Transforming your data center into an AI factory

The industrial landscape is undergoing a transformation. Traditionally, foundries transformed raw materials into basic components, while factories assembled products. However, emerging AI factories and foundries are now taking a new approach to product creation and innovation.

These new creations are poised to fundamentally change software development, resource consumption, and management. This change impacts the way companies operate and the value they deliver.

Unlocking Productivity with Generative AI (GenAI)
Businesses are increasingly using GenAI to increase productivity. Hitachi Vantara, for example, uses GenAI copilots and large language models (LLMs) to support customer service, marketing, and sales teams.

MIT and Stanford research suggests that GenAI can enable customer service agents to resolve 14% more issues per hour. In addition, GenAI has increased productivity by 34% for new and less experienced workers. In Australia, every sector and profession sees potential productivity gains from increased automation, with education, professional services, and financial services set to benefit the most by 2030.¹

GenAI and AI are also changing software development and its functionality. Software engineers can achieve much higher productivity with the help of GenAI.

McKinsey research indicates that with GenAI, developers can document code functionality for maintainability in half the time, write new code in almost half the time, and optimize existing code (code refactoring) in almost two-thirds of the time.

Companies are also integrating AI and machine learning (ML) into their software to enable data-driven decisions based on real-time customer insights and use cases rather than static rules.

Preparing IT infrastructure for the AI ​​era
As organizations adopt GenAI and software with AI and ML built in, they need to make sure their IT infrastructure has the necessary power and agility. This creates an opportunity to work with trusted partners to upgrade and modernize data centers.

It is important to remember that no single entity owns all the hardware and software required for successful AI and GenAI ventures. This requires establishing highly integrated processes across the product lifecycle and business operations.
Companies and key partners must also ensure compliance with all applicable regulations and enforce best practices throughout the supply chain. This includes following proper processes and implementing checks and balances on materials and manufacturing processes.

It also requires adherence to software design best practices and ensuring efficient transport and delivery of solutions. Alignment and tight integration are key, especially in the case of GenAI, which requires significant compute and storage resources and can lead to uncontrolled compute costs, energy consumption and carbon emissions if left unchecked.

Optimized for performance and sustainability
A recent report highlights that a single version of Nvidia’s Blackwell data center chip uses a staggering 1,200 watts of electricity. That’s significantly more than it did just a few years ago.

GenAI, along with the AI ​​foundries and factories that support GenAI applications, are heavily dependent on the computing power, network connectivity, and storage to support massive data sets.

This requires optimization similar to FedEx’s approach to delivery. The company continually optimizes routes and implements measures to ensure on-time delivery while minimizing fuel consumption, costs and carbon footprint.

But optimization goes beyond just efficiency. Sustainability is a growing concern, and companies need to find ways to leverage AI and GenAI while minimizing their environmental impact.

This may mean considering alternative cooling solutions for data centers, using renewable energy sources, or adopting energy-efficient hardware and software configurations.

Building the Right Infrastructure for GenAI Workloads
While GenAI is a new workload (or set of workloads), it is important to understand that it poses unique challenges compared to traditional workloads. Organizations are still learning how to best tune their infrastructure for these new workloads.

Building the right infrastructure—a mix of cloud and on-premises systems—requires extensive analysis and expertise. Collaborating with innovative partners with a proven track record of deploying and managing mission-critical infrastructure will enable organizations to maximize the value of GenAI.

Adopt an iterative process
GenAI optimization is an iterative process and there are no quick fixes. Adopt solutions that streamline infrastructure and automation, and look for partners with capabilities that span from data preparation (including data cleansing and obfuscation) to scalable, flexible, and cost-effective data storage, AI model training, and inference. Partnering with experts with experience in your specific vertical and data-centric workflows is critical to success.

AI and GenAI are fundamentally data-driven. The most important thing is to have the most relevant and complete data, along with the right data infrastructure to locate, secure and protect it. This includes solid data management practices to ensure data quality, security and compliance with relevant regulations.

The Future of AI Factories
The concept of AI factories is still in its early stages, but it has enormous potential to change the way companies operate and create value. By embracing GenAI, optimizing infrastructure, and prioritizing collaboration and sustainability, organizations can position themselves to become leaders in this emerging landscape.

As AI factories and foundries continue to expand, they are likely to play an increasingly important role in driving innovation across industries, shaping the future of business and technology.