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 simply recognizing AI’s potential and implementing random AI initiatives and achieving measurable results? As part of a digital transformation strategy, AI requires the most reliable, secure, interconnected, and high-performance infrastructure available on the market. It should 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.
AI usage is expected to grow at an annual rate of 36.6% from 2024 to 2030. It confirms that AI is a strategic necessity and a major driver of the global economy, rather than just an option. However, there is still a critical gap between the ambition to adopt AI and the ability to scale these initiatives successfully. The need for a robust technical infrastructure, without which even the most ambitious AI strategies will never reach production and deliver positive, measurable outcomes, is one of the most significant barriers to AI implementation.
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. Similarly, traditional IT infrastructure still performs, but it’s not equipped to handle the high-intensity AI requirements, such as training large language models (LLMs) or processing high-volume, real-time data streams.
In contrast, AI-ready infrastructure is initially designed to run AI applications and evolve in line with emerging technologies and business objectives. 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
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. It 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 requires resources to maintain and keep it 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 provides organizations with valuable insights that enable them to leverage the power of data analytics and drive informed decision-making. Hence, the need for rigorous data governance, data validation and its protection is even more critical 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%). To strike a balance between availability and security, it’s crucial to incorporate strong data protection mechanisms as cornerstone components of your strategy.
Solid and agile foundation for future growth
Traditional IT infrastructure often struggles to keep pace with the dynamic demands of modern businesses, Modern business demands along with evolving innovations are creating such a dynamic environment that traditional IT infrastructure struggles to keep up with it. The scalable and flexible nature of an AI-ready infrastructure, in contrast, 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, measured in competitive disadvantages, security vulnerabilities and operational inefficiencies, is significantly higher.
The proper 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 following 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.
