Advance your hybrid cloud with these four fundamentals

A tower built on a solid foundation with well-constructed floors stands strong for years to come. Your cloud environment needs the same careful layering, especially as AI becomes central to business operations.

Organizations that once mandated a cloud-first protocol have shifted to cloud adoption when it makes the most business sense. According to Unisys’ 2025 Top IT Insights Report, regulated, sensitive resources are often best suited for on-prem, and data-intensive, scalable applications are frequently moved to the cloud.

Hybrid cloud lets you deploy the best environment based on needs, costs and speed. However, optimizing this environment to prepare for AI goes beyond determining what goes where. Successful hybrid cloud integration requires four essential layers to build a solid foundation for innovation.

#1: A strong infrastructure layer for scalability

Your infrastructure layer includes compute, storage and network resources necessary for smooth hybrid cloud operations. Building a solid infrastructure supports easier provisioning, automated workloads and seamless data integration for AI applications. Open and flexible compute, networking and storage pooled into shared resources would be essential to dynamically provision resources as needed. This approach increases flexibility, reduces costs and enhances scalability.

Without taking the necessary steps first to ensure flexible architecture, organizations may avoid hybrid cloud for AI workloads because it is:

• Unprovisioned or not software-enabled: A significant backend lift must occur to support them.
• Limited in its shelf life: Without proper planning, you'll quickly run out of computing power when trying to scale AI workloads.

Ensuring adequate infrastructure increases elasticity, agility and availability. A flexible, provisioned infrastructure is also necessary to receive structured and unstructured data and support data integration for AI applications.

#2: A data layer for AI optimization

Quality data is like oxygen — essential for survival and performance. Without a continuous flow of quality, scalable data, AI models can't function effectively or produce reliable results and actionable insights. To maintain this flow, your data layer needs the right tools and solutions for storage, management, governance, security and encryption.

Getting the most from AI requires carefully connecting it with your data. All the necessary automation on top of the engineering, such as large language model operations, relies on a strong relationship between data and AI. Encourage this connection by:

• Knowing the cloud solutions that bring data and AI together
• Identifying business challenges and high-impact use cases where AI could benefit your organization
• Aligning your AI strategy with your data, technology and business strategy

Establishing a data modernization strategy can also give you the crucial insights required to put AI to use in your organization. Together, these fundamentals enable AI to ingest your data seamlessly.

#3: A governance layer for regulatory compliance

The governance layer of hybrid cloud architecture involves policies, processes and technologies that support your environment’s management, compliance and security. Storing your data properly in purpose-built databases enables you to govern it properly.

Introducing AI requires additional governance. This means integrating responsible AI practices into your existing compliance framework to align it with government requirements regulating how you’re allowed to use AI and the data that feeds models.

Doing so can decrease your data breach vulnerability and lower the risk of cyber attackers obtaining your proprietary business data or sensitive customer and partner information. Responsible AI practices also solidify the trust of your customers, partners and prospects by reassuring them of your commitment to protecting their data.

#4: A machine learning model layer for insights

The machine learning model layer provides core intelligence for AI applications in hybrid cloud environments. By fine-tuning pre-trained language learning models for specific domains, you can gain actionable business outcomes.

For the best results, this layer relies on effective model training and validation and continuous model monitoring. The payoff of spending attention on this layer can be significant:

• Better resource allocation as machine learning models analyze usage patterns and resource demands
• Automated, dynamic scaling as predictive models can anticipate workload demand changes and scale resources accordingly
• Performance optimization of AI workloads as machine learning models identify bottlenecks and inefficiencies in your infrastructure
• Cost optimization as the algorithms analyze cost data and usage patterns and detect cost-saving opportunities
• Strengthened security and compliance as models detect anomalies, identify security threats and ensure regulatory adherence

If AI needs to seamlessly integrate with your business application, consider an additional apps and integration layer. You can build this layer with APIs and AI services, chatbots, AI dashboards and enterprise software integration.

Build a foundation primed for AI

AI amplifies what hybrid cloud can do for your organization. To realize it, build up your infrastructure, data, governance and machine learning layers as part of a solid foundation. To hear how other managers and executives are working with AI, download our report and learn how AI solutions from Unisys can assist with your foundation building so you can optimize your AI use.