By now, many business leaders understand how generative AI (GenAI) can dramatically reshape markets and industries and are moving quickly to harness its transformative power. But even as adoption surges, few companies have successfully leveraged the tool to take the lead.
That’s because significant challenges persist in leveraging GenAI’s large language models (LLMs). One is the security and compliance risks inherent to GenAI. To make accurate, data-driven decisions, businesses need to feed LLMs with proprietary information, but this risks exposing sensitive data to unauthorized parties.
Another concern is the skill and resource gap that emerged with the rise of GenAI. From the upfront costs of investments to the talent required to manage and support GenAI, these demands can take a toll on businesses. With these constraints, they must cautiously tread the GenAI line while developing measured strategies for maximizing returns.
That being said, a strategic approach to GenAI is still necessary. Without one to pilot the GenAI journey, projects and business functions that rely on the tool can exceed budgets and outrun its value.
Looking beyond existing infrastructures
For a start, enterprises can leverage new technologies purpose-built for GenAI. As the core of the GenAI revolution, an AI factory will provide businesses with the building blocks for AI models and frameworks for their operations and generate the actionable intelligence and fresh content they need to develop truly cutting-edge AI solutions.
Underpinning this is an AI-optimized infrastructure, the first layer (or the nuts and bolts) of the factory itself. This layer serves as the foundation for enterprises to elevate their GenAI strategy. Take for example the Dell AI Factory with NVIDIA, which is founded on an extensive portfolio that is purpose-built for AI:
- Servers with acceleration and diverse GPU options
- AI PCs and workstations that push the limit on GenAI deployment
- Storage options to maximize data protection and scalability
- Networking solutions for meeting the performance needs of GenAI workloads
- Data management tools for AI that power faster model tuning and results
- Customized subscriptions and as-a-Service solutions
Create customized, GenAI-powered digital assistants
As companies find their feet with a sturdy GenAI foundation, they can look at several use cases beyond just answering questions with standard responses or extracting insights from data. Key to the GenAI roadmap is digital assistants. Unlike traditional chatbots, digital assistants can comprehend natural conversations and context, as well as non-verbal cues. By delivering personalized customer experiences, they can resolve straightforward queries and navigate them through product offerings.
Dell Technologies takes this a step further with a scalable and modular architecture that lets enterprises customize a range of GenAI-powered digital assistants. Known as Dell Validated Design for Digital Assistants, these pre-tested and proven blueprints are based on high-performance Dell and NVIDIA infrastructure, and uses cutting-edge technology like retrieval-augmented generation (RAG) for secure information retrieval and 2D/3D rendering capabilities. They help companies deploy the tool with ease, reducing the time spent on designing, planning, and testing digital assistants.
Delivering enterprise-ready, GenAI-powered code
Like content, code can be automatically generated with prompts, allowing developers to delegate repetitive coding tasks to AI. However, code generation done without oversight, security, or compliance in mind increases the chances of noncompliance as well as intellectual property and copyright risks.
A way to circumvent these is to define guardrails within the GenAI system while standardizing best practices. For instance, organizations can implement ideal code examples and preferred processes into code-writing models. They can also tailor AI-assisted coding solutions to their on-premises environments, offering companies the scalability and flexibility to supercharge the development process.
Making data-driven decisions with synthetic data
Given the often sensitive and regulated nature of data, security and compliance concerns may inhibit many businesses from feeding their information into LLMs. Synthetic data, which are artificially generated but highly realistic, can help them model and investigate new insights, particularly in circumstances where existing data is confidential or insufficient, such as cyberattacks. These can also be used with existing data sets to provide a comprehensive forecast of business needs and opportunities.
Companies that have rewired their infrastructure to optimize their GenAI investments offered a glimpse of what’s possible with GenAI. Taboola, a content recommendation platform, ran an AI solution that enabled them to serve four billion precisely targeted web pages to 500 million unique users daily. This is done by utilizing more than 10,000 Dell PowerEdge servers engineered for AI workloads that require massive amounts of high performance, scalable compute power.
Then there is Northwestern Medicine, which boosted the efficiency of its healthcare delivery. GenAI solution can run multimodal LLMs, powered by a cluster of four Dell PowerEdge XE9680 servers equipped with eight NVIDIA H100 GPUs, and is deployed on premises. As a result, the company improved radiology performance by 40 percent, and enabled predictive, proactive management that elevated the safety, quality and consistency of their healthcare.
Find out more about how the Dell AI Factory can simplify your GenAI journey today here.
