The growing adoption of Artificial Intelligence is driving—and will continue to drive—a new wave of IT infrastructure modernization. This article explores how these two trends are connected, what priorities are emerging among professionals, and offers practical guidance for organizations looking to innovate in a structured and effective way.

Artificial Intelligence: a growing business priority

Artificial Intelligence (AI) is now a priority for 90% of IT and DevOps decision-makers, and 94% expect to increase investment in IT infrastructure modernization to support it.

These insights come from the State of Enterprise AI report by Vanson Bourne and Nutanix, a global study conducted in 2023 that examines how organizations approach AI strategy and implementation.

Building on these findings, we explore the relationship between AI and IT modernization, and provide a practical guide for companies aiming to turn emerging challenges into concrete opportunities.

The impact of AI on the IT landscape

The integration of AI into IT represents a significant shift, enabling a wide range of applications—from automation and predictive analytics to personalized customer experiences and enhanced cybersecurity.

These innovations help reduce costs and improve operational efficiency, while also supporting business growth by enabling more flexible and adaptive operating models.

More importantly, AI strengthens organizations by improving their ability to analyze complex data, make informed decisions and deliver real-time, personalized services—enhancing both customer experience and internal performance.

Addressing the challenges of modernization

Integrating AI into IT infrastructures creates significant opportunities, but also introduces complex challenges. If not managed carefully, these can slow down modernization efforts and limit expected benefits.

AI workloads require continuous and reliable access to data. This means that every AI-driven process depends on a data strategy capable of ensuring secure and high-performance access to information. As highlighted in the State of Enterprise AI report, this requirement is reshaping how organizations design their data infrastructure in two key ways:

  • Infrastructure readiness: 91% of respondents agree that their IT infrastructure needs to evolve to effectively support AI workloads, particularly in areas such as security, resilience and scalability. Additionally, 85% expect to increase investments in this area over the next one to three years.
  • Data mobility: 93% emphasize the importance of a strong edge strategy, as data increasingly moves across data centers, cloud and edge environments. Many organizations plan to train AI models in the public cloud, refine them in private environments and deploy them at the edge. To support this, 83% expect to increase investment in edge strategies in the coming years.

Other common challenges include ensuring data quality, managing privacy and security, and addressing organizational resistance to change. A holistic approach is essential—one that combines strong data governance, robust security policies and, where needed, the support of experienced technology partners.

A guide to IT modernization through Artificial Intelligence

In a previous article, we outlined the key elements required to successfully launch an IT modernization initiative. The question now is how AI fits into this process—and how to integrate emerging technologies into existing operations.

The following framework provides a clear path for organizations moving in this direction. From assessing business needs to planning infrastructure investments, from building skills to implementing and monitoring AI solutions, each step plays a critical role.

Assessment and planning

  • Assess business needs: start with a clear understanding of operational and strategic priorities. Identify which processes can benefit most from AI, such as automating repetitive tasks or improving decision-making through predictive analytics.
  • Define objectives: objectives should be SMART—Specific, Measurable, Achievable, Relevant and Time-bound—and aligned with the broader business strategy. This ensures clarity and makes outcomes easier to measure.
  • Evaluate the current IT landscape: assess existing infrastructure to identify gaps that may hinder AI adoption, including hardware, software, networks and storage systems.

Training and skills development

  • Upskill existing teams: training should cover both technical AI capabilities and practical applications within business processes. Tailored programs can accelerate adoption across the organization.
  • Bring in specialized expertise: in some cases, hiring new talent—such as data scientists, machine learning engineers or AI ethics specialists—may be necessary to support more advanced initiatives.

AI development and integration

  • Select the right technologies: choose platforms and tools that not only meet technical requirements but also integrate well with existing systems and offer long-term support.
  • Start with pilot projects: testing AI solutions in controlled environments allows organizations to validate results, reduce risks and refine their approach before scaling.

Implementation and monitoring

  • Adopt a phased approach: gradual implementation helps manage organizational change and allows continuous optimization based on feedback.
  • Monitor performance continuously: ongoing evaluation ensures that AI solutions remain aligned with business objectives and deliver expected value.

Governance and security

  • Establish strong data governance: ensure data is managed responsibly and in compliance with regulations, with clear rules on access and usage.
  • Implement robust security measures: protect data through encryption, multi-factor authentication and other security controls.
  • Ensure regulatory compliance: aligning with data privacy and security regulations reduces legal risk and strengthens trust.

Conclusion

Artificial Intelligence has firmly entered the mainstream, but its adoption in business contexts is still evolving. Organizations are still identifying the most relevant use cases, defining strategies and evaluating investment priorities.

What is already clear is that effective AI adoption depends on the ability to manage data seamlessly across data centers, cloud and edge environments. This requires not only technological adjustments but often organizational change, along with investments in skills and external support.

There is no single path to success, but a structured approach can make the difference. The steps outlined in this guide provide a practical foundation for organizations looking to move forward with clarity and control.

CONTACT our team to approach your IT modernization and innovation initiatives with a structured and pragmatic approach.