How is AI impacting the global economy? Some of the big names in AI - Alphabet, Microsoft, Tesla, Intel Corp and Amazon among them - have seen volatility in share price as returns from tech companies have come into conflict with investors’ high expectations. As the markets figure out where the demand and investment in AI infrastructure lie, companies need to be strategic in how they invest in their own AI infrastructure. 

Whatever happens in the stock market, though, the AI genie is out of the bottle: the technology is simply too powerful to ignore and smart investment in AI infrastructure should be front of mind for CIOs and CTOs seeking to position their organizations for a profitable future. Despite the undeniable potential of AI, however, there are still significant hurdles that remain in accelerating its innovation and widespread adoption within businesses.

In this article and ahead of the upcoming AI Infrastructure & Architecture Summit, we will look at these hurdles and how they can be overcome through fostering robust ecosystems and building a strong foundation of infrastructure.

When Generative AI left the pilot stage and went mainstream

Automation has been a priority for many years, with businesses keen to find more tasks that can be competently performed by robots. As AI has developed, organizations have sought to use the technology to better serve customers, employees and partners, while functions such as sales, marketing, finance, support, operations, IT and product development looked to AI as a route to streamlining and improving internal workflows. GenAI models such as ChatGPT are pushing the boundaries of creative content generation, while deep learning algorithms are revolutionizing fields such as healthcare and finance. Whatever industry you’re in and whatever role you play within it, AI has suddenly opened up fertile ground for new ways of working.

Early adopters quickly found that scalable AI solutions require businesses to accommodate the ingestion, sharing, storage and processing of enormous and diverse data sets while keeping sustainability in mind. While there were clear opportunities, the road was not as straight and smooth as it first looked.

Ecosystems and infrastructures

According to the Equinix 2023 Global Tech Trends Survey (GTTS), 42 percent of IT leaders stated that their existing IT infrastructure is not prepared to accommodate growing AI adoption. Additionally, 41 percent doubted their team’s ability to implement the technology, which means that businesses must tread carefully in utilizing the correct infrastructure where it is most needed, at the right time.

Right now, one of the critical factors hindering AI adoption is the lack of robust ecosystems. A thriving AI ecosystem is a network of interconnected players that includes research institutions, technology providers, data specialists and industry experts. These players collaborate to share knowledge, resources and best practices, accelerating innovation and fostering a sense of community.

A flourishing AI ecosystem fosters several advantages. Firstly, it allows for the cross-pollination of ideas and expertise. Startups with cutting-edge algorithms can partner with established tech giants to bring their innovations to a wider audience. Secondly, AI ecosystems facilitate the development of standardized tools and frameworks. This standardization makes it easier for enterprises to integrate AI into their workflows without having to reinvent the wheel. Finally, active collaboration within an ecosystem accelerates the development of talent. Universities and research institutions can work with industry partners to design AI curriculums that equip future professionals with the necessary skills.

A strong ecosystem alone is essential but not enough to truly unlock the potential of AI. For that, a robust infrastructure is equally crucial. AI infrastructure refers to the hardware, software and networking capabilities that facilitate the development, training and deployment of AI models. Building and maintaining this infrastructure requires significant resources, since it is the tangible backbone that enables AI operations.

While AI ecosystems and infrastructure are distinct, they can and should influence each other. An ecosystem can inform the design and evolution of infrastructure, driving the development of specialized hardware and software tailored to specific AI workloads. Conversely, a robust infrastructure can facilitate collaboration and knowledge sharing within an ecosystem by providing the necessary tools and resources.

Over time, there could be a convergence where ecosystems become more formalized and standardized, taking on elements of infrastructure, while infrastructure becomes more adaptable and responsive to the needs of the ecosystem, creating a symbiotic relationship.

Why AI requires bespoke infrastructure

According to IBM, at the core of AI infrastructure lies the need for immense computational power. Training complex AI models requires processing vast amounts of data, often in real-time. This necessitates the use of high-performance computing (HPC) systems, including powerful CPUs, GPUs (Graphics Processing Units) and sometimes even specialized AI accelerators.

Furthermore, AI infrastructure must be scalable. As AI models grow in complexity and the volume of data increases, the infrastructure needs to be able to adapt and expand to meet these demands. Scalability is crucial for enterprises looking to deploy AI across their entire organization.

Finally, no business should be operating AI without robust data security. AI models are only as good as the data they are trained on. Securing sensitive data throughout the AI lifecycle, from collection to storage to processing, is paramount. Implementing robust data governance practices and employing state-of-the-art security protocols are essential for maintaining trust in AI applications.

As Akash Takyar, CEO at LeewayHertz says: “Deliberate attacks on AI systems can be especially damaging because of the high value and sensitivity of the data involved. Robust cybersecurity measures, including encryption, intrusion detection systems and secure software development practices, are essential in protecting against these threats.”

In summary, AI demands bespoke infrastructure due to its unique computational requirements. Traditional IT infrastructure is ill-equipped to handle the massive datasets and complex algorithms AI necessitates. Specialized hardware like GPUs and TPUs, coupled with optimized software and networking, are essential for training and deploying AI models efficiently. Finally, the dynamic nature of AI workloads necessitates infrastructure that can scale rapidly to meet fluctuating demands, a challenge for conventional systems.

Building and maintaining a robust AI infrastructure

The greatest challenge of building and maintaining a robust AI infrastructure suited to your business is that different enterprises will have varying needs based on the specific AI applications they aim to deploy. While some companies might opt for on-premise solutions with dedicated hardware, others may prefer leveraging the scalability and cost-efficiency of cloud-based AI infrastructure.

There is a growing trend towards hybrid AI infrastructure solutions, which combine on-premise and cloud resources. This approach allows enterprises to benefit from the security and control of on-premise hardware while enjoying the scalability and flexibility of the cloud.

Several industry leaders are actively contributing to the development of AI infrastructure. Dell Technologies, for instance, offers a comprehensive suite of AI-ready hardware and software solutions, including high-performance workstations and servers specifically designed for AI workloads. Similarly, IBM's AI infrastructure solutions focus on providing a secure and scalable platform for developing, training and deploying AI models across hybrid cloud environments.

The path forward in accelerating AI innovation and enterprise adoption is clear. By fostering robust AI ecosystems and investing in building a strong foundation of infrastructure, we can unlock the true potential of this transformative technology.

Looking Ahead: Challenges and Opportunities

Despite the challenges, the opportunities presented by AI are immense. A study by Review of Artificial Intelligence in Education states that AI has the potential to add approximately $13 trillion to the global economy by 2023 and raise the global GDP by 26 percent in the same timeframe. But this will not be evenly distributed to companies operating now: the future will belong to the businesses which overcome the challenges of adopting AI and creatively using it throughout their operations.

We can look ahead to anticipate further challenges that we will need to address as AI continues to evolve. One key concern is the increasing demand for skilled AI professionals. Data scientists, machine learning engineers and AI ethicists are just some of the essential roles that need to be filled to drive innovation and responsible development.

Another crucial challenge is ensuring ethical and responsible AI development and deployment. Questions surrounding bias, fairness and transparency need to be continuously examined and understood. Collaborations within the AI ecosystem, where researchers, policymakers and industry leaders can come together to develop ethical frameworks, are essential for building public trust in AI. It is only through transparency and trust that we will begin to realize the full potential of this exciting technology.

Hear more live at the #AIInfraSummit!

Join us on January 13th-15th, 2025 at Hilton London Syon Park at the #AIInfraSummit where AI engineering leaders and infrastructure experts will come together to redefine how enterprises design, deploy, and scale AI-driven applications. As an attendee, you'll gain firsthand insights from experts on delivering enterprise-scale generative AI ecosystems through a purpose-built, full-stack platform. Learn how to manage AI compute resources effectively, meet AI demands at any scale with infrastructure designed for custom workloads, and stay ahead of the curve by adapting to the evolution of foundational AI models. This is a summit which will enable your teams to stay abreast of enterprise AI deployments and operational excellence. Book your seat online now.