APMdigest's Predictions Series continues with 2026 Cloud Predictions — industry experts offer predictions on how Cloud will evolve and impact business in 2026. Part 1 covers AI's impact on cloud and cloud's impact on AI.
CONVERGENCE OF CLOUD AND AI STRATEGY
As we head into 2026, cloud and AI strategy are converging in a way we haven't seen before. AI workloads are becoming more demanding, regulatory expectations are increasing, and organizations are realizing that not every workload belongs in the same place. The focus is shifting from a "cloud-first" mindset to a "cloud-fit" approach, designing architectures that deliver elasticity and speed for AI, while still giving enterprises the control, reliability, and compliance they need.
At the same time, AI is transforming how cloud environments operate on a daily basis. Rather than manually navigating tools, teams are now supervising, guiding, and orchestrating systems that can manage many processes autonomously. Importantly, humans remain at the center of this transformation. Their role is not diminished, but redefined.
The organizations that will lead in this next phase are those that continuously measure what's working, refine workload placement based on evidence rather than trends, and build adaptable cloud foundations that can evolve as quickly as their AI ambitions. This combination of intentional architecture, AI-enabled operations, and disciplined measurement is what will define true cloud maturity in 2026.
Mary Elizabeth Porray
Global Vice Chair for Client Technology, EY
CONVERGENCE OF CLOUD AND AI INFRASTRUCTURE
The AI and Cloud Convergence - 2026's Enterprise Breakthrough: 2026 is shaping up to be the breakthrough moment when AI and cloud infrastructure finally become one powerhouse force. 2025 was dominated by chatter around GenAI and cutting-edge models, but the game-changer will be AI agents embedded right into the heart of cloud operations. Companies will shift from wondering if AI fits their strategy to actually putting it to work by streamlining processes, supercharging their apps, and spinning up new workloads at lightning speed. When these technologies merge, AI transforms from a high-potential technology into an essential business tool that yields tangible, beneficial results.
Jonathan LaCour
Chief Technology Officer, Mission
Cloud platforms will unite scale with responsible AI as a guiding principle. In 2026, AI will be deeply woven into cloud infrastructure to enable secure, transparent automation that accelerates innovation without compromising compliance — ensuring trust and human oversight at every step.
Dan Miller
EVP of the Financials and ERP Division, Sage
HYBRID AI
Hybrid AI Becomes the New Default: The "cloud-everything" era is coming to an end. Data gravity, sovereignty laws, and inference cost control are drivers for on-premises and model-to-data architectures. Enterprises are realizing that critical AI workloads need to remain close to their data, whether on-premises or in hybrid environments, to meet stringent requirements for performance, compliance, and data sovereignty. As a result, DevOps and data teams will increasingly build intelligent, governed "˜AI factories" inside the enterprise, integrating AI pipelines directly with existing systems rather than relying solely on public cloud services. This approach ensures organizations can scale AI responsibly while maintaining control over sensitive information and operational efficiency."
Justin Borgman
CEO and Cofounder, Starburst
AI CHALLENGE: HYBRID CLOUD
Hybrid cloud complexities will continue to stifle AI success, hitting enterprises' bottom lines in 2026 if they fail to redesign their architectures. This year, IT leaders had to learn a difficult lesson on their AI journeys: you can't drive IT innovation without the proper foundation. Companies have and will continue to face difficulties implementing AI and seeing ROI from their deployments due to the scattering of data across multiple clouds and on-premises environments without considering interoperability.
Defaulting into hybrid cloud creates a disjointed and disconnected architecture — causing enterprises to enter into a game of hide-and-seek with their own data, and there can be no AI without access to trusted data. To be successful and see business value from their AI projects in 2026 and beyond, enterprise IT leaders will need to adopt a more strategic approach to IT infrastructure. By integrating end-to-end architectural principles, optimizing workload placement, and ensuring data governance, security, and compliance, IT leaders will adapt to new AI and data needs, building strategically designed hybrid environments that make it possible to uncover and optimize data for AI.
Rohit Badlaney
GM - IBM Cloud Product and Industry Platforms
Next year, AI workloads will continue exposing limits in cloud architecture. Cloud platforms and hyperscalers were designed under an assumption of data adjacency — when data and compute are in the same network. The AI challenge is the disaggregation of compute and data, with compute in one cloud and data in a different cloud. Replicating massive datasets across clouds kills velocity, increases cost, and is simply impossible in privacy-regulated sectors. Industry leaders will need to seek creative solutions for this growing issue.
Richard Yu
CPO, LucidLink
AI CHALLENGE: CLOUD COSTS
Cloud costs and arch complexity will continue to stall adoption: Whichever way we look at AI, it's costly. If companies stay in the cloud, it's expensive to run complex AI projects there. It's also prohibitively expensive to migrate infrastructure back in-house unless organizations are certain it's the right strategy (i.e. when it's cheaper to have on-prem control than to operate in the cloud). The uncertainty around selecting the right technical framework, coupled with heavy potential costs, will slow adoption. What we are sure of is that companies want to push the boundaries of AI and get there quicker than their competitors, and so they will look to iterate quickly, and learn first from their cloud providers, before committing to a long-term strategy."
Tobie Morgan Hitchcock
CEO, SurrealDB
AI DRIVES REPATRIATION
The year hyperscale AI comes home: Teams want to gain tighter control over AI models and GPUs so we'll see a move back to the datacenter for the largest companies. It's a cost control issue: with high charges, potentially almost as high as revenue for an AI Assistant company, to run the largest-scale AI projects in the cloud, it makes sense for companies of that size to manage these locally. This will prompt the move back to the datacenter, and on-premise environments in 2026.
We see this playing out in the GPU space. As cloud costs grow, companies such as Nvidia are investing heavily in novel GPU technologies, in expectation of rapid growth in on-premise and co-located AI deployments.
Tobie Morgan Hitchcock
CEO, SurrealDB
NEOCLOUD CONSOLIDATION
The Great Neocloud Consolidation Begins: More than 80% of the NVIDIA and AMD GPU market share will concentrate among a handful of neocloud and alternative cloud providers worldwide for both the NVIDIA and AMD AI ecosystems. The winners will be those with the trifecta of capital, scale, and go-to-market execution: the ability to raise capital and keep pace with demand, quickly deploy massive GPU clusters, and attract top-tier AI customers to their platforms. Those lacking one or more of these capabilities will struggle to compete and begin to fade from the market.
Kevin Cochrane
CMO, Vultr
ALTERNATIVE HYPERSCALERS
The Rise of the Alternative Hyperscaler: More than a neocloud, enterprises will recognize the need for an alternative hyperscaler. This new class of cloud provider will combine full public cloud capabilities with specialized AI infrastructure services, while supporting an open, composable ecosystem. The winners will be platforms that deliver scale, flexibility, and openness, enabling organizations to deploy advanced AI workloads without being locked into a single vendor or limited stack.
Kevin Cochrane
CMO, Vultr