Architecting It Estates For Maximum Ai Impact
Architecting IT Estates for Maximum AI Impact Drive innovation in a colocated cloud with purpose-built compute Contents Getting started with AI 3 Chapter 1 Designing a purpose-built IT estate for AI 4 Optimizing workloads for impact Chapter 2 Using the right instances and compute environments6 Purpose-built hardware for AI Better together: Azure + Intel Chapter 3 Take the first steps to colocating in Azure 9 Architecting IT Estates for Maximum AI Impact 3 Getting started with AI Today, AI is driving a strategic shift across industries. Organizations see themselves using AI to power innovation and access deeper insights. Unfortunately, many of their current IT estates weren’t designed to handle the demands of AI workloads. Trying to deploy AI on outdated and disparate systems results in performance bottlenecks, wasted investments, inefficient resource usage, and missed opportunities. When organizations fail to modernize their IT estates, they risk falling behind as outdated systems struggle to support the data processing and scalability AI requires. To truly unlock AI’s potential, companies must migrate their infrastructure, databases, and applications to the same cloud—an approach known as colocation. Migration and colocation in the cloud are the first steps to laying a seamless foundation for innovation, allowing organizations to overcome common on-premises challenges. Cost management and resource inefficiencies: Controlling rising costs, including services, hardware, software, and energy consumption, while meeting fluctuating compute demands. Security and compliance risks: Ensuring robust cybersecurity measures and maintaining regulatory compliance across outdated and modern systems. Data silos and integration issues: Breaking down data silos and integrating legacy systems to enable seamless AI adoption and data-driven decision-making. Infrastructure limitations and technical debt: Overcoming the limitations of outdated systems, retiring technical debt, and mitigating disruptions from the end of software or hardware support. Operational inefficiencies and management complexity: Simplifying complex AI environments and reducing high management overhead to improve productivity and agility. Because AI demands substantial computational power and speed, there’s an additional layer of this foundation to consider—the compute environments. Using purpose-built processors and virtual machines can further drive significant AI performance improvements, ensuring AI workloads are optimized for speed and efficiency. Underpinning the entire foundation is robust code-to-code security, which helps ensure compliance and protection against emerging cyber threats. Use this e-book to examine the foundational layers for architecting maximum AI impact, then begin your journey to building a purpose-built IT estate for AI innovation. By using these strategies, your organization will be better positioned to achieve AI success, boost agility, and remain competitive in a rapidly changing market. Architecting IT Estates for Maximum AI Impact 4 Designing a purpose-built IT estate for AI To get the most value from AI initiatives, organizations must first overcome the limitations of outdated IT systems. Migration and colocation are the essential first steps in redesigning your IT estate to drive successful AI outcomes. Migration is about more than moving workloads to the cloud—it’s about building a flexible foundation that scales to meet demand, reduces spending on hardware and maintenance, and provides advanced security across workloads. Colocation is a strategic approach that brings all databases, infrastructure, and applications together in the same cloud, creating a unified environment that simplifies management, promotes collaboration, and ensures seamless integration. This consolidated redesign of your IT estate offers your teams the best setup for AI success by providing dynamic scalability and reducing the complexity of managing multiple, disconnected systems. By colocating in Azure, your organization can better allocate resources, minimize inefficiencies, and reduce operational costs. Colocation also helps ensure seamless access to data and comprehensive security and threat detection, which are crucial for running AI-driven workloads securely.
