Build Your GenAI Strategy on a Rock-Solid Foundation (Model)

AI-FMLs will create efficiencies at scale across domains and work functions. Many leaders are familiar with AI-FMLs’ capabilities for Q&A and summarization, but these models also excel in other domains like data preparation. For example, general-purpose AI-FMLs enable businesses to extract and understand information such as sentiments, topics, and named entities from ingested content. Traditional machine learning models require massive sets of training data, are resource-intensive to build, and don’t scale reliably across multiple domains. AI-FMLs are allowing developers to bypass building their own ML models for these tasks (thus bypassing laborious data preparation work) and to create applications that can more easily adapt to other tasks and domains.

But no single foundation model will support all the needs of an enterprise. There is not currently a do-everything model that can meet the needs of every team within an organization. Tech executives should plan to utilize multiple foundation models, depending upon the data or application workflow. Some tasks may require a high-end model for specific types of summarizations or analysis, but many tasks can be accomplished with models that are smaller or lower-performance on paper.

Weigh cost, power, and domain training. Sometimes cutting-edge models will confer a competitive advantage, but other times, the cost of running them will outweigh the benefits, and older or smaller open-source models will suffice. But don’t simply look at the base model capabilities: An AI-FML may work for many generalized language tasks, but it may also need significant work to align to your use case. Some industries use domain-specific language in very precise ways (think manufacturing or medicine), and general-purpose models may not cut it for applications in those industries.

Vet models based on their ecosystem capabilities. AI-FMLs deployed for business are parts of larger application ecosystems that support accuracy and transparency in model behavior. Capabilities for engineering/testing/validating prompts, developing retrieval-augmented generation (RAG) architectures, and plugging into external applications’ APIs are essential parts of AI-FMLs in genAI applications. Before committing to a model, find out whether it will work with the needs of your technology ecosystem and whether it will connect all your tools effectively.