Demystifying AI Starts With Sound Strategy
You're an IT leader overseeing generative AI (GenAI) experiments, some of which could have a material impact on the business. Craft a detailed strategy of how to scale these proof-of-concepts (PoCs) into production. Include recommendations for relevant products and services.
Though that isn't a GenAI prompt, it very well could be. It also happens to describe the progression for many enterprises seeking to expand their GenAI efforts. You've probably experienced this in the two-plus years that GenAI has taken flight. Maybe your IT department is going through it right now.
One of the typical challenges organizations face is scaling GenAI from PoC to production, which entails applying the right technologies properly to achieve business outcomes. This hinges on establishing consensus about strategy followed by crisp execution—challenges that McKinsey says eludes organizations.
All this can seem daunting and mysterious for the uninitiated. Fortunately, Dell and NVIDIA have got you covered with this product roll-out guide, which describes solutions and services that can help you scale AI.
Start with Strategy. As the foundation for AI success, strategy requires aligning stakeholders from IT and other lines of business and building a roadmap around targeted outcomes. Once stakeholders are aligned, you'll identify and evaluate use cases designed to improve employee productivity, drive operational efficiency and/or increase customer satisfaction. Prioritizing initiatives based on feasibility, complexity, availability of data and ROI helps organizations concentrate on what will drive the most value. Data-driven frameworks and clear roadmaps reduce uncertainty, while governance will help you sustain progress as the strategy evolves.
Evaluate Your Technology. Choosing the right large-language model (LLM) and supporting infrastructure has outsized ramifications. The right model can drive efficiency, accuracy, and business impact; a model that is misaligned with your use cases risks inefficiencies, wasted resources or suboptimal outcomes. Evaluate your options, starting with the models needed to support targeted use cases, as well as techniques that refine results.
Do you run an open source on-premises or opt for a proprietary model connected via API? Pre-trained or custom for your environment? Will you use retrieval-augmented generation (RAG) to fuel your model with more contextually relevant results? Testing models against real or synthetic datasets allows organizations to assess performance metrics such as precision, recall, and latency. Meanwhile, IT must align accelerated computing infrastructure with model requirements. While IT should collaborate with business stakeholders on these decisions, its technical acumen makes it the ultimate arbiter.
Building: From PoC to Production. You've laid the foundation, mapping out the strategy and evaluating the technology requirements. Now it's time to develop and deploy. As you build, ensure that AI PoCs migrate into enterprise applications capable of delivering prescribed outcomes with resilience and reliability while respecting corporate compliance guardrails for responsible AI and regulations. As you progress, test your AI applications to ensure that they can handle real-world data volumes, integrate into existing systems and deliver consistent value.
Ensure that AI solutions are regularly refined, incorporating feedback to meet changing business demands, as well as to improve performance and results. This means fine-tuning models, adapting deployment patterns and making certain the system remains aligned with organizational priorities and emerging challenges. You'll update your models and add new features to remain relevant and accommodate shifting business priorities.
Pursuing a phased approach to deploying AI will help you demystify the complexities of enterprise AI adoption, en route to achieving your targeted business outcomes.
This isn't a light lift, but trusted advisors can help organizations progress from their 'as-is' state to their 'to-be' future, positioning them for sustainable success.
Dell Technologies and NVIDIA can help you leverage AI to drive innovation and achieve your business goals. The Dell AI Factory with NVIDIA delivers capabilities to accelerate your AI-powered use cases, integrate your data and workflows and enable you to design your own AI journey for repeatable, scalable outcomes.
The Dell AI Factory with NVIDIA pairs best-in-class accelerated computing, software and networking technology with Dell servers, storage and professional services. See details on the components of this end-to-end solution here.
A sound AI strategy is a good start but it's just the beginning of your AI journey. What your organization does to execute that strategy is pivotal for successful AI deployments.
Learn more about the Dell AI Factory with NVIDIA.
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