Spotlight

Report:

The Forrester Wave™: AI Foundation Models For Language, Q2 2024

How does Forrester define the AI Foundation Models For Language market in 2024?

AI foundation models for language (AI-FMLs) have become central to enterprise technology strategies, enabling organizations to augment employee knowledge, integrate generative AI into automation processes, and implement transformative use cases. The market is highly dynamic with rapid innovation and choices between hot startups and tech giants. Enterprise buyers must look beyond model performance benchmarks and focus on vendors that offer clearly articulated enterprise-focused roadmaps, configuration and governance tools to reduce hallucinations, respect for IP rights, and infrastructure that can scale with low latency and high availability. Enterprises are likely to need multiple models to satisfy specific use cases, including open-weight model communities.

Key Facts for The Forrester Wave™: AI Foundation Models For Language, Q2 2024 in 2024

How did the AI Foundation Models For Language market evolve in 2024?

What product features are required to be included in this year's evaluation?

What are the common features of top products in the AI Foundation Models For Language space?

No common features specified.

Scope Exclusions

Inclusion Criteria

Vendors must, among other requirements:

Offering Strengths — Relative Weighting

Strategy Strength — Relative Weighting

FAQs

Q: What does this research cover?

A: This research covers a comprehensive evaluation of 10 AI foundation models for language providers using 21 criteria across current offering, strategy, and market presence. The report examines core model capabilities (language understanding, multimodality, context windows, code generation), enterprise tooling (governance, security, application development, model management), training data transparency, IP protections, scalability, and vendor roadmaps. It provides detailed vendor profiles highlighting strengths and weaknesses for Google Gemini, Databricks DBRX, NVIDIA Nemotron, IBM Granite, OpenAI GPT-4, AWS Amazon Titan, Microsoft Phi, Anthropic Claude, Cohere Command, and Mistral AI.

Q: Who should use this research?

A: Enterprise business and technology professionals should use this research to select the right AI foundation model provider for their specific needs. The report helps buyers evaluate vendors beyond just benchmark performance, focusing on enterprise-critical factors like governance capabilities, IP protection, scalability, application development tooling, and strategic roadmaps. Organizations can use the included Excel-based vendor comparison tool to adapt criteria weightings to their individual requirements and make informed decisions about which AI-FML providers align best with their use cases, existing technology investments, and enterprise transformation goals.

Q: What are the mandatory features of vendors included in this market?

A: To be included in this Forrester Wave evaluation, vendors must: 1) Offer a comprehensive, differentiated AI foundation model for language that they principally train and maintain, 2) Provide an AI-FML solution as defined by Forrester, 3) Actively market their solution and compete directly with other AI-FML vendors, 4) Have significant mindshare and interest among Forrester's end-user clients, and 5) Have enterprise customers that have rolled out their AI-FML offering in full production.

Q: What are some reasons for not being included in this report?

A:

  • Lack of a comprehensive, differentiated AI foundation model for language that the vendor principally trains and maintains
  • Solution does not meet Forrester's definition of an AI-FML
  • Insufficient active market participation or direct competition with other AI-FML vendors
  • Low mindshare among Forrester's end-user clients
  • Absence of enterprise customers in full production deployment
  • Vendor declined to participate in or contributed only partially to the evaluation

Q: What should buyers consider when evaluating products in this market?

A:

  • Look for clearly articulated roadmaps fine-tuned to enterprise needs, not just core model performance
  • Evaluate tools for configuring and governing models to reduce hallucinations and align with brand values
  • Ensure vendor respects IP rights and provides transparency about training data
  • Assess infrastructure capability to scale with low latency and maintain high availability
  • Consider need for enterprise tooling including prompt engineering, activity auditing, and deployment control
  • Evaluate multimodal capabilities if use cases extend beyond text
  • Review context window size for knowledge retrieval and research use cases
  • Assess multilingual capabilities if operating in global markets
  • Consider whether open-weight or proprietary models better fit your strategy
  • Evaluate vendor's partner ecosystem and integration capabilities
  • Review contractual assurances and indemnification policies for data usage

Q: How has the AI Foundation Models For Language market evolved in 2024?

A:

  • Shift from core model performance focus to enterprise tooling for effective model usage
  • Expansion of core model capabilities to understand audio and images in the context of language
  • Larger context windows reducing the need for retrieval augmented generation (RAG) in some cases
  • Maturation of tooling for development, governance, and deployment to support multitude of AI applications
  • Growing importance of model alignment to business data, company values, and policies
  • Increased emphasis on IP rights protection and training data transparency
  • Need for low latency and high availability as AI-FMLs become part of enterprise applications
  • Rise of open-weight model communities alongside commercial offerings
  • Demand for multimodal capabilities beyond text-only interactions

Q: What differentiates Strength of Offering vs. Strength of Strategy?

A: Strength of Current Offering (vertical axis) evaluates the vendor's existing AI-FML capabilities including corpus quality, model capabilities, enterprise tooling, and deployment features. Strength of Strategy (horizontal axis) evaluates the vendor's forward-looking plans including vision for enterprise AI transformation, innovation roadmap, partner ecosystem development, pricing models, and supporting services.

Reference

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