AI market evolution: Data and infrastructure transformation through AI

Artificial Intelligence (AI), a term once relegated to science fiction, is now driving an unprecedented revolution in business technology. From nimble start-ups to global powerhouses, businesses are hailing AI as the next frontier of digital transformation.

Nutanix commissioned U.K. research firm Vanson Bourne to survey 650 global IT, DevOps, and Platform Engineering decision-makers on their enterprise AI strategy. The Nutanix State of Enterprise AI Report highlights AI adoption, challenges, and the future of this transformative technology.

1. AI adoption is ubiquitous but nascent

Enthusiasm for AI is strong, with 90% of organizations prioritizing it. However, many face challenges finding the right IT environment and AI applications for their business due to a lack of established frameworks. Currently, enterprises primarily use AI for generative video, text, and image applications, as well as enhancing virtual assistance and customer support. Other key uses include fraud detection, cybersecurity, and image/speech recognition.

Most AI workloads are deployed in private cloud or on-premises environments, driven by data locality and compliance needs. AI applications are evenly distributed across virtual machines and containers, showcasing their adaptability.

2. AI a primary driver in IT modernization and data mobility

AI’s demand for data requires businesses to have a secure and accessible data strategy. The majority (91%) of respondents agree that long-term IT infrastructure modernization is essential to support AI workloads, with 85% planning to increase investment in this area within the next 1-3 years.

Data mobility across data centers, cloud, and edge is essential, but businesses face challenges in adopting edge strategies. However, 93% of respondents recognize the importance of an edge strategy for AI, and 83% plan to increase investments in edge technology over the next one to three years.

While early adopters lead, most enterprises understand the need for infrastructure modernization to support AI. Key challenges include designing and deploying AI infrastructure, with priorities such as data security (53%), resilience and uptime (52%), management at scale (51%), and automation (50%).

3. AI skills remain a concern: investment is coming

As AI evolves, organizations are recognizing the need for new skills and competencies. Over the next one to three years, 84% of businesses plan to increase investments in their data science and engineering teams, with a focus on generative AI, prompt engineering (45%), and data science/data analytics (44%), identified as the top areas requiring more AI expertise.

Additionally, 90% of respondents intend to purchase or leverage existing AI models, including open-source options, when building AI applications, while only 10% plan to develop their own. This allows organizations to maximize resources and accelerate time to market.

4. Sustainability and ESG are not off the AI table

ESG is now a critical business imperative. Survey respondents ranked ESG reporting as a top area needing AI skills development, even above R&D and product development.

Companies are seeking ways to enhance reporting, meet regulatory requirements, and optimize IT operations. Many believe that responsible AI use will help achieve these goals, though they also recognize that the systems powering AI algorithms are resource-intensive themselves.

5. Data security, data quality, and data governance still raise warning bells

Data security remains a top concern. Respondents rank data security as the top concern for AI workloads, followed closely by data quality. Cost, by comparison, ranks a distant 10th. AI applications rely heavily on secure data, models, and infrastructure.

Data governance is also critical, with AI pushing it from an afterthought to a primary focus. Consistent data access, quality, and scalability are essential for AI, emphasizing the need to protect and secure data in any AI initiative.

6. Cost Roadblocks will start to emerge

Early AI adoption often comes with a “honeymoon phase” where costs are overlooked in favor of staying ahead of the curve. However, 90% of respondents already recognize that AI applications will drive up daily IT and cloud expenses.

As budgets tighten, AI will soon face the same financial scrutiny as other IT investments. This highlights the need to justify costs, identify infrastructure options that offer optimal total cost of ownership (TCO), and strategically plan AI investments for sustained value.

Implementing enterprise AI is a long-haul journey

The journey to AI maturity is complex, with no single path or definitive approach to infrastructure decisions. Success will come to enterprises that adopt AI and embed it into their operations, making thoughtful infrastructure choices, investing in talent, and building long-term strategies. As businesses embrace AI, they stand poised for unprecedented innovation and transformation.

Read the full Nutanix State of Enterprise AI Report for valuable insight into AI adoption, challenges, and the future of this transformative technology.