If AI investments must be based on long-term business outcomes and values, you must unpack the different facets of AI and their respective computational requirements.
In Preparing the Foundation for the AI of Tomorrow, the authors at ABi Research highlight four key organizing principles for enterprises:
AI Infrastructure Must Be Driven by Business Outcomes: The vision of AI infrastructure
must be based on the intended business outcome of AI deployment. Businesses must first
understand the short- and long-term values AI brings to their operation before designing
the most suitable AI models. When an AI project has a clear business outcome, it has actual
financial values that senior management can recognize.
AI Infrastructure Must Be Heterogenous and Flexible: To unlock the actual value of AI
and yield maximum benefits, scale-up and scale-out of AI applications are critical. Building
an AI infrastructure that offers the proper foundation to support different facets of AI model
design, development, and deployment across different computing platforms goes a long way
to protect and future-proof current investments. A heterogeneous compute platform will offer
the best performance across all AI tasks. AI developers can use the CPU for data gathering and
preparation, before switching to the GPU and ASIC for model training, and finally using either
the GPU, ASIC, or CPU for AI inference workload.
AI Infrastructure Must Be Backward Compatible: All AI infrastructure must be able to
work with existing enterprise solutions. Therefore, setting a versatile, robust, and interoperable
foundation with all existing solutions is a must. Incompatibility risks creating many silos in the
business operation, leading to poorly optimized IT/OT infrastructure and processes.
AI Infrastructure Must Be Open and Secure: Businesses always want to avoid vendor lockin. An AI infrastructure consisting of open hardware and software that can interoperate with
other solutions is significant in ensuring smooth IT/OT processes. At the same time, openness
should not lead to a compromise in security. The AI foundation must feature state-of-the-art
cybersecurity and data protection mechanisms to prevent hacking, protect user data, and
comply with legal requirements.
Beyond these four pillars, ABi also has recommendations for end users driving to introduce AI into their businesses:
- Focus on Solution Providers and Chipset Suppliers That Embrace Openness, Freedom of Choice, Trust, and Security
- Leverage Support From Ecosystem Partners
- Develop a Clear Internal AI Roadmap Based on Business Outcomes
- Get Organizational Buy-In
It is clear that AI is still in its infancy, and building the proper foundation for it is critical for its
future success. Instead of looking at AI from today’s lens, all businesses must have a clear long-term plan. This vision will help them navigate the challenges and technology requirements for AI, helping them make the right decision in investing in the most optimal and future-proof AI
infrastructure. The following section discusses various approaches businesses can take to deploy AI. In addition, it highlights the key features and characteristics businesses must pay attention to when selecting their AI technologies. Learn more here.
I am fascinated by the potential for AI and ML to transform business and society, and occasionally say interesting things about it. My educational background includes a CS degree, AI/ML post-grad work, and AWS certifications. I have served in AI marketing roles at IBM, Lenovo, and now Intel.