At the start of last year, we outlined 5 Cloud Computing Trends for 2025, from AI-powered cloud operations to GenAI workflow penetration and the rise of quantum computing. A year later, many of those predictions have played out and then some. AI did not just augment cloud operations, but fundamentally reshaped how organizations think about infrastructure. Multi-cloud became a default label, though for most organizations, the real question is not how many clouds they are on, but whether any of them are configured well. Edge-to-cloud integration has moved from a nice-to-have to a competitive necessity.
The question is no longer whether to migrate. It is whether you are getting the full value from what you have already built.
But 2026 brings a different kind of question: with 96% of companies using public cloud services, 92% running multi-cloud strategies, and the global cloud market exceeding $1 trillion, adoption is no longer the debate.
The question is no longer whether to migrate. It is whether you are getting the full value from what you have already built.
The answer, for most organizations, lies in what we call the hybrid reality, and it is reshaping enterprise IT in ways most companies are not fully prepared for.
Being on the Cloud Is Not the Same as Being on the Right Cloud
A tempting narrative is circulating in boardrooms. Cloud migration is a solved problem. The heavy lifting is over. Time to move on.
That narrative is wrong, and we see it firsthand.
Recently, we worked with two customers who challenged this assumption in very different ways. One came to us running on a legacy hosting provider, with costs climbing while delivering diminishing value. Moving to AWS was not about chasing the latest trend. It was about escaping an infrastructure setup that had become more expensive than necessary.
The second customer was running on a managed platform built on AWS, but with guardrails that prevented them from accessing the full breadth of AWS services. They were not off the cloud. They were on it. But they were boxed in, unable to tap into the tools and architectures that could actually move their business forward.
Neither company was starting from zero. One was on infrastructure that no longer made financial sense; the other was on the cloud but boxed in. Both needed a migration in 2026, not because they were behind, but because what they were running on wasn’t right for where they’re headed.
82% of cloud decision-makers cite managing cloud spend as their top challenge1. An estimated 29% of cloud spending is wasted on underused resources1.
This is far more common than most people realize. 82% of cloud decision-makers cite managing cloud spend as their top challenge1. An estimated 29% of cloud spending is wasted on underused resources1. And 76% of organizations report a shortage of skilled cloud professionals, struggling to hire and train for cloud architecture, security, and DevOps roles2.
The pattern is consistent: most organizations are not overclouded. They are underoptimized. When cloud costs spiral, it is easy to blame the platform. In practice, the root cause is usually a strategy gap, not a technology gap: lift-and-shift workloads that were never modernized, resources left running long after they are needed, services chosen based on familiarity rather than fit for purpose, and governance models that do not match how teams actually build and deploy.
But cost is only one dimension of the problem. Many of the optimization opportunities we see stem from gaps in foundational cloud practices: infrastructure-as-code that was never fully adopted, CI/CD pipelines that are incomplete or inconsistent, manual processes that should have been automated years ago, and architectures that have not been reviewed against current best practices. It is worth noting that CI/CD alone is not DevOps. Too many organizations invest in pipeline tooling while never adopting DevOps as an operating model: shared ownership between development and operations, continuous feedback loops, and accountability that extends beyond deployment. The tooling underdelivers when the culture around it has not changed.
Taken together, these are platform engineering challenges. Strategy gaps and execution gaps are not separate problems. They are symptoms of organizations that have not invested in a coherent internal platform, one that governs how teams build, deploy, secure, and operate workloads consistently. Without that foundation, AI adoption, advanced workload placement, and long-term scalability become far harder than they need to be.
AWS recognizes this. Their investment in cloud optimization now extends well beyond cost management to include long-term architecture improvement and modernization. The AWS Well-Architected Framework Review, now part of the broader Cloud Optimization organization, reflects this shift. Tools like AWS Cost Explorer, Compute Optimizer, and Savings Plans give organizations real levers to pull on spend. Services like AWS Outposts and Local Zones allow teams to keep workloads closer to where they need to be while remaining within the AWS ecosystem. But the organizations seeing the best results are pairing those tools with the operational foundations that make them effective: IaC, automated governance, security embedded in the pipeline, and architecture that is continuously reviewed, not just initially designed.
The organizations getting the most from their cloud investment are doubling down on doing cloud right, from the foundation up.
The organizations we see getting the most from their cloud investment are not retreating. They are doubling down on doing cloud right, from the foundation up. That is not a step backward. It is cloud maturity.
AI Changed the Economics of Cloud
In our 2025 blog, we predicted AI-powered cloud operations and deeper penetration of GenAI workflows. While both happened aggressively, what few anticipated was how dramatically AI would reshape cloud economics.
AI workloads are resource-intensive. Fine-tuning models on proprietary data, running inference at scale, operating ML pipelines, and supporting retrieval-augmented generation all consume specialized compute, significant storage, and growing amounts of energy. The result: cloud bills tied to AI are under more scrutiny than ever, and organizations are being forced to think carefully about where each workload actually belongs.
For enterprises, this creates a strategic inflection point. The question is no longer whether to run AI in the cloud. It is which parts of the AI workload belong in the cloud, which belong closer to the edge or on-premises, and how to make that decision in a way that balances cost, performance, and control.
Some organizations are discovering that keeping inference local while using the cloud for training and fine-tuning can materially change their cost structure. Others are applying FinOps disciplines to ensure every dollar of AI-related cloud spend produces a measurable return. In both cases, the answer is not less cloud. It is a more intentional cloud, which is the same platform engineering discipline we discussed earlier, applied to a new class of workload.
This is also where regulation enters the picture. Compliance and data governance have long been infrastructure challenges in regulated industries such as healthcare, finance, and government. What is changing in 2026 is the scope. The EU AI Act takes full effect in August. New state-level AI compliance laws in the United States continue to roll out. Frameworks like NIS2, DORA, and the EU Product Liability Directive raise the bar further. For organizations that previously operated outside heavily regulated environments, AI is bringing compliance requirements to their doorstep for the first time. Where AI models are trained, where inference runs, where data is stored, and who can access it are now factors in workload placement decisions alongside cost and performance. Organizations that already treat compliance as an infrastructure design principle have a head start. Those that do not will need to revisit cloud environments that may not have been materially updated since their initial migration.
The Rise of Agentic AI in Cloud Architecture
In 2025, we talked about AI powering cloud operations. In 2026, that concept has evolved into something more structural: agentic AI embedded within cloud architecture itself.
AI agents, autonomous systems that manage resource allocation, enforce governance, and route workloads between cloud and edge in real time, represent a new architectural layer. Cloud environments must now support not only applications but also intelligent systems that manage them.
That requires rethinking observability, governance, cost controls, and security models from the ground up.
So, Why Cloud in 2026?
This is the question we started with: Are you getting the full value from what you have already built?
Cloud in 2026 is about optimization, integration, and intelligence. It is about building hybrid architectures that place every workload where it performs best and costs the least, using AI as a force to monitor, scale, secure, and continuously tune cloud environments in real time, and preparing infrastructure for a regulatory environment that is more demanding than ever.
The companies that treated cloud as a one-time migration project are falling behind. The ones that treat it as an evolving strategy, continuously reassessed, redesigned, and refined, are pulling ahead.
Prepare for the Hybrid Reality with ClearScale and AWS
Whether you are evaluating workload placement, modernizing a legacy migration, building agentic AI pipelines, or trying to make sense of your cloud bill, we can help you turn cloud from a cost center into a competitive advantage.
Schedule a call with one of our AWS experts to discuss your vision.
Authors: David Ernst, Director of Migrations
Sources
- Flexera, 2026 State of the Cloud Report https://info.flexera.com/CM-REPORT-State-of-the-Cloud
- Softjourn, 100+ Cloud Computing Statistics for 2026 https://softjourn.com/insights/cloud-computing-stats
