Gartner Predicts 40% of Generative AI Solutions Will Be Multimodal by 2027

The future of generative AI is poised for a massive transformation, with Gartner predicting that by 2027, 40% of GenAI solutions will be multimodal—integrating text, image, audio, and video. This marks a dramatic leap from the mere 1% of multimodal AI solutions available in 2023. This development was highlighted at the Gartner IT Symposium/Xpo in Gold Coast, Australia, where analysts discussed how multimodal capabilities will revolutionize human-AI interaction, bringing AI closer to replicating the richness of human communication and cognition.

The Rise of Multimodal GenAI

Multimodal GenAI refers to AI models that are trained and operate across various types of data—such as text, images, sound, and video—enabling them to understand and generate responses in more human-like ways. Erick Brethenoux, Distinguished VP Analyst at Gartner, emphasized that this transition is driven by the need to better capture relationships between different data types, creating richer, more cohesive outputs.

In the real world, human understanding is inherently multimodal—we process information through a blend of visual, auditory, and sensory inputs. As GenAI solutions evolve to include more modalities, they will be able to handle increasingly complex tasks that mirror this natural information processing. For instance, future applications could enable seamless translation between audio conversations, written text, and visual context in real time, offering enhanced decision-making tools across industries such as healthcare, customer service, and entertainment.

Currently, most GenAI solutions are limited to two or three modalities, but this is expected to increase in the coming years, enabling AI systems to work with richer datasets and offer more sophisticated features. This shift to multimodal models will allow companies to deploy AI in more environments, enhancing user experiences and delivering insights that are not possible with single-modality models.

Open-Source Large Language Models: Democratizing AI

Another key trend highlighted in Gartner’s predictions is the growing importance of open-source large language models (LLMs). Open-source LLMs allow organizations to access and customize foundational AI models, speeding up development, fostering innovation, and reducing vendor lock-in. By leveraging open-source models, developers and enterprises can tailor GenAI to their specific use cases, while benefiting from a global ecosystem of collaboration and innovation.

Gartner’s Arun Chandrasekaran, Distinguished VP Analyst, noted that open-source LLMs bring advantages in terms of customization, privacy control, and security. Unlike closed, proprietary models, open-source LLMs allow organizations to modify models based on their unique needs and provide greater transparency into the model’s decision-making processes. This reduces dependency on external vendors and encourages rapid experimentation and iteration within enterprises. Moreover, open-source LLMs can be more efficient to train, offering significant cost advantages, especially for smaller organizations or those with niche applications.

Domain-Specific GenAI Models: Precision and Performance

As GenAI continues to evolve, domain-specific models—AI models tailored for specific industries or tasks—are expected to become increasingly critical. Unlike general-purpose models, domain-specific models can be optimized for particular sectors, such as healthcare, finance, legal, or manufacturing. This specialization allows for more accurate and contextually appropriate outputs, improving performance and reducing the risk of “hallucinations”—the generation of incorrect or nonsensical data by AI.

The benefits of domain-specific GenAI models are clear: they enhance accuracy, reduce the need for complex prompt engineering, and provide faster time-to-value. For example, in a financial services context, a domain-specific model could handle regulatory compliance questions with a higher degree of accuracy than a general-purpose model by leveraging targeted training on relevant data sets. Similarly, in healthcare, domain-specific models could improve diagnostic accuracy by focusing on medical terminologies and patient data.

By delivering higher contextual accuracy, these models can reduce operational risks, improve decision-making, and enhance security and privacy. As such, businesses in highly regulated industries are likely to be early adopters of domain-specific GenAI, taking advantage of models that are finely tuned to meet their unique demands.

Autonomous Agents: The Next Frontier of AI Independence

One of the most groundbreaking trends Gartner identified is the rise of autonomous agents—AI systems capable of performing tasks and making decisions independently, without human intervention. These agents leverage a range of AI techniques to observe their environment, learn from it, and adapt their actions accordingly. By identifying patterns, making decisions, and executing a series of actions, autonomous agents have the potential to significantly enhance business efficiency.

Autonomous agents are particularly promising for industries that require continuous, real-time decision-making, such as logistics, finance, and customer service. These agents could automate complex workflows, optimize supply chains, or handle customer inquiries end-to-end, all without requiring human supervision. According to Gartner, this technology will redefine many aspects of business operations, enabling companies to cut costs, improve scalability, and reduce response times.

Brethenoux highlighted that the true value of autonomous agents lies in their ability to not only execute tasks but also learn and improve over time. This continuous learning ability will make autonomous agents more reliable, versatile, and impactful in their respective roles, potentially driving a major shift in workforce structures, from task execution to task supervision and optimization.

Navigating the GenAI Ecosystem: Challenges and Opportunities

Despite the tremendous potential of multimodal AI, open-source LLMs, domain-specific models, and autonomous agents, Gartner warns that navigating the fast-evolving GenAI ecosystem will be challenging. The current market is fragmented, with a wide range of competing technologies and vendors vying for dominance. However, industry consolidation is expected in the coming years, which could simplify the landscape and help enterprises choose the right solutions for their needs.

As the market matures and the initial hype around GenAI subsides, real, tangible benefits will emerge. Enterprises that can navigate this ecosystem effectively, selecting the right tools and technologies, will be well-positioned to capitalize on the transformative potential of AI.