The integration of artificial intelligence (AI) in business operations is no longer a futuristic concept; it is a current necessity. CEO of Nvidia Jensen Huang introduced a new concept to the rapidly evolving AI landscape during his speech at the GTC conference in March this year. He discussed the rise of “AI factories” and “AI foundries,” terms traditionally associated with product development and raw material processing.
By extending these industrial concepts to AIHuang proposed a new approach to innovation, which could revolutionize software development, resource management and business operations in general. Companies that are already integrating or considering integrating AI into their workflows should carefully consider this approach to improve business value. By leveraging AI, businesses can increase productivity, optimize operations and generate significant value, ushering in a new era of innovation and growth.
Product Manager at Hitachi Vantara.
Prepare your business for GenAI integration
GenAI is quickly becoming a key productivity tool for many organizations. EY analysis suggests that GenAI systems are expected to permeate large segments of business operations in the coming years, with significant implications for various activities such as customer support, marketing and sales, business operations and software programming. GenAI is already making significant progress in the field of customer service, where its ability to mimic human interactions allows businesses to provide rapid, personalized support and interact with customers in real time. Additionally, businesses are beginning to integrate AI and machine learning (ML) into their software, leveraging GenAI’s potential to improve decision-making through a deep understanding of customer needs and use cases, rather than relying on simplistic problem-solving methods.
For businesses looking to expand their use of AI and ML-enhanced software, have the right IT infrastructure is essential. This infrastructure must be robust and flexible enough to meet the growing demands of GenAI and the improvements it offers. In today’s highly digital world, upgrading and modernizing IT infrastructure is more important than ever and can be supported by the right partners.
Partnership for winning AI initiatives
Implementing AI and GenAI in your business is no easy feat. To effectively leverage these technologies, businesses need essential hardware and software components, which requires tightly integrated processes across the product lifecycle and overall business operations.
Another important consideration is ensuring that the company and its partners adhere to governance and compliance standards. This includes applying best practices that align with the company’s AI deployment model, covering areas such as material selection, manufacturing processes, software design and solution delivery . This is particularly crucial for GenAI, which requires significant calculations and storage resources and, if not managed properly, can result in high computational costs, increased energy consumption and a larger carbon footprint.
A key aspect of deploying GenAI applications is the considerable power they require. AI foundries and factories that support these applications require extensive compute, storage, and networking resources to manage large data sets and maintain these models. Organizations must also choose optimized methods to deliver services efficiently while keeping sustainability in mind.
Navigating the GenAI Landscape
When approaching AI as a workload or suite of workloads, it is important to realize that GenAI brings different requirements compared to traditional computing scenarios. To succeed in the GenAI space, companies must adapt their infrastructure strategies to accommodate these new workloads, which can be a difficult process.
An important part of this infrastructure must reside in the cloudbut on-premises systems will also play an important role. Companies must therefore carefully select and build systems suitable for cloud and on-premises environments. This can be facilitated by partnering with experts in critical infrastructure deployment and management. These partnerships are essential to achieving the best results from GenAI initiatives, both today and in the future.
It is important to remember that optimizing GenAI is a gradual process and not something that can be achieved overnight. To succeed, businesses must focus on streamlined infrastructure and automation solutions and collaborate with partners who can support them throughout the process. This includes data preparation, consolidation, and AI model training and inference, each of which has unique infrastructure requirements. Building relationships with trusted partners with experience in a specific business area and data-centric workflows is also crucial for success.
The way forward
All AI and GenAI applications starting with the data, it is therefore essential for organizations to use the most relevant and comprehensive data sets and ensure their data infrastructure is secure and accessible. The journey to becoming an AI-driven company is both challenging and rewarding. By adopting AI and GenAI technologies, businesses can achieve new levels of productivity and innovation.
However, success requires a robust IT infrastructure, strategic partnerships and a commitment to governance and compliance. As organizations navigate the complexities of AI implementation, they must prioritize data integrity, sustainability, and continuous optimization. With the right approach and support, businesses can harness the full potential of AI, create unique offerings and drive sustained growth in an increasingly digital world.
We have presented the best AI phone.
This article was produced as part of TechRadarPro’s Expert Insights channel, where we feature the best and brightest minds in today’s technology industry. The views expressed here are those of the author and are not necessarily those of TechRadarPro or Future plc. If you would like to contribute, find out more here: https://www.techradar.com/news/submit-your-story-to-techradar-pro
