Artificial intelligence (AI) has become a key driver of transformation and business growth for enterprises. As more and more companies adopt AI, CIOs are shifting their focus from the "why" to the "how" of AI implementation. How can they quickly and economically scale AI production to create value and drive business growth?
One solution to this challenge is high-performance AI infrastructure. Purpose-built platforms and clouds with optimized processors, accelerators, networks, storage, and software offer a powerful way to cost-effectively manage and accelerate the growth and "industrialization" of production AI. By standardizing on a public cloud-based, accelerated "AI-first" platform, businesses can quickly build and deploy high-performing AI applications while also managing expenses and keeping internal resources focused on data science and AI.
There are three major requirements for accelerating AI growth: standardization, cost management, and governance. Standardizing on optimized stacks, pre-integrated platforms, and cloud environments helps to avoid the negatives associated with a chaotic variety of products and services, such as unmanaged procurement, suboptimal development and model performance, duplicated efforts, and lack of specialist personnel.
Cost management is also essential for maximizing AI growth. Unauthorized spending, often by business groups, can add 30-50% to technology budgets. By centralizing procurement and provisioning of enterprise-standard AI services, companies can restore institutional control and discipline, while also providing flexibility for organizational consumers.
Finally, governance is crucial for ensuring that AI is used responsibly and ethically. By establishing clear policies and procedures, companies can ensure that their AI systems are transparent, accountable, and aligned with their values and goals.
Overall, high-performance AI infrastructure is a vital component for maximizing AI growth and driving business success. By standardizing, managing costs, and ensuring governance, companies can ensure that their AI systems are robust, cost-effective, and aligned with their overall goals.