The multimodal artificial intelligence (AI) market is growing rapidly. The global market stood at USD 1.7 billion in 2024 and is projected to reach USD 10.9 billion by 2030, growing at a CAGR of 36.8 percent, according to Grand View Research.
Multimodal AI refers to systems that can process and reason across multiple types of data, including text, images, audio, and video. Instead of treating each format separately, these systems can interpret different types of information together. While a traditional AI system might read an invoice or analyze a photograph, a multimodal system can interpret the invoice, the photograph, a technician's voice notes, and other relevant inputs as part of the same context.
The distinction matters because enterprise data rarely exists in a single format. A maintenance report, a sensor reading, a photograph of worn equipment, and a technician's voice note about the same fault all describe one event. With conventional AI, each format may be processed separately, leaving teams to check the outputs, reconcile them, and piece together the full picture. The result is additional manual effort, slower decisions, and, in some cases, insights arriving too late to act on.
Gaps like this, where one event is scattered across formats with no single system to view it as a whole, pushed AI development toward models built to hold multiple data formats together. Why keep running parallel systems for one event?
Five areas where multimodal AI is earning its keep
Enterprises adopting multimodal AI tend to focus on a handful of high-value areas, each addressing a variation of the same challenge: making decisions based on evidence spread across multiple formats—something single-format tools can only address in isolation. These include:
- Document intelligence: Contracts, invoices, and forms combine structured fields, free text, and layout elements such as tables and stamps. Multimodal AI reads all three together, replacing manual effort at a scale that earlier tools could not match.
- Visual quality inspection: On production lines, multimodal AI correlates camera feeds with sensor and process data to catch defects early.
- Customer engagement: Contact centers increasingly field voice, text, and shared screenshots within one interaction. Multimodal systems interpret all three together, cutting the back-and-forth needed to understand what a customer means.
- Risk, fraud and compliance: Financial institutions pair transaction patterns with document images and behavioral data to flag fraud that a single data stream would miss. Insurers apply a similar approach to claims, combining photos of damage with claim forms and adjuster notes to catch inconsistencies faster.
- Knowledge and R&D synthesis: Research teams use multimodal AI to read a technical paper, interpret an accompanying diagram or schematic, cross-reference the tables, and then summarize the findings in plain language, cutting time-to-insight on dense technical material.
In short, wherever enterprise decisions depend on more than one type of evidence, multimodal AI has proven to be faster and more accurate than forcing that evidence through single-format tools.
The groundwork for multimodal adoption
The technology is developing quickly. Realizing its value requires the right data, processes, and use cases. Here are some pointers on how to lay the foundation for multimodal AI models:
- Establish a unified data strategy: The performance of multimodal AI platforms depends strictly on the alignment among its data types. Text, image, and audio records describing the same event need to be linked, not scattered across separate systems.
- Invest in high-quality AI annotation: Multimodal systems require training and validation data annotated consistently across every modality involved, not just the dominant one. Gaps here quietly cap model accuracy long before anyone notices.
- Start with a bounded, high-friction use case:The enterprises seeing the strongest returns are not the ones deploying multimodal AI everywhere at once. They are the ones picking one workflow, such as document processing or quality inspection, where mixed-format data is a known bottleneck.
- Build in human review for high-stakes decisions:Compliance, healthcare, and financial use cases still benefit from a human checkpoint, particularly while multimodal outputs are being validated against real-world outcomes.
Get a fuller picture, not just a faster one
The shift toward multimodal AI is changing enterprise operations. For years, organizations have processed text, audio, images, and video through separate AI tools, relying on manual effort to connect them. Multimodal AI evaluates these inputs together, improving decisions across document intelligence, quality inspection, fraud prevention, and customer experience.
Adoption, however, requires organizations to have a strong foundation of targeted use cases, human oversight for high-risk decisions, and a unified data strategy.
A majority of enterprises using AI are expected to run multimodal AI in the coming years. But these models alone cannot deliver results. Enterprises that gain the most from this shift will be those that connect data across formats rather than treat each as a separate asset.
How can Infosys BPM help?
Multimodal AI is only as reliable as the data annotation behind it, and most enterprises are not equipped to annotate text, image, audio, and video data consistently at scale. Infosys BPM's Data Annotation Services for AI/ML help organizations build that foundation by combining trained annotators, quality frameworks, and domain expertise to prepare multimodal training data that models can learn from. Infosys BPM teams work across data types and industries, helping enterprises move multimodal AI initiatives—from pilot to production—with confidence in the data underneath them.
