Build Your GenAI Strategy on a Rock-Solid Foundation (Model)
- By Forrester analysts
- April 24, 2024
OpenAI’s ChatGPT set the record for the fastest-growing consumer application, and there are now scores of other models similar to GPT-3.5 available (both proprietary and open-source), but don’t be fooled: The market for foundation models powering generative AI (genAI) and predictive AI is still in its infancy. For any language-related genAI task—writing code, supporting customer service, or creating ad copy—enterprises are relying on what Forrester calls AI foundation models for language (AI-FMLs). These are the pre-trained (typically) large language models (LLMs) that can ingest and generate text, though multimodal models, which can also ingest and produce audio, images, or video, have crested the horizon. This market is evolving and changing quickly, and tech leaders must understand how to navigate it.
Next-generation AI applications will be built on foundation models, but …
Foundation models are the bedrock of genAI-powered applications, and there will be many models (large and small) targeted at different parts of data pipelines and workflows. Forrester clients can learn more about key concepts related to foundation models in our report, The Technology Leader’s Primer For AI Foundation Models, but all readers searching for AI-FMLs with which to build their applications need to know that:
- 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 training data sets, are resource-intensive to build, and don’t scale reliably across multiple domains. AI-FMLs allow developers to bypass building their own ML models for these tasks (thus bypassing laborious data preparation work) and create applications that can more easily adapt to other tasks and domains.
- However, 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.
Enterprises must select foundation models carefully.
For the foreseeable future, most enterprises will source their foundation models from third parties and not pre-train their own. Forrester clients can start building their AI-FML purchasing strategy using our new report, The AI Foundation Models For Language Landscape, Q2 2024, which includes information on how AI-FML vendors differ in offerings, size, and market focus. When choosing an AI-FML, enterprises must:
- 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 with 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.
We will release a Forrester Wave™ evaluation covering AI-FML this summer, looking at the leading vendors based on scoring criteria such as data preparation, training tools, and model governance.
The original article is here.
The views and opinions expressed in this article are those of the author and do not necessarily reflect those of CDOTrends. Image credit: iStockphoto/peterschreiber.media
Forrester analysts
Rowan Curran is Forrester’s senior analyst. His research focuses on AI, ML, and data science, looking at challenges and opportunities for technology executives and their teams.
Charlie Dai is Forrester’s vice president and principal analyst. He serves technology executives and offers strategic advice and guidance to Forrester clients in broad areas, including cloud (cloud-native technologies, public cloud, and hybrid cloud management), big data and AI (ML, computer vision, and data management), IoT (IoT device OS and IoT software platforms), blockchain, commercial drones, quantum computing, DevOps, digital process automation, low-code, and open source software.
Mike Gualtieri is Forrester’s vice president and principal analyst. His research focuses on AI technologies, platforms, and practices that enable technology professionals to deliver applications that lead to prescient digital experiences and breakthrough operational efficiency.
Leslie Joseph is Forrester’s principal analyst. He serves technology executives through research and advisory that focuses on enterprise applications, technology platforms, automation, citizen development, and the future of work.
Aaron Suiter is Forrester’s senior research analyst.