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Why AI readiness matters

What does AI-ready IT Infrastructure require?

AI readiness requires an infrastructure built for how AI is used, processed, and scaled. From workplace devices to compute, storage, and security, every layer needs to be ready for what comes next. 

 AI Workplace On-device AI enables features such as speech enhancement, eye contact correction, and meeting summaries directly on the device. The challenge is scaling AI-capable devices while keeping the existing fleet manageable, secure, and ready for changing technology requirements.
AI Servers On-premises and hybrid environments provide greater control over sensitive data, processing, and security. At the same time, state-of-the-art GPU and NPU hardware is expensive, ties up capital, and can become obsolete faster than it can be depreciated. The challenge is to maintain the flexibility to scale infrastructure while operating within increasingly tight budgets.
Storage & Security AI workloads increase demands on storage capacity, data management, and security. The challenge is ensuring that data remains accessible and protected while building an infrastructure that can scale with growing workloads and future AI requirements.

How Can Companies Manage the Costs of IT Infrastructure?

Assess: Gain transparency on IT costs before you invest.

AI, new software generations and growing IT requirements can quickly drive hardware investments into the millions, particularly for AI servers and high-performance infrastructure. A Total Cost of Ownership (TCO) calculation looks beyond the purchase price to include deployment, maintenance, support, replacement and end-of-life. Usage-based models can provide greater cost transparency, predictable payments and a lifecycle-based approach.

Module: Stay flexible as technology evolves

AI technology is evolving rapidly, making flexible technology refresh increasingly important. With a lifecycle-based usage model, technology can be replaced at the end of an agreed usage period – for example, after 36 months. This allows organizations to refresh technology regularly and adapt infrastructure to changing performance requirements.

Optimize: Free up capital for innovation

AI transformation can require significant investment in new hardware and infrastructure. A usage-based model can reduce upfront capital requirements, freeing resources for strategic priorities such as AI, cybersecurity, digitalization and business innovation. The goal is not simply to spend less on IT, but to direct capital where it creates the greatest value.

Scale: Adapt your technology as AI workloads and business needs evolve.

AI workloads are not always predictable. As use cases move from experimentation into production, computing requirements can change quickly. A flexible usage model makes it easier to scale technology capacity up or down, without committing to infrastructure based solely on today's needs.

How AI-Ready Is Your IT?

 

Take the AI Readiness Check to assess your current IT setup, identify potential gaps, and discover where you can make your infrastructure more flexible, scalable, and ready for what comes next.

 

 

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Frequently Asked Questions

What is AI-ready IT infrastructure?

AI-ready IT infrastructure provides the computing, processing, storage and security capabilities required to support an organization's AI workloads and use cases.

How does AI affect IT hardware requirements?

AI can increase demand for GPUs, NPUs, high-performance CPUs, memory and storage. Requirements vary depending on whether AI workloads are processed locally, on-premises, in hybrid environments or in the cloud.

How can companies finance AI infrastructure?

Companies can purchase AI infrastructure or use alternative models such as leasing and other usage-based approaches. The appropriate model depends on investment requirements, cash flow objectives, technology cycles and the organization's financial strategy.

Why are flexible IT usage models relevant for AI?

AI technology evolves rapidly. Flexible usage models can help organizations adapt their infrastructure and refresh technology without relying solely on large upfront investments.

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