From hype to an essential part of day-to-day routine, AI has taken a leading position in the evolution of the technology world, providing a distinct competitive advantage by equipping enterprises to develop smarter products, optimize processes and deliver personalized customer experiences. The one remaining question is: how can we bridge the gap between merely recognizing AI’s potential, implementing random AI initiatives and achieving measurable results? Being a part of a digital transformation strategy, AI requires the most reliable, secure, interconnected and high-performance infrastructure on the market. It should be able to handle both existing and future AI-powered functions while meeting companies’ evolving needs.
In this article, we’ll explore the definition and role of AI-ready infrastructure, its benefits and the cost of inaction.
Source: Nutanix
Current AI adoption and market growth
The following market indicators demonstrate AI’s exponential growth trajectory:
- According to Statista, the size of the AI market in 2025 is 244bn U.S. dollars.
- Gartner predicts an exponential growth of Generative AI, with 80% of companies projected to adopt it by 2026.
- A report by CompTIA reveals that 55% of companies are currently using AI, and an additional 45% are exploring its implementation for the future.
With an expected annual growth rate of 36.6% between 2024 and 2030, AI is a major driver of global economic growth. It confirms that AI is no longer optional; it is a strategic necessity that fosters innovation and growth. However, there is a critical gap between the ambition to adopt AI and the ability to successfully scale these initiatives. One of the biggest obstacles to AI integration is the need for a robust technical infrastructure, without which even the most ambitious AI strategies will never reach production and deliver positive, measurable outcomes.
What is “AI-ready infrastructure”?
With a growing number of innovations and mounting pressure to incorporate them, companies need infrastructure as dynamic as their business. Even though a physical map can still guide you to where you need to go, it can’t compare to a GPS navigator that can instantly analyze your location, build the optimal routes without traffic and adjust to you on the go without the extra action and expense of maintaining its relevance. In the same way, traditional IT infrastructure still performs, but it is not equipped to handle high-intensity AI requirements like training large language models (LLMs) or processing high-volume, real-time data streams.
In contrast, AI-ready infrastructure is initially built to run AI applications and grow in line with technologies and business goals. It can do so by leveraging a compatible, fit-for-purpose support infrastructure to optimize the cost and scale of AI workloads while ensuring consistent management, compliance and secure management of AI models and data.
An AI-ready infrastructure provides the compute, storage, networking and management platforms that can handle high-volume data ingestion, model training, real-time inference and continuous deployment securely and cost‑efficiently.
Key attributes include:
- Scalable compute. Elastic GPU/CPU resources for training and inference.
- High‐performance storage. Low-latency, high-throughput data access for large datasets.
- Optimized networking. Low-latency, high-bandwidth connectivity across edge, cloud, and on‑premises.
- Automation & orchestration. Infrastructure-as-code, containerization, and MLOps pipelines.
- Governance & security. Built‑in policies for data privacy, model provenance, and compliance.
Competitive advantages of infrastructure modernization
Operational efficiency and cost reduction
While the initial investment in AI-ready infrastructure may seem substantial, the long-term cost savings will be significant. By automating repetitive tasks, reducing manual intervention and identifying inefficiencies that would otherwise go unnoticed, organizations not only reduce operational costs but also focus human talent on more important strategic activities. This creates a multiplier effect on productivity and innovation. Moreover, with a modern infrastructure, the total cost of ownership is lower than with an outdated one, which needs resources to keep it up and running.
Data mobility and security
AI workloads require access to vast amounts of data from multiple sources, making unfettered data mobility across data centers, cloud and edge environments a must-have for organizations. In addition, enterprises generate massive amounts of data daily, and this data remains untapped without the right tools. An AI-ready infrastructure enables organizations to harness the power of data analytics by providing actionable insights that drive informed decision-making. Hence, the need for rigorous data governance, including data validation and protection, is more important than ever. Based on “The CEO’s guide to generative AI: Platforms, data, and governance”, the most common barriers to implementing generative AI will be concerns about data provenance and validation (61%) and data security (57%). This underscores why robust data protection mechanisms should be core components of any AI-ready infrastructure strategy, balancing availability and security.
Solid and agile foundation for future growth
Traditional IT infrastructure often struggles to keep pace with the dynamic demands of modern businesses, while the scalable and flexible nature of an AI-ready infrastructure allows enterprises to adapt quickly to ever-changing market conditions. It helps maintain relevance and achieve long-term growth. Investing in infrastructure today is like laying a foundation for your future house, as it determines not just what you can build now but what will be possible tomorrow.
Quantifiable wins
- Up to 25% reduction in infrastructure costs via cloud‑native platforms
- 1.5× higher revenue growth and 1.6× greater shareholder returns for AI leaders
- 27–133% productivity boosts from targeted AI initiatives
The cost of inaction
- Organizations that run core business processes on old mainframes spend up to 30% more on infrastructure than those leveraging cloud-native platforms.
- The average cost of a data breach has reached $4.88 million, and 70% of them occur in organizations that run their IT using legacy systems.
- 85% of organizations report that time spent maintaining legacy systems hampers their ability to launch new solutions, leading to “innovation paralysis.”
- Organizations using outdated IT tools compared to AI-based solutions experience a 25% drop in efficiency.
- 94% of C-suite executives believe legacy infrastructure significantly hinders their business agility, making it challenging to adapt to market changes.
Final thoughts
An AI-ready infrastructure is the critical link between AI strategy and measurable business outcomes. While investing in this foundation requires resources, the cost of inaction, which is measured in competitive disadvantages, security vulnerabilities and operational inefficiencies, is much higher.
The right infrastructure helps companies not only deploy AI but also support it, scale it and get tangible results from it. Without this foundation, even sophisticated AI models fail to deliver meaningful ROI. In the next article, you’ll find real-world examples of how infrastructure modernization has helped companies globally transform their operations, allowing you to assess your own journey and identify the necessary steps to become an AI-connected business.
