Democratizing AI deployments for the Enterprise. Start your Engines.

Artificial Intelligence (AI) has come of age and companies around the world, in every industry, are keen to leverage it as a source of business transformation and competitive advantage. They want to use AI to simplify & automate their workflows, optimize their operations, enhance their product, and transform the way they engage with their customers. Some organizations – those that have hit some key milestones as early adopters of AI – are already using  it to find new avenues for growth, attract investment opportunities, and disrupt industries.

For organizations today,  the question isn’t whether or not they should tap into AI – it’s when and how. And the right answer involves doing two things well: First, organizations must understand the current and future AI use cases that will have the most impact. Second, they need to right-size their investment in AI-related infrastructure while balancing current  business and IT needs, with a view for scalability in the future.

Evaluating use cases for AI is crucial because it enables IT teams to determine the appropriate infrastructure required, whether it’s for model training, fine-tuning, retrieval-augmented generation (RAG), and/or inferencing. Additionally, the infrastructure needs can vary significantly based on the type and size of the AI model — be it large language models (LLMs), computer vision, or predictive models — each with distinct requirements.

Right-sizing the investment based on this decision should be self-evident by now. The competitiveness for supply has driven prices sky high and maintaining a pragmatic and focussed view of your needs is critical to maximizing the return on the investment (ROI) and paving the way to go further down the AI route over time with the approval of all stakeholders involved.

The Formula 1 Dilemma – Where’s ¥our Focus with AI?

When it comes to AI, Cisco has worked with a great variety of organizations around the world, at different stages of their AI journey – and helped each one achieve great results. Our teams have seen organizations lose confidence when they cannot match the capability or investment of organizations like Meta and Google – believing they cannot compete as secondary players. But that’s far from the truth.

Think of the AI deployments at Meta & Google as Formula 1 race cars. Each F1 car requires a specialist engineering team to design and support the car. Getting that F1 car on the track and keeping it operational in a controlled environment is a feat of engineering and comes at a mind-boggling cost.

Most organizations, irrespective of their size, don’t need F1 cars. Yes, race cars are powerful, exhilarating and glamorous – but they’re not what organizations need to run a chatbot, query a logistics or sales promotions model, or turbocharge predictive maintenance solutions. For these functions, organizations need vehicles that inherit the cutting-edge innovation of F1 cars, packaged into something user-friendly and practical for everyday use, on normal roads and at reasonable expense.

This allows them to focus on achieving insights and results quickly, without being bogged down by the upkeep and maintenance required for a more complex AI solution.

Introducing Cisco Nexus HyperFabric for AI

The vehicles that encapsulate the innovation of Formula 1 into something user-friendly and practical for everyday use — symbolic of practical AI deployments for modern organizations — are Cisco’s specialty. Our work with both cutting-edge hyperscalers and enterprise customers across the world has allowed us to map out the best architectures for all kinds of use cases, while keeping future scalability in mind.

Whether it’s through our Unified Computing System (UCS), Nexus AI fabrics built on Ethernet, or our Cisco Nexus HyperFabric for AI solution, we understand how each component can help clients exploring AI and enable them to optimize their deployments for maximum efficiency as well as ROI. Additionally, our Cisco Validated Designs (CVDs) provide comprehensive guides that simplify and streamline the deployment process.

Anyone who has had any experience with building AI capabilities in their own data center knows that it is no easy feat. It’s a high-risk project at the very least. The stakes are high because the investment is high and the risk of introducing new technology siloes and more shadow IT can undo the years of work driving simplification, consolidation, and standardization.

Cisco Nexus HyperFabric takes all of that away. The validated infrastructure design allows organizations to cut through complication and complexity, the management framework provided by Cisco allows the seamless integration of everything. IT teams then have the opportunity to partner with business teams in the organization and focus on what’s really important – getting the use case right, moving from training to inferencing quickly, and reducing the time to value.

Assessing Needs and Maturity on the Journey to AI

According to a recent study, 97% of companies say the urgency to deploy AI-powered technologies increased – however, just 14% of organizations worldwide are fully ready to integrate AI into their businesses.

Getting started with AI is critical, but organization must only invest in infrastructure they need. Not getting it right could cost them dearly. The expertise and deep knowledge at Cisco could make all the difference when it comes to navigating complexity and inching closer to more maturity with AI infrastructure. Cisco’s solutions, including UCS, Nexus, and the Cisco Nexus HyperFabric for AI solution, are designed to support organizations at every stage of their AI journey. Learn more about Cisco’s AI solutions online now.