Adapting Enterprise Infrastructure in the Age of AI: Why and How - with NVIDIA

Adapting Enterprise Infrastructure in the Age of AI: Why and How - with NVIDIA

Sally Eaves 18/06/2024
Adapting Enterprise Infrastructure in the Age of AI: Why and How - with NVIDIA

The integration of artificial intelligence (AI) into enterprise infrastructure has become a necessity.

Companies must adapt their systems to leverage AI's full potential, enhancing efficiency, decision-making, and innovation.

As AI is steadily delivering on the promise to boost efficiency, productivity and increasingly - sustainability - businesses are seeking to seize the 'AI Advantage' – from improved decision-making to automating tasks and rapidly heralding new business models, revenue streams and impact outcomes onto the horizon.

These benefits, along with industry-specific enhancements in finance, retail and pharmaceuticals as just 3 sector examples are fast becoming core to maintaining or gaining competitive advantage. And actualizing this? Well that increasingly necessitates constructing a scalable enterprise infrastructure that is fit for purpose to support Artificial Intelligence. Nvidia recently published a timely guide: 'Evolving Your Infrastructure for AI: Top Considerations for IT Leaders'

See 🔗 📌

But why does AI require this? What can enterprises do to evolve their internal IT to align with the new era of AI? Moreover in respect to Generative AI too with its 'easy-to-consume' applications such as ChatGPT, Stable Diffusion, DALL-E ... the list goes on... so does the implications! Let's explore this in more detail now.

AI is Becoming a Core Competency

Recognizing AI as a core competency means making a move beyond the adoption of AI technologies to also adjust broader IT infrastructure 'to meet' AI. From investing in AI Research and Development, right through to cultivating a skilled workforce proficient in AI – and fostering a culture of innovation.

Large enterprises have the scale to make these investments – but it requires a sensible action plan, including knowing where current enterprise infrastructure might be lacking. But relying entirely on outside vendors/consultants and external knowledge for AI competency is really not an option. This is simply too intrinsic to growth and sustainability.

Much of this new competency must come from within internally, with facilitation from outside vendors/consultants and the fostering of internal and external knowledge exchange. More on why this knowledge context matters so much here from original research. And this also means that enterprise technology infrastructure requires extensive adaptation too!


How Current Enterprise Infrastructure is Lacking

AI thrives on vast amounts of data. Pre-AI enterprise infrastructure is often designed for smaller data-sets and can therefore struggle to store, process and analyse the vast volumes of data pre-requisite for effective AI. The same applies to sheer processing power: running complex AI algorithms requires almost brutal force and existing infrastructure might not be powerful enough to train internal AI models. Recent AI Enterprise Infrastructure research conducted by Forrester also highlights the costly pitfalls from 'Shadow IT' 👤👤

Security is a factor too as AI introduces new cyber security considerations across Data Security and Privacy, Model Security, Operational Security, Ethical and Responsible AI, Incident Response and Recovery and Collaborative Defense.

It is also worth considering how enterprise infrastructure integrates with AI applications: it does not work in isolation ⚙️ AI needs to integrate with existing IT systems for tasks like data exchange and results implementation – and this necessitates modernised infrastructure.

AI applications require a nimble, flexible environment with the ability to scale resources up and down as needed. Traditional, rigid IT systems lack agility: many enterprises rely on a collection of legacy systems that are ill-equipped to communicate with cutting-edge AI tools.

Costly workarounds do remain an option – but I believe a wholesale reimagination of enterprise IT for the AI Age is an optimal approach.

💡Five Ideas for Adapting Enterprise Infrastructure💡

Nvidia is right at the core ;) of providing the raw computing power required to drive the AI age, something reflected in its recent recognition as one of TIME’s Most Influential Companies of 2024. And personally, I believe this success is also aligned to its commitment to couple technological advancement with that of learning advancement too, a great example being this new 🔗guide 🔗packed with useful resources and advice which outlines how enterprises can go about refreshing their IT infrastructure to meet the demands and promises of AI.

The discussion centres around five principles – 1) taking a holistic viewpoint, 2) deciding on cloud modality, 3) the need for investment, 4) a focus on culture, and 5) thinking about budget.

Holistic Infrastructure Strategy

Fast computation is just 1 piece 🧩 of the puzzle – but it’s an important piece. Whether it’s growing existing data centres or buying more capabilities in the cloud, enterprises must ensure they obtain the needed computing power at a cost that makes sense.

Large organisations must also plan for robust storage to handle massive datasets, high-bandwidth networking for rapid data transfer, and a software stack that optimizes model development and deployment.

Nvidia suggests that enterprises consider centralized AI Centers of Excellence to streamline the process of building out infrastructure by providing pre-configured infrastructure, tools, and governance, with the result of both improving ROI - and reducing risk.


Accelerated AI Infrastructure is Key

A core point is the need to invest in specialized hardware such as GPUs or AI-optimized chips for the heavy lifting of model training and inference. Conventional computing power in conventional data centres simply cant do this.

New to the enterprise IT stack would be the needed software: designed for parallel processing and efficient resource utilization. It’s a foundation critical to handling large datasets, iterating quickly on models, and deploying AI solutions at scale. Enterprises should arguably start fresh with AI infrastructure, building AI-optimised computing power from the ground up 🆙

☁️ Cloud, On-Premises, or Hybrid?

There’s a choice to be made around the location of AI infrastructure. Cloud offers scalability, flexibility, and access to cutting-edge AI tools/services, making it ideal for rapid experimentation.

On-premises solutions may be more suited where strict data regulations are a factor, or where long-term cost predictability is paramount: which it may well be given how resource-intensive AI is.

Nvidia also suggests that a hybrid approach balances flexibility and control, allowing you to leverage the cloud for development or bursting, while keeping core operations and sensitive data on-premises.

This is also reflected in the latest AI Enterprise Infrastructure research conducted by Forrester which finds that AI is easier with on-premises infrastructure, with 9 out of 10 enterprise respondents committed to the hybrid cloud.


Build a Skilled AI Team

Imperative skills today include data literacy across the reading, understanding, interpretation and critical evaluation of data and ‘the how’ of both drawing conclusions and communicating data effectively, with specific skills such as prompt engineering growing in prominence. AI Fluency, the skill to 'understand and work with AI' will soon become essential, akin to working with PCs. So how can these needs be best supported?

Reflecting on the new study insights, this necessitates a combination of upskilling existing IT staff through training in AI frameworks, cloud deployment, and data engineering.  If hiring, Nivida suggest companies should prioritize cloud platform expertise, alongside proficiency in DevOps – this helps to ensure the streamlined CI/CD pipelines needed for iterative AI app development -which also reduces risk. Data scientists who understand the nuances of AI model development are also pivotal to successful teams.

The global IT skills shortage makes building a dedicated AI infrastructure challenging – putting this into context,  the latest research from IDC predicts that over 90% of organizations worldwide will feel the pain of the IT skills crisis by 2026, this equates to c. $5.5 trillion in losses caused by product delays, reduced competitiveness, and ultimately loss of business. As an alternative, AI training and certification programs can upskill an existing team: deployment and management solutions can also help.

Balance Budget with Long-Term Goals

Enterprises should never focus solely on upfront investment (TCO); factor in ROI and the ability to scale AI initiatives. Cloud solutions can offer lower initial costs but you should also consider long-term expenses and potential vendor lock-in.

The cloud offers a budget-friendly entry point for AI by reducing upfront costs (CapEx) in favour of ongoing fees (OpEx). However, long-term cloud expenses can rise. Enterprise IT leaders should assess the TCO over time: data storage, compute needs, and maintenance.

Without a doubt, accelerated computing optimizes energy efficiency and cost in the long run, especially for resource-intensive AI workloads – and budgeting is critical.

Opportunity in Transformation

The AI revolution demands a corresponding transformation of enterprise infrastructure – a transformation can enable companies to reap the benefits of improved decision-making, automation, and new business models.

But large organisations in particular must develop in-house AI expertise to ride the AI wave - or more accurately and to borrow a surfing term to 'shred' - and aggressively ride the wave to the fullest. Relying on external vendors and advisors alone won’t make the cut ✂️ – only in-house capabilities will give enterprises the fullest opportunity to gain a decisive advantage and seize the opportunities of our AI-(em)powered future.

And a quick reminder that you can freely access the NVIDIA guide: 'Evolving Your Infrastructure for AI: Top Considerations for IT Leaders' below: 🔗 📌

All feedback most welcome 🗨️

Many thanks! Sally

About the Author

A highly experienced chief technology officer, senator for advanced technologies, and a global strategic advisor on digital transformation, Sally Eaves specialises in the application of emergent technologies, notably AI, 5G, cloud, security, and IoT disciplines, for business and IT transformation, alongside social impact at scale, especially from sustainability and DEI perspectives.

An international keynote speaker and author, Sally was an inaugural recipient of the Frontier Technology and Social Impact award, presented at the United Nations, and has been described as the "torchbearer for ethical tech", founding Aspirational Futures to enhance inclusion, diversity, equity, and belonging in the technology space and beyond. Sally is also the chair for the Global Cyber Trust at GFCYBER.

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Sally Eaves

Tech Expert

Dr. Sally Eaves is a highly experienced Chief Technology Officer, Professor in Advanced Technologies and a Global Strategic Advisor on Digital Transformation specialising in the application of emergent technologies, notably AI, FinTech, Blockchain & 5G disciplines, for business transformation and social impact at scale. An international Keynote Speaker and Author, Sally was an inaugural recipient of the Frontier Technology and Social Impact award, presented at the United Nations in 2018 and has been described as the ‘torchbearer for ethical tech’ founding Aspirational Futures to enhance inclusion, diversity and belonging in the technology space and beyond.

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