, with data center operators continuing to adopt a diverse array of power solutions, including natural gas turbines with carbon capture, HVO-fueled back-up generators, wind, solar, geothermal, and battery storage. Worldwide, renewables currently supply 27% of the electricity consumed by data centers, mostly through wind, solar and hydropower sources. Total power generation for renewables is projected to grow 22% each year until 2030, meeting nearly half of the anticipated growth of data center electricity demand.
Agility Key to Stability
Expect 2026 to be a pivotal year, where AI’s impact moves from a disruptive force to a foundational element of business and technology. As AI reshapes every layer of digital infrastructure, tomorrow’s data centers will not simply support technology – they will enable intelligence itself. From liquid-cooled AI factories to retrofitting existing facilities to highly advanced digital twins, data center operators will need to embrace outside expertise in the quest to keep up with the breakneck speed of AI. Global partnerships are critical to maintain resilience and agility given the technological shifts and geopolitical uncertainties at play.
