Multimodal enterprise software adoption to hit 80% by 2030, predicts Gartner

Unlike unimodal AI, multimodal GenAI can process text, images, audio, video, and numerical data simultaneously, enabling enterprise software to interpret complex real-world scenarios and generate deeper insights, such as real-time guidance from video, sensor, and voice inputs combined

Enterprise software is on the cusp of a transformative shift, with 80% of applications expected to become multimodal by 2030, up from less than 10% in 2024, according to research firm Gartner. The driving force behind this evolution is the rapid development of multimodal generative AI (GenAI), which enables applications to process and respond to a variety of data types in a unified and intelligent manner.

Unlike traditional unimodal AI systems that handle one type of data at a time, multimodal GenAI can simultaneously interpret text, images, audio, video, and numerical inputs. This allows software to engage with real-world complexity more naturally, offering deeper insights and improved decision-making. A multimodal AI system, for example, could analyze a production floor video, interpret sensor outputs, and factor in spoken operator feedback to deliver real-time operational guidance.

“This marks a fundamental transformation in how businesses operate and innovate,” said Roberta Cozza, Senior Director Analyst at Gartner. “Multimodal GenAI will enable enterprise software to take proactive actions and make more relevant, contextual decisions.”

The impact is expected to be particularly significant in healthcare, finance, and manufacturing, where industry-specific models can use multimodal reasoning to automate tasks, improve accuracy, and deliver contextual intelligence.

Multimodal AI becomes strategic priority

Gartner’s Emerging Tech Impact Radar places multimodal GenAI at the center of strategic technology investments. The report advises product leaders to begin evaluating and integrating these capabilities to unlock competitive advantage and higher business value.

Currently, most generative AI models support limited modalities—typically combining two or three, such as text-to-image or audio-to-text. But Gartner anticipates a dramatic expansion in the coming years, enhancing both user experience and operational potential.

Cozza emphasized that organisations must now prioritise the integration of multimodal capabilities to drive innovation and productivity. “Leveraging diverse data inputs will redefine how enterprise software supports users and decisions,” she noted.

Experts suggest this trend could surpass the disruption caused by large language models, marking a pivotal phase in enterprise AI. Gartner’s forecast serves as a strategic signal: companies investing in multimodal GenAI today will lead tomorrow’s enterprise software revolution.