The introduction of generative AI (gen AI) is not any longer an issue of future speculation. Given the big potential it offers, firms are already benefiting from it to streamline operations, increase productivity and pass on these advantages to their customers.
This change brings with it latest challenges. When customers begin implementing AI on-premises, step one is to evaluate whether their data centers are ready: IT infrastructure upgrades include adequate power and cooling, preparing the network to handle large amounts of knowledge, optimization and expanding infrastructure capability and implementing protective measures while enabling scalability. According to a report by the IBM Institute for Business Value (IBM IBV) in collaboration with Oxford EconomicsIn a survey of two,500 executives in 34 countries and 26 industries, 43% of C-level technology executives said their concerns about their technology infrastructure had increased within the last six months attributable to GM AI and that they at the moment are give attention to updating this technology to scale.
Organizations will need to have an implementation strategy in place that helps ensure efficient operations, minimal downtime, and rapid responses to IT needs while addressing regulatory compliance, ethical considerations, and security threats. To reap the advantages of such technological development, it’s important to have a key partner with in-house AI expertise and the flexibility to administer the total lifecycle of this underlying infrastructure.
IBM Technology Lifecycle Services (TLS) offers a comprehensive suite of infrastructure support and services solutions from deployment to retirement, helping organizations optimize their IT infrastructure for availability and resiliency. IBM TLS helps upgrade data centers to AI readiness, leveraging a worldwide supply chain and logistics framework to satisfy the needs of high-intensity AI workloads for IBM products and various original equipment manufacturers (OEMs) at scale. Here are among the biggest challenges data centers can face when running AI workloads and ways IBM TLS addresses them:
1. Managing a fancy AI infrastructure stack with multi-vendor technologies
Today's data centers have change into more complex attributable to the introduction of AI and reliance on technologies from multiple vendors. According to TechTarget Enterprise Strategy Group’s “Navigating the Evolving AI Infrastructure Landscape” report, 30% of organizations expect to deploy AI in hybrid cloud environments, highlighting the necessity for modernized infrastructure and effective connectivity.
Maintaining operational resiliency requires up-to-date infrastructure and proactive risk management, but monitoring various contracts and troubleshooting will be difficult and dear for internal IT staff. IBM TLS expands customers' existing capabilities not only by providing and supporting IBM products (IBM Z, Power and Storage), but in addition by integrating latest, AI-compatible technologies from multiple vendors.
Large language models require significant resources and multiple computers working in parallel in large network cluster configurations. As the backbone of the infrastructure, this network must support scalable architectures with high bandwidth, low latency, and specific optimizations for GPU communications, memory access, and distributed AI tasks. The IDC “2023 AI View” The report notes that the network was the most important infrastructure expense for genetic AI training. accounts for 44%. By offering an integrated, holistic approach focused on resiliency and availability, with specialized teams world wide and strategic partnerships, IBM TLS acts as a one-stop shop for purchasers and a consultant for procurement, planning, deployment, support, optimization and Update data center infrastructure (servers, network, storage and software), facilitating a smooth transition to AI-enabled environments.
As AI creates increasingly complex hurdles for data centers, addressing these issues could also profit from leveraging AI itself. At the forefront of this shift, IBM TLS integrates AI into tools and processes to empower agents and improve the client experience. For a more detailed take a look at how IBM TLS leverages AI, read what Bina Hallman, vp of TLS Support Services for IBM Infrastructure, has to say.
2. Improve resiliency and data protection
Next-generation AI systems based on complex components similar to GPUs, networking and storage may face higher failure rates attributable to intense workloads, and the large amounts of knowledge being processed and shared could also increase vulnerability. Unplanned downtime and potential data breaches are costly to businesses, but proactive support speeds problem resolution and anticipates problems before they occur.
The IBM IBV survey, “The CEO's Guide to Generative AI: Platforms, Data, and Governance,” shows that almost all of them say concerns about data lineage and lineage (61%) and data security (57%) are a barrier for the introduction of Gen AI. To address these challenges, IBM TLS offers solutions similar to IBM Support Insights, which manages a listing of over 3,000 clients and three.5 million IT resources and identifies and alerts over 1.5 million lively vulnerabilities with remediation recommendations. This approach helps maintain the integrity of the AI infrastructure, minimize outages, and support issues attributable to expired contracts. In addition, IBM TLS helps customers delete data from legacy assets and provides media destruction services to make sure cleanup meets the U.S. National Institute of Standards and Technology (NIST) guidelines for media cleanup.
IBM TLS offers premium support levels in Expert Care for IBM products and Multivendor Enterprise Care for some non-IBM products, providing fast repair times for critical issues and providing customers with a dedicated Technical Account Manager (TAM). The TAM is an issue expert (SME) who reviews your complete IT environment, acts as a single point of contact, and focuses on proactive actions and problem resolution to extend the corporate's operational efficiency.
3. Advice on electricity consumption and CO2 emissions
The growing energy demands of knowledge centers resulting from increasing AI integration could lead on to higher operating costs through power consumption and CO2 emissions, counteracting sustainability goals. As reported by the International Energy Agency (IEA) In January, global data center electricity consumption could rise from an estimated 460 TWh in 2022 to over 1,000 TWh in 2026. When introducing AI, sustainability goals must not be ignored The IBM TLS portfolio helps customers make informed decisions by assessing workload requirements and infrastructure utilizationin addition to monitoring electricity consumption and carbon footprint. IBM IT Sustainability Optimization Assessment leverages IBM Turbonomic software, which runs select “what-if” planning scenarios to know data center optimization opportunities and impacts. Following the assessment, customers receive an in depth report with really useful actions, estimated cost reductions, projected energy consumption and carbon footprint improvements to assist them align their AI initiatives with sustainability goals.
When latest obstacles arise, being prepared, anticipating potential problems, and dealing with a trusted and experienced IT support and repair partner can impact the success of AI adoption and ongoing maintenance. For a long time, IBM has followed core principles that support a whole AI solution stack using multi-vendor technologies. No matter where customers are of their journey, IBM is capable of leverage its expertise to supply businesses with infrastructure for AI capabilities, tailored product offerings, comprehensive consulting, technology lifecycle services, and collaboration with our extensive partner ecosystem to support.
Is your infrastructure AI-ready? How we imagine the following generation of support
