The story in 2023 was artificial intelligence (AI). With the emergence of ChatGPT, generative AI has all but taken over the tech world, and the possibilities are incredible. All indicators point to exponential AI growth as we move into 2024.
It’s safe to say that nearly every organization either already has AI in production (in some capacity) or is at least exploring it. Last fall, a survey of executives showed that 55% of organizations were using AI either in production mode or in pilot.1 That was three months ago—a lifetime for AI—and the number is likely higher now.
The pressure to deploy AI is palpable, and it’s coming from the top down. In fact, 97% of leaders said the urgency has increased in their organizations within the last six months.2
Business leaders are eager because the potential business value of AI-powered technology seems endless, from helping increase efficiency and productivity, improving innovation, delivering better customer experiences, and growing revenue to reducing operational and business risk.
Unlocking the full capability of AI, however, takes planning and preparation, and 86% of companies globally are not prepared to fully leverage the technology.3
Key Considerations for AI Infrastructure
AI preparedness takes many forms and includes many areas, such as strategy, infrastructure, data, governance, skills and talent, and organizational readiness. Ultimately, the success or failure of an AI project will depend on each of these factors—particularly in the area of infrastructure.
Where will your machine learning or deep learning model run? Will it be in the cloud or on-premises? Will you need dedicated hardware? Choosing the right infrastructure for AI is one of the most important decisions to make, and there is a wide range of options. There are several things to consider before you decide:
- Performance and scalability
AI requires high-performing, scalable compute and storage. The infrastructure must be capable of handling large amounts of data, with fast I/O speeds, and multiple users simultaneously accessing the model. Approximately 54% of organizations today say their infrastructure is only moderately scalable and not capable of handling complex AI-powered technology without an upgrade.4 - Cost
Whether you choose a cloud platform or build your own AI infrastructure, cost is a factor. Cloud offers unlimited scalability, but there are trade-offs when it comes to managing cloud resources. Building your own infrastructure requires an initial investment, but an optimized infrastructure can contribute to long-term cost savings through TCO and accelerated time to market. - Workloads
What type of AI workloads will you need to support? Before making any decisions on infrastructure, you need to consider the workload—whether it’s the initial model build, large-scale distributed training, or the final production inference—because each has unique demands that will shape your infrastructure design. - Ease of use
Skills and experience of your team may influence your decision. On-premises infrastructure may require more technical expertise to configure and manage, but there are ways to make it easier. Standardizing hardware and software components can reduce compatibility issues and streamline deployment and maintenance. Automation tools can reduce errors and simplify tasks such as infrastructure deployment, maintenance, and management. Additionally, incorporating Machine Learning Operations (MLOps) can help streamline the machine learning lifecycle, from data preparation and model training to deployment and monitoring, by automating repetitive tasks and providing a centralized platform for managing machine learning workflows. AI Advanced Services from ePlus support can provide guidance to help you optimize your investments. - Security
Protecting your most valuable asset—your data—is required for any infrastructure you choose. Bad actors are everywhere, and AI systems will increasingly become prime targets for attacks. Whatever infrastructure you select must offer advanced security protections such as encryption, secure data transfer protocols, and access controls.
Where are you today?
The potential of AI is just coming into focus, but one thing is certain: AI will impact every organization. In fact, 98% of global executives agree AI models will play a significant role in their strategies for the next 3 to 5 years.5
Where are you in the journey? Are you just starting out, exploring AI and developing your strategy? Or, are you already on the road, piloting use cases and refining your data platform? Wherever you find yourself, ePlus has resources to help. Check out www.eplus.com/ai for more information about AI Ignite and our tailored offerings to support you at any stage of your AI journey. And, keep an eye open for upcoming AI blogs in our series.
[1] Gartner Poll Finds 55% of Organizations are in Piloting or Production Mode with Generative AI. https://www.gartner.com/en/newsroom/press-releases/2023-10-03-gartner-poll-finds-55-percent-of-organizations-are-in-piloting-or-production-mode-with-generative-ai
[2] Cisco AI Readiness Index. https://www.cisco.com/c/m/en_us/solutions/ai/readiness-index.html#blade_introduction
[3] Ibid.
[4] Ibid.
[5] A New Era of Generative AI for Everyone. Accenture, March 2023. https://www.accenture.com/content/dam/accenture/final/accenture-com/document/Accenture-A-New-Era-of-Generative-AI-for-Everyone.pdf