GEC NEWS WIREForty percent of generative AI (GenAI) solutions will be multimodal (text, image, audio and video) by 2027, up from 1% in 2023, according to Gartner, Inc. This shift from individual to multimodal models provides an enhanced human-AI interaction and an opportunity for GenAI-enabled offerings to be differentiated.
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.”
Multimodal GenAI is one of two technologies identified in the 2024 Gartner Hype Cycle for Generative AI, where early adoption has potential to lead to notable competitive advantage and time-to-market benefits. 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 (see Figure 1).
Figure 1: Hype Cycle for Generative AI, 2024
Source: Gartner (September 2024)
“Navigating the GenAI ecosystem will continue to be overwhelming for enterprises due to a chaotic and fast-moving ecosystem of technologies and vendors,” said Arun Chandrasekaran, Distinguished VP Analyst at Gartner. “GenAI is in the Trough of Disillusionment with the beginning of industry consolidation. Real benefits will emerge once the hype subsides, with advances in capabilities likely to come at a rapid pace over the next few years.”
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.
“In the real world, people encounter and comprehend information through a combination of different modalities such as audio, visual and sensing,” said Brethenoux. “Multimodal GenAI is important because data is typically multimodal. When single modality models are combined or assembled to support multimodal GenAI applications, it often leads to latency and less accurate results, resulting in a lower quality experience.”
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.
“Open-source LLMs increase innovation potential through customization, better control over privacy and security, model transparency, ability to leverage collaborative development, and potential to reduce vendor lock-in,” said Chandrasekaran. “Ultimately, they offer enterprises smaller models that are easier and less costly to train, and enable business applications and core business processes.”
