The promise of Artificial Intelligence (AI) offers the potential for efficiency, deeper insights, and innovative new services for enterprise users. Yet, for many large organizations, bringing AI into their established, mission-critical environments feels like trying to merge two different universes.

Enterprise and AI: Can the Twain Meet?

The enterprise and AI often operate with different fundamental principles and architectures. On the one hand, enterprises have spent decades building robust, reliable IT infrastructures. Data management within these organizations is a mature discipline with well-defined processes for data governance, quality, storage, and retrieval. Security, confidentiality, and meeting strict data locality requirements such as complying with regulations like GDPR or regional laws are non-negotiable pillars. These environments have historically been built around stable, often CPU-centric architectures designed for predictable workloads, focusing on reliability and long-term support. AI, on the other hand, is a world characterized by blistering speed and constant change. New AI models, algorithms, and techniques emerge daily, often developed and trained on vast, publicly available datasets. These models can be complex and come in open-source and proprietary forms. Their rapid evolution is inherently disruptive to static IT planning. Furthermore, AI training and increasingly, running the model to make predictions, rely heavily on specialized accelerated computing, such as GPUs or other AI accelerators, which is a significant departure from traditional CPU-bound enterprise tasks. Thus, integrating the fast, disruptive, accelerator-dependent nature of AI with the secure, stable, data-governed, CPU-centric reality of the enterprise represents arguably the biggest challenge businesses face in truly leveraging AI at scale.

Unlocking Enterprise AI through Open Ecosystems

Bridging this gap requires a thoughtful, layered approach that addresses the unique needs of the enterprise while embracing the power of AI innovation. For Intel, the approach centers on delivering scalable AI solutions by fostering and leveraging the power of open ecosystems. This involves building across several interconnected layers:

• Open App Frameworks: Enterprises need AI solutions that aren't black boxes or require massive, custom integration projects. This makes open application frameworks a viable choice as they allow for easier integration into existing workflows and provide flexibility.
• Security: Trust is paramount. So, a software ecosystem that ensures secure and responsible AI focuses on software that supports data privacy within AI workflows, provides tools for identifying and mitigating bias, offers explainability where possible, and allows organizations to maintain control and meet compliance requirements. Open software allows for greater transparency and customization for security needs.
• Scalability: Enterprises need confidence that their AI infrastructure can grow with demand and is built on proven, reliable designs. Thus, an infrastructure ecosystem based on scalable and reference-based architecture provides blueprints that accelerate deployment and reduces risk.
• Manageability: AI compute needs to be available where the data is – whether at the edge, in the data center, or even on the client PC. Additionally, for sensitive enterprise data, confidential computing technologies are essential to protect data while it is being processed by AI models. This makes a compute ecosystem that is accessible and confidential critical.

Intel embeds this open, ecosystem-based approach into every relevant product line – from AI-ready PCs to powerful data center platforms. Their goal is to ensure AI can be consistently, securely, and easily deployed into enterprises at scale, while meeting their unique requirements for security, manageability, and total cost of ownership.

Intel Enterprise AI – Open Ecosystem Stack

Crucially, this is not a journey Intel is undertaking alone. Realizing the potential of enterprise AI requires collaboration. Intel is committed to partnering deeply with the entire ecosystem, including independent software vendors (ISVs), system integrators (SIs), developers, and other hardware vendors, to build these integrated, open solutions and bring them successfully to market for customers.

What This Means for SAPinsiders

Integrating AI with complex SAP data is a major enterprise challenge. While the desire to apply AI to gain insights and automate processes within SAP is high, integrating AI with the inherent complexity and sensitivity of SAP data environments remains a primary hurdle. SAP data is often vast, residing in intricate schemas such as within S/4HANA, BW/4HANA, or legacy systems and frequently siloed across different modules or instances. Beyond technical integration, stringent enterprise requirements for data security, privacy, compliance, and ensuring data locality for sensitive business records add significant complexity. In fact, SAPinsider research shows that integrating AI with existing SAP landscapes and achieving data readiness for AI/ML are among the top barriers for SAP users when attempting to adopt AI solutions. Intel platforms provide the enterprise-grade compute foundation for SAP AI. The latest Intel Xeon processors, particularly those with built-in AI acceleration features like Intel Advanced Matrix Extensions (Intel AMX), are optimized for efficient AI inference workloads directly within the data center or at the edge. When combined with the Intel Distribution of OpenVINO toolkit, these platforms allow organizations to deploy AI models that can process and analyze large volumes of SAP-related data quickly and efficiently. Furthermore, Intel's ongoing focus on platform security, including capabilities relevant to confidential computing, helps protect sensitive SAP data during processing. Unlock tangible business value and automation within SAP environments. By successfully integrating AI inference directly onto SAP-accessible data using capable platforms, organizations can move beyond proofs-of-concept to realize concrete business outcomes. This enables use cases such as predicting maintenance needs based on S/4HANA asset data, optimizing supply chain logistics using integrated SCM data, enhancing customer experience through AI analysis of SAP CRM interactions, or automating complex financial closing processes within SAP Finance. Discover the potential of Enterprise AI