The growth of artificial intelligence (AI) is exploding, and IT organizations are urgently seeking to modernize and expand their data centers to accommodate the latest AI-enabled applications to have a profound impact on enterprise business. It’s a race against time. According to the latest Cisco AI Readiness Index, 51% of companies say not having up to a year to deploy their AI strategy will have a negative impact on their business.
AI is already changing the way companies do business.
The rapid rise of generative AI over the past 18 months is already transforming the way businesses operate across nearly every industry. For example, in healthcare, AI can make medical information more accessible to patients, help doctors diagnose patients more quickly and accurately, and provide healthcare teams with the data and insights they need to provide the highest quality of care. do. In the retail sector, AI is helping companies maintain inventory levels, personalize interactions with customers, and reduce costs through optimized logistics.
Manufacturers are leveraging AI to automate complex tasks, improve manufacturing yields, and reduce production downtime, while financial services are using AI to provide personalized financial guidance, improve customer care, transform branches into experience centers, and more. there is. State and local governments are also beneficiaries of AI innovation, leveraging it to improve citizen services and enable more effective data-driven policymaking.
Overcome complexity and other key deployment barriers
The promise of AI is clear, but the path forward for many organizations is not. Companies face serious challenges in improving their readiness. These include a lack of talent with the right skills, concerns about cybersecurity risks posed by AI workloads, and the lead times it takes to procure the required technology, data silos, and data spread across multiple geographic jurisdictions. There is work to be done to take advantage of the AI opportunity, and one of the first orders of business is overcoming several critical deployment barriers.
Uncertainty is one such barrier. This is especially true for those who are figuring out what role AI will play in their operations. But waiting to have all the answers before starting to make necessary infrastructure changes means falling further behind the competition. That’s why it’s important to start building your infrastructure concurrently with your AI strategic planning activities. Evaluating AI-optimized infrastructure in terms of accelerated compute performance, high-performance storage, and reliable 800G networking is essential, and leveraging a modular design from the beginning provides the flexibility to adapt as these plans evolve.
AI infrastructure is inherently complex, which is another common deployment barrier for many IT organizations. Although 93% of companies recognize that AI will increase their infrastructure workloads, less than one-third (32%) of respondents report that they are prepared to apply, deploy, and make the most of AI technologies from a data perspective. Compounding this complexity is the ongoing shortage of AI-related IT skills, which will make data center operations even more difficult. According to the AI Readiness Index, nearly half (48%) of respondents say their organization is somewhat well-resourced with the right level of internal talent to manage a successful AI deployment.
Adopting a platform approach based on open standards can fundamentally simplify AI deployments and data center operations by automating many AI-related tasks that must be performed manually by highly skilled and often scarce resources. These platforms also offer a variety of sophisticated tools purpose-built for data center operations and monitoring to reduce errors and improve operational efficiency.
Achieving sustainability is critical to our bottom line.
As organizations evolve their data centers to handle new AI workloads, and the computing power required to handle them grows exponentially, sustainability is another enormous challenge to overcome. While renewable energy sources and innovative cooling measures play a key role in curbing energy use, building the right AI-enabled data center infrastructure is critical. This includes energy-efficient hardware and processes, as well as tools purpose-built to measure and monitor energy usage. As AI workloads continue to grow in complexity, achieving sustainability will be critical to revenue, customers, and regulators.
Cisco is actively working to lower the barriers to AI adoption in the data center using a platform approach that addresses complexity and technical challenges while monitoring and optimizing energy usage. Learn how Cisco AI-Driven Infrastructure for Data Centers can help organizations build the AI data centers of the future.
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