AI at scale depends on cloud at scale because cloud effectively becomes the engine for AI, innovation and growth. Without a modern cloud foundation, it’s difficult to access the compute power, data, and flexibility that AI needs.

What we’re seeing in the market:

• Tech budgets are shifting: A significant share of front-runners’ technology budgets is now directed to cloud and AI, reflecting how tightly the two are linked.
• Cloud returns are mixed: Only 42% of companies say they’re achieving the returns they expected from cloud. This suggests that simply “moving to cloud” isn’t enough—organizations need a tailored strategy that aligns cloud with business goals and AI ambitions.
• Modernization is a major spend category: Around 25–40% of enterprise cloud spend goes to modernization. This is because coordinated modernization efforts help optimize processes, improve security, spark innovation and drive growth—capabilities that are essential for AI.

In practice, this means reframing cloud from a hosting decision to a strategic platform for AI. When cloud, data and AI are designed together, you can:

• Scale AI models efficiently across the business
• Access and govern data more effectively
• Experiment and innovate faster, with lower risk

Organizations that treat cloud as the backbone of their AI strategy are better positioned to reimagine products, services and operations over time.

Many organizations are in the same position: they’ve moved to cloud but aren’t seeing the full business impact yet. Several recurring barriers show up in the data:

• Outdated applications: About 40% of companies cite legacy or outdated applications as a key barrier to realizing cloud value. These apps can’t easily take advantage of cloud-native capabilities, which limits agility and innovation.
• Security concerns: 41% of companies say security risks are a major barrier to cloud adoption and value. Without a clear cloud security strategy, organizations hesitate to move critical workloads or data.
• Fragmented modernization: While 25–40% of enterprise cloud spend goes to modernization, it’s often not coordinated. When modernization is piecemeal, it’s harder to optimize processes, boost security and drive innovation in a measurable way.
• Data and digital capabilities for gen AI: Only a small share of companies feel extremely confident they have the right data strategies and core digital capabilities to fully leverage generative AI. This gap limits the upside from both cloud and AI.

To improve ROI, companies are focusing on:

• Application and mainframe modernization: Using cloud and gen AI to rethink legacy systems, unlock agility and accelerate innovation.
• Security-by-design: Protecting data, applications and infrastructure with scalable, cost-effective cloud security solutions that support resilience.
• Clear cloud strategy: Moving from generic cloud adoption to a custom strategy that ties cloud investments directly to business outcomes and AI use cases.

By addressing these areas systematically, organizations can move beyond basic migration and start to reimagine how cloud supports growth and differentiation.

As cloud and AI mature, organizations are broadening their focus beyond basic infrastructure to include sovereignty, edge and modern networks. A few trends stand out:

1. Digital and AI sovereignty

• 46% of companies have applied sovereignty to their infrastructure, but only 22% apply it to AI models.
• Those that extend sovereignty across the full stack—from infrastructure to data to AI models—are better positioned to boost resilience while still scaling innovation.
• Sovereign AI is shifting from a pure risk-management topic to a way to strengthen competitiveness and protect cultural and strategic value.

2. Edge computing as an AI enabler

• Users’ smart devices generate data constantly. Processing that data closer to its source at the edge can:
• Improve performance and responsiveness
• Reduce data transfer and processing costs
• Enhance security and privacy
• Provide a better user experience
• Research shows companies are increasingly using edge to evolve enterprise AI and are looking for ways to scale its value across the business.

3. Modern infrastructure and networks

• Traditional infrastructure often can’t keep up with changing business needs. Organizations are moving toward modern setups that integrate compute, network, workplace and data capabilities.
• Companies with outdated networks struggle to meet new demands. Modern, software-driven networks can:
• Improve efficiency and collaboration
• Cut operating costs
• Enhance security
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• Modern infrastructure is increasingly seen as a living foundation that evolves alongside AI innovation, rather than a one-time upgrade.

In practice, leading organizations are designing cloud, sovereignty, edge and networks together as part of a single, modern digital core. This integrated approach helps them reimagine how they deliver experiences, manage risk and scale AI across the enterprise.