Forrester announces rebranding of AI/ML platform to reflect practicalities of generative AI era

Forrester’s research team has announced a strategic rebranding of their AI and machine learning platform coverage to reflect the convergence of traditional AI/ML platforms with foundation models.

Historical context: Forrester’s decade-long journey in AI research has evolved from focusing on predictive analytics in 2015 to a comprehensive analysis of AI platforms that incorporate foundation models and generative AI capabilities in 2024.

• The initial focus in 2015 was on predictive analytics for customer behavior and decision optimization
• In 2017, the coverage expanded to include machine learning and deep learning technologies
• By 2022, the scope broadened to encompass full-lifecycle AI/ML solutions
• Recent years saw the integration of foundation models and generative AI capabilities into the research framework

Market convergence drivers: The AI landscape is experiencing a significant merger between traditional AI/ML platforms and foundation models, creating a more integrated ecosystem for enterprise AI deployment.

• AI/ML platform providers are expanding their foundation model capabilities across the entire development lifecycle
• Foundation model vendors are adding comprehensive platform features like API integration and agent development tools
• Enterprises typically utilize multiple language models as components of their broader AI infrastructure

Key transformation dimensions: The evolution of AI platforms reflects four fundamental shifts in how organizations develop and deploy AI solutions.

• AI capabilities have expanded from discriminative to generative tasks, enabling content creation and automation
• The transition from task-specific models to foundation models has accelerated development cycles
• Deployment options have diversified from centralized to heterogeneous architectures
• AI systems are moving toward greater autonomy and self-improvement capabilities

Strategic implications: Forrester’s rebranding of its market coverage to “AI Platform” signals a new era in enterprise AI adoption and implementation.

• The consolidation of AI foundation models for languages (AI-FML) into the broader platform category reflects market maturity
• Research will continue to focus on business use cases, key functionalities, and evaluation criteria
• The new framework aims to help enterprises refine their AI adoption strategies

Future outlook: The convergence of AI platforms and foundation models represents a pivotal shift in enterprise AI capabilities, with implications for how organizations will develop, deploy, and manage AI solutions in the coming years. Success will likely depend on how well organizations can integrate these evolving technologies while maintaining flexibility and scalability in their AI infrastructure.