By 2027, 40% of GenAI solutions to be multimodal predicted by Gartner
By MYBRANDBOOK
Gartner predicts that by 2027, 40% of generative AI (GenAI) solutions—up from 1% in 2023—will be multimodal. The transition from single-mode to multimodal models offers improved AI-human interaction as well as a chance to distinguish GenAI-enabled products. Multimodal GenAI is one of two technologies identified in the 2024 Gartner Hype Cycle for Generative AI.
Erick Brethenoux, Distinguished VP Analyst at Gartner, said, “As the GenAI market evolves towards models natively trained on more than one modality, this helps capture relationships between different data streams and has the potential to scale the benefits of GenAI across all data types and applications. It also allows AI to support humans in performing more tasks, regardless of the environment.”
Along with open-source large language models (LLMs), both technologies have high impact potential on organizations within the next five years.
Among the GenAI innovations Gartner expects will reach mainstream adoption within 10 years, two technologies have been identified as offering the highest potential – domain-specific GenAI models and autonomous agents.
Multimodal GenAI
Multimodal GenAI will have a transformational impact on enterprise applications by enabling the addition of new features and functionality otherwise unachievable. The impact is not limited to specific industries or use cases, and can be applied at any touchpoint between AI and humans. Today, many multimodal models are limited to two or three modalities, though this will increase over the next few years to include more.
Open-source LLMs
Open-source LLMs are deep-learning foundation models that accelerate enterprise value from the implementation of GenAI, by democratizing commercial access and allowing developers to optimize models for specific tasks and use cases. Additionally, they provide access to developer communities in enterprises, academia and other research roles that are working toward common goals to improve and make the models more valuable.
Domain-specific GenAI models
Domain-specific GenAI models are optimized for the needs of specific industries, business functions or tasks. They can improve use-case alignment within the enterprise, while delivering improved accuracy, security and privacy, as well as better contextualized answers. This reduces the need for advanced prompt engineering compared with general-purpose models and can lower hallucination risks through targeted training.
Autonomous agents
Autonomous agents are combined systems that achieve defined goals without human intervention. They use a variety of AI techniques to identify patterns in their environment, make decisions, invoke a sequence of actions and generate outputs. These agents have the potential to learn from their environment and improve over time, enabling them to handle complex tasks.
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