As artificial intelligence (AI) adoption accelerates, technology leaders are under growing pressure to turn proof-of-concept into production. Yet too many find themselves stuck in pilot mode, unable to scale or demonstrate measurable outcomes.
The key challenge isn’t modernising AI on-premises. Most enterprises have already recognised that the data centre isn’t designed for modern AI workloads. The real test is operationalising AI at scale, building the infrastructure, data pipelines, and governance frameworks needed to turn experimentation into enterprise value.
Unlike conventional workloads, AI requires elastic compute, high-performance memory, and continuous access to diverse, real-time data. Traditional on-premises environments rarely deliver these capabilities.
“The permeation of AI and agentic workflows across virtually every application is forcing many customers to accelerate their cloud modernisation plans,” says Spencer Martensen, global product lead, SAP on AWS. “Mission-critical business processes running in applications like SAP are prime targets for realising the operational efficiency that AI promises, but achieving that is impractical while they remain on-prem.”
This is a key reason enterprise AI pioneers are moving decisively to cloud. Their goal isn’t just to scale AI; it’s to build smarter, more agile, and more accountable systems from the start, leveraging cloud-based SAP environments on AWS to embed intelligence and governance directly into core business processes.
Foundry’s latest Cloud Computing Study confirms this shift, with 31% of IT decision-makers citing AI and machine learning adoption as the top driver for cloud investment.
Why cloud is the foundation for scalable AI
The practice of migrating mission-critical applications to the cloud over recent decades has reshaped how technology leaders design, deliver, and operationalise AI, turning experimentation into execution and ideas into impact. AI pioneers are investing in cloud-native environments because of their ability to:
- Accelerate delivery: Pre-built services and elastic resources enable rapid prototyping, testing, and deployment, reducing time-to-value.
- Contain costs: Dynamic resource allocation keeps infrastructure spend aligned with actual usage.
- Gain visibility: Built-in observability tools improve visibility into model performance and system health.
- Govern with confidence: Fine-grained controls, audit trails, and policy tools simplify compliance across data and model lifecycles.
These benefits aren’t theoretical. They are already reshaping real-world enterprise strategies, especially for organisations running mission-critical workloads like SAP.
SAP migration: a strategic AI enabler
For enterprises with large SAP estates, migrating to the cloud clears a critical path to AI adoption, removing rigid infrastructure constraints.
“Customers know that to transform their business with agentic AI, they need to modernise their business process backbone,” says Martensen. “Cloud providers such as AWS support that entire journey, from migrating SAP workloads predictably to reimagining core customer experiences with intelligent, cloud-enabled innovation.”
Cloud-native environments also give technology leaders the freedom to experiment. Modern architectures make it possible to test and scale AI workloads alongside SAP systems safely, without disrupting core operations. This enables a fail-fast mentality while maintaining operational stability.
This modernisation is further enabled by the right infrastructure choices. On AWS, instances powered by Intel® Xeon® Scalable processors offer a proven platform for AI-ready performance. Built-in features like Intel® Advanced Matrix Extensions (AMX) help accelerate machine learning training and inference, a key benefit in SAP-centric environments where workload efficiency is critical.
Right workload, right environment
As enterprises modernise their SAP and AI environments, selecting the right infrastructure model becomes critical to balancing performance, cost, and scalability.
“Cloud acts as a service provider that ensures everything required from an AI standpoint is already built into the foundation. That allows organisations to focus on the use cases that really matter,” says Akanksha Bilani, senior global sales director at Intel Corporation.
This is why cloud-based environments are becoming the default choice for enterprises scaling AI. They provide the agility, reliability, and built-in governance needed to operationalise intelligence across core business processes.
The direction of travel is clear. Enterprise AI pioneers aren’t just investing in models; they’re rethinking infrastructure, platforms, and operating models, and turning to cloud as the foundation for scalable, production-ready AI.
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