Why cloud strategy still matters in the AI era

The transition from traditional data centers to cloud environments often leads to an evolution in your IT operations. How you approach cloud strategy determines how well AI can perform in it.

Crafting the right cloud approach creates a resilient, secure, and sovereign environment for AI to learn, adapt, and deliver maximum impact. However, many organizations discover that AI initiatives stall when cloud environments lack the governance, visibility, automation, and scalability required to support modern AI workloads. From managing GPU-intensive applications to ensuring data quality and compliance, AI places new demands on cloud operations that traditional approaches were not designed to address.

Thoughtful cloud strategy gives your organization the freedom to collaborate, experiment, and drive AI results without compromising governance or control.

When choosing an operational model for your cloud environment, you have many options: CloudOps, FinOps, SaaSOps, DevSecOps, NoOps, AIOps, and more. That can make it difficult to know what you need. Leading with an AI-first mindset, however, can help you achieve success with the operating model that works best for your hybrid and multicloud environment.

Rethink employee skills and job roles

Hybrid and multicloud environments call for multifaceted talent. This means moving toward multiskilled roles rather than the siloed positions common in traditional IT operations.

Where you might once have employed a dedicated network engineer or compute engineer, multicloud environments require broader expertise. For instance, a cloud engineer may operate a variety of cloud infrastructure services across multiple providers.

New operational models and multiskilled resources are also crucial because:

• The industry is shifting from infrastructure as a service (IaaS) to platform as a service (PaaS) and software as a service (SaaS).
• Lines are blurring between applications and infrastructure, as serverless computing and container technologies continue to gain momentum.
• AI-led automation is growing in use for self-healing systems, infrastructure as code and application deployment as code.

AI is also transforming IT operations itself. AIOps platforms can analyze operational data, identify anomalies, predict issues before they occur, and automate remediation actions, allowing teams to focus on higher-value initiatives.

Solidify your data foundation

The right enterprise data platform forms a solid foundation for an AI-first approach. Established data platforms act as a data custodian, storing significant amounts of information crucial for everyday operations.

For AI initiatives, data quality, accessibility and governance are just as important as data volume. Organizations need trusted, well-governed data that can be securely accessed by analytics and AI applications while meeting regulatory and compliance requirements. Strong data governance frameworks help ensure AI models are built on accurate, consistent, and explainable data, reducing risk while improving outcomes.

The reliability and integrity of these mature platforms, along with structured data assets, contribute to success with modern applications. Decades of organized data feed AI and analytics, increasing result accuracy. The historical depth supports business intelligence, making it easier to spot trends and opportunities.

Instead of replacing these platforms quickly, consider evolving them over time to preserve these data advantages by taking strategic measures:

• Manage risk by making incremental changes to protect data.
• Share practical data insights through continuous employee learning.
• Optimize resources with phased projects that balance preservation and innovation.
• Maintain the value of structured assets with data integrity practices.

Evolve your operating model as technology
needs do

Being AI-first means weaving AI into everything you do. The right operating model to support AI initiatives combines agility, collaboration, and adaptability. You don't have to select and stick with just one operating model when a combination may serve you better.

You might start by adopting IaaS on cloud and embracing the CloudOps model. In this mode, you remain responsible for cloud infrastructure operations like monitoring and patching. As you consume more SaaS applications, your operating model evolves to be SaaSOps, where infrastructure operational responsibility decreases. AIOps becomes increasingly valuable here, helping automate and optimize operations as AI workloads grow.

As AI workloads expand, organizations must also adopt FinOps practices to manage the significant compute and storage demands associated with model training, inference, and data processing. Visibility into cloud consumption and cost optimization becomes critical to scaling AI sustainably.

SaaS offerings are more turnkey and generally less expensive in terms of total cost of ownership, but it can be easy to underestimate the level of IT operations needed to adopt them successfully. You'll need your own IT operations model to address things like identity management for SaaS applications and integration with employee life cycle management.

Continue collaborating and evolving your models as you go. To establish an effective cloud FinOps model, for example, collaborate across the organization, including business, finance, HR and sales, to optimize your cloud deployments and expand savings opportunities. Tapping different disciplines helps you understand the whole picture.

Grow your cloud and AI talent via training

Current data center support staff and broader IT operations team members may be strong candidates for cloud and AI training and certification. Give these employees the opportunity to build the skills new operating models require.

If you have the resources, consider establishing an in-house training program that provides coaching, certification, and hands-on experience. If that is not feasible, establish training and certification programs offered by respected cloud and AI experts. Enrolling your most capable people in these programs is one of the most direct ways to build the teams you need.

What makes a cloud strategy AI-ready

Traditional IT operations models and cloud strategies have real value – but succeeding with an AI-first approach means knowing where they need to evolve. The organizations pulling ahead are the ones treating cloud as the infrastructure layer that makes AI possible, not as a destination itself. That means cleaner data, more flexible operating models, and teams with the skills to manage increasingly complex environments.

Unisys can help your organization work through that evolution by building AI-ready cloud environments through Cloud Transformation solutions, Hybrid Cloud Managed Services, and intelligent operations capabilities.

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