The rapid adoption of AI technologies has ushered in a new era of cloud strategy – one where agility and resilience at scale make cost and resource optimization more crucial than ever.

Modern enterprises now view hybrid cloud environments as interconnected systems spanning hyperscaler instances and internal data centers, rather than just a blend of on-premises and public clouds. This interconnectedness is the foundation upon which successful AI strategies are built.

For success, organizations must make the best use of assets, including intelligent consumption and management of cloud resources powered by AI. Four strategic approaches can help you optimize your hybrid cloud environment for AI success.

1. Address technical debt to realize AI potential

Before your organization can fully leverage AI in hybrid cloud environments, you must confront an uncomfortable truth: technical debt — the cumulative weight of legacy systems, outdated dependencies and architectural patterns built for yesterday's workloads — compounds exponentially in AI deployments. Deploy a platform burdened by technical debt, and you'll likely never resolve it post-deployment — the weight only increases as AI workloads scale.

Understanding and resolving technical debt is central to AI success in hybrid environments. Tackling technical debt requires understanding workload relationships and how to map them out for a deeper understanding of the issues. Hybrid cloud infrastructures fortified with AI-based monitoring and analytics provide the visibility needed to manage dependencies and streamline workloads proactively.

Since AI capabilities are often net new to organizations, consider establishing a greenfield environment for your deployments. This approach narrows the scope of technical debt you need to resolve and creates a landing zone for new AI applications, refactored workloads or a modernized platform strategy that supports your long-term goals.

2. Recognize AI as an accelerator for cloud innovation

Rather than incremental improvements, the most successful AI initiatives take rethinking your organization’s hybrid cloud investments, including how they deploy and manage workloads and data centers.

Success with AI requires a shift in traditional thinking about infrastructure and application management. Revisiting your organization’s current IT infrastructure and operational approaches enables you to support new and evolving AI workloads effectively.

As you investigate your hybrid strategy, remember the golden rule of AI deployments. AI requires data. So, your strategy should be informed by the location of the data that will feed your AI applications. The beauty of a hybrid cloud strategy is the flexibility in operating and consumption models, but the location of your data will largely dictate the location of your AI application(s).

3. Automate operations to support AI growth

AI workloads are inherently complex and dynamic, often spanning multiple environments within hybrid clouds — from on-premises data centers to hyperscaler cloud instances. Managing these requires agility and operational consistency that manual processes simply can’t guarantee. AI-powered automation can help your organization manage this complexity.

Automation drives innovation. It frees internal teams to focus on higher-value AI application development, analytics and strategic initiatives rather than troubleshooting operational issues. It also helps enterprises reduce risk and technical debt. Consistent AI-assisted management minimizes human errors, accelerates deployment cycles and improves system resilience. For example, automated provisioning, scaling and patching in a hybrid cloud ensure that AI workloads receive the right resources at the right time, eliminating manual bottlenecks.

By deeply embedding automation into hybrid cloud management practices, enterprises can harness AI’s full potential and stay ahead in the competitive market. Automation also enables consistent deployments, higher-quality delivery and configuration management. However, automation doesn't necessarily require agentic AI in all cases. Not yet, anyway.

4. Embrace FinOps for AI cost management

Optimizing your hybrid cloud environment requires preparing your data center and developing skills for new technology requirements. Cloud financial operations (FinOps) provides the visibility, governance and control that can help your organization scale AI-driven applications.

Practical FinOps approaches are crucial when deploying costly AI workloads across hybrid clouds. AI enables more intelligent cost optimization through predictive analytics and automated governance, ensuring that enterprises avoid waste and maximize the return on cloud investments. As organizations experiment with and scale AI, FinOps practices are invaluable tools for maintaining economic control and reducing cloud sprawl.

Maximize your AI success

AI in hybrid cloud environments demands a thoughtful strategy that balances operational models, technical debt and financial management. Organizations that adopt these AI-driven hybrid cloud strategies are better positioned to fully leverage their cloud investments and drive innovation. Discover how Unisys's AI solutions and services can guide your AI implementation, enabling you to streamline operations, enhance system performance and achieve measurable business results. And explore our hybrid cloud report for insights.