Open-source models running on premises help you manage performance, reduce risk and lower costs. This is enterprise AI.
The modern workplace is hybrid, with most organizations prescribing a split between corporate office and home office locations. As a result, workload allocation has become more distributed.
Data requirements have become more decentralized, as applications are increasingly served by on-premises systems, multiple public clouds, edge networks and other environments. Perhaps nothing typifies this trend more than generative AI workloads, which typically require large amounts of compute processing coupled with fast storage and networking.
As an IT leader, your default instinct may be to build and run your GenAI application, such as a digital assistant, in the public cloud. At first blush, this is natural, as the public cloud helped you rapidly run and scale applications.
Yet GenAI is busting datacenter boundaries, as running data close to compute and storage capabilities offers the best outcomes. A more bespoke approach may be the best choice.
At its core, this is AI for the modern enterprise—or enterprise AI for those who favor brevity.
Why Enterprise AI Matters
Where are you going to run these innovative AI services? You can always host your GenAI services in the public cloud.
However, if you value your proprietary data as one of your key competitive differentiators, you may wish to protect it. As such you might do well to be more prescriptive in your approach to models, techniques and infrastructure.
Consider running an open-source model instance in your datacenter, where you can more closely hold your IP and control what you do with your data. Pre-trained models incorporating retrieval augmented generation help refine results with corporate data and run well on GPU-powered servers behind the corporate firewall.
This closely-held philosophy is catching on and incorporates more modular and minimalist computing trends. Some suggest that the locally owned and operated approach is inevitable as the future of AI-fueled enterprise services increasingly includes agents that make their own decisions about how to run and govern business processes.
This do-it-in-house approach also helps organizations realize greater cost predictability. The cost to run open-source models with RAG allows organizations to save operational costs compared to GenAI services in the public cloud. These are key savings at a time when the cost of inferencing—the ability for models to make decisions—rises over the lifetime of a model.
Security and governance are cornerstones of enterprise AI. Customers with more sensitive IP requirements, such as government agencies, may choose to build sovereign AI, which taps into its nation’s data and infrastructure operated and maintained on premises.
Moreover, models are increasingly being tailored to run on AI PCs, which affords you greater portability for corporate workflows and enables them to extend to the edge of your networks. Such right-sizing helps reduce the latency that can crop up between compute, storage and network operations running over long distances.
Whether your models are running on servers, PCs or even smartphones, your GenAI workloads should be able to scale across different areas of the business, extending from knowledge workers to the edge and, of course, to customers.
Accordingly, your enterprise AI will also feature humans in the loop, vetting GenAI model outputs and ensuring adherence to corporate governance.
Don’t Fall Prey to Inertia
But perhaps you’ve also found yourself at the proverbial fork in the road. Maybe you don’t know what to do or are wary of trusting GenAI (as many organizations do when mulling adopting emerging technology).
As always when exploring new innovation, partner with your business stakeholders to identify use cases that make the most sense. Does that include a digital assistant for retail shoppers, or a digital twin for manufacturing facilities? You and your partners know your business best.
Test, fail and learn before you decide where to place your production investments. As you do recognize that some of your competitors are wrestling with the same challenges and maybe they are foregoing experimentation, or at least considering it, as a result.
Don’t fall into inertia.
You don’t want to miss the opportunity to develop your own GenAI products and services for internal use, or better yet, for customers.
There’s no better time to embrace the enterprise AI practice for operating critical AI applications and services in your datacenter or at the edge—where you can closely control and monitor performance, security and other factors that help you best protect and serve your business.
The enterprise AI is fueled by the Dell AI Factory, a modern approach to helping organizations build AI products and services. The Dell AI Factory helps organizations bring AI to their data with infrastructure.
It also ensures access to the burgeoning ecosystem of AI partners ecosystem while providing professional services, use cases and other tools to help organizations build and run AI solutions with the best performance while honoring their budgets.
Learn more about the Dell AI Factory.