Growth in artificial intelligence (AI) is surging, and IT organizations are urgently looking to modernize and scale their data centers to accommodate the newest wave of AI-capable applications to make a profound impact on their companies’ business. It’s a race against time. In the latest Cisco AI Readiness Index, 51 percent of companies say they have a maximum of one year to deploy their AI strategy or else it will have a negative impact on their business.
AI is already transforming how businesses do business
The rapid rise of generative AI over the last 18 months is already transforming the way businesses operate across virtually every industry. In healthcare, for example, AI is making it easier for patients to access medical information, helping physicians diagnose patients faster and with greater accuracy and giving medical teams the data and insights they need to provide the best quality of care. 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 in financial services, AI is enabling personalized financial guidance, improving client care, and transforming branches into experience centers. State and local governments are also beneficiaries of innovation in AI, leveraging it to improve citizen services and enable more effective, data-driven policy making.
Overcoming complexity and other key deployment barriers
While the promise of AI is clear, the path forward for many organizations is not. Businesses face significant challenges on the road to improving their readiness. These include lack of talent with the right skills, concerns over cybersecurity risks posed by AI workloads, long lead times to procure required technology, data silos, and data spread across multiple geographical jurisdictions. There’s work to do to capitalize on the AI opportunity, and one of the first orders of business is to overcome a number of significant deployment barriers.
Uncertainty is one such barrier, especially for those still figuring out what role AI will play in their operations. But waiting to have all the answers before getting started on the required infrastructure changes means falling further behind the competition. That’s why it’s critical to begin putting the infrastructure in place now in parallel with AI strategy planning activities. Evaluating infrastructure that is optimized for AI in terms of accelerated computing power, performance storage, and 800G reliable networking is a must, and leveraging modular designs from the outset provides the flexibility to adapt accordingly as these plans evolve.
AI infrastructure is also inherently complex, which is another common deployment barrier for many IT organizations. While 93 percent of businesses are aware that AI will increase infrastructure workloads, less than a third (32%) of respondents report high readiness from a data perspective to adapt, deploy, and fully leverage, AI technologies. Further compounding this complexity is an ongoing shortage of AI-specific IT skills, which will make data center operations that much more challenging. The AI Readiness Index reveals that close to half (48%) of respondents say their organization is only moderately well-resourced with the right level of in-house talent to manage successful AI deployment.
Adopting a platform approach based on open standards can radically simplify AI deployments and data center operations by automating many AI-specific tasks that would otherwise need to be done manually by highly skilled and often scarce resources. These platforms also offer a variety of sophisticated tools that are purpose-built for data center operations and monitoring, which reduce errors and improve operational efficiency.
Achieving sustainability is vitally important for the bottom line
Sustainability is another massive challenge to overcome, as organizations evolve their data centers to handle new AI workloads and the compute power needed to handle them continues to grow exponentially. While renewable energy sources and innovative cooling measures will play a part in keeping energy usage in check, building the right AI-capable data center infrastructure is critical. This includes energy-efficient hardware and processes, but also the right purpose-built tools for measuring and monitoring energy usage. As AI workloads continue to become more complex, achieving sustainability will be vitally important to the bottom line, customers, and regulatory agencies.
Cisco actively works to lower the barriers to AI adoption in the data center using a platform approach that addresses complexity and skills challenges while helping monitor and optimize energy usage. Discover how Cisco AI-Native Infrastructure for Data Center can help your organization build your AI data center of the future.
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