5 Multimodal AI Use Cases Every Enterprise Should Know in 2025

If your enterprise AI strategy still relies solely on text-based models, you’re solving yesterday’s problems, not preparing for tomorrow’s opportunities.

Why? Because enterprises are no longer just experimenting with generative AI, they’re scaling it. But with scale comes a challenge: single-modality models are reaching their limits and multimodal AI is quickly becoming the new standard.

Despite the hype, most current AI deployments remain narrow in scope such as automating emails, summarising documents or generating code snippets. These systems depend on text-to-text large language models (LLMs), which perform exceptionally well as long as the input is clean, structured and language-based.

But that’s not how enterprise data works because enterprise data is multimodal by default:

• Customer feedback comes as reviews, screenshots, voice messages
• Product data spans CAD files, schematics, and videos
• Operations data includes logs, charts, tables, and dashboards
• Internal documentation combines text, visuals, and metadata

This is exactly the reason why enterprises are using multimodal AI to lead in the market with the following use cases:

1. Customer Support Automation with Image + Text Understanding

In large enterprises, customer support teams face the complex task of interpreting diverse user submissions: screenshots, error logs, product photos, and fragmented text descriptions. Traditional chatbots or rule-based automation systems fall short in these scenarios because they rely purely on structured or text input.

Multimodal AI changes the game. By combining vision and language understanding, models like GPT-4V or Claude 3 can interpret a user’s screenshot, analyse error messages embedded in UI, and even suggest resolution steps based on documentation or prior tickets—all in one go. Instead of routing a ticket through multiple agents, support queries are automatically triaged, summarised, and escalated intelligently.

For example, a telecom provider can use multimodal AI to resolve connectivity complaints by analysing a modem's LED status photo and accompanying a user’s text message like “not working again.” The model understands inputs and triggers context-aware responses or workflows—reducing resolution time and operational cost.

The result? Higher first-contact resolution, reduced agent workload, and better CX at scale. Enterprises investing in multimodal AI for support see measurable ROI in efficiency, agent satisfaction, and faster resolution.

2. R&D Acceleration by Fusing Text, Tables and Diagrams

Enterprises in sectors like biotech, pharmaceuticals, and engineering deal with vast unstructured research content: scientific papers with embedded diagrams, tables of results, handwritten lab notes, and structured datasets. Traditionally, human analysts would need to manually interpret, correlate, and validate findings across these modalities.

Multimodal AI models remove these bottlenecks. They can read a research paper, interpret a diagram (e.g., a molecular structure or a prototype schematic), cross-reference it with tables or graphs, and summarise the key insights in plain language. The AI effectively acts as a “research assistant” that understands the full picture—not just text in isolation.

For example, in drug discovery, models can process chemical structure diagrams and correlate them with patient trial data and documentation to recommend next-step compounds. In engineering R&D, AI can analyse product test reports that contain visual inspection photos, thermal images, and annotation-heavy PDFs.

This dramatically reduces the time-to-insight in complex R&D workflows, enabling enterprises to bring innovations to market faster. By embedding multimodal AI into their research stack, organisations can empower smaller teams to achieve what previously required larger analyst teams and get there faster.

3. Compliance and Risk Monitoring Across Visual and Textual Documents

Regulated industries like finance, legal, and healthcare must process and validate thousands of multimodal documents for compliance. Think contracts with annotated clauses, signed forms with embedded tables, or identity proofs with photos and text. Manual auditing is time-consuming, error-prone, and expensive.

Multimodal AI makes compliance automation far more robust. It can “read” a document like a human would—understanding layout, interpreting tables, identifying signatures and logos, and recognising red flags in text and visual cues. The model can spot inconsistencies between document versions, verify data fields across modalities and highlight compliance gaps or missing disclosures.

In financial services, for instance, AI can process loan applications containing scanned PDFs, bank statements with charts, and hand-filled forms. It ensures all compliance checkboxes are met before approval. In healthcare, multimodal models can analyse medical records, handwritten prescriptions, and insurance forms, ensuring correct coding and regulation adherence.

4. Intelligent Enterprise Search That Goes Beyond Text

Enterprise knowledge bases are no longer limited to written documentation. Training manuals include images and diagrams. Field reports have photos and video clips. Marketing assets contain layered infographics and slide decks. Traditional enterprise search tools, however, remain limited to text, missing out on all this context-rich content.

Multimodal AI transforms internal search into a truly intelligent experience. Employees can query the system using natural language like “Show me how to calibrate model X with the red sensor error” and receive not just text results, but relevant screenshots, instructional videos and annotated diagrams- all ranked by context and relevance.

The AI understands the user’s intent, processes inputs and outputs across formats, and retrieves cross-modal results. This is invaluable in operations, engineering, sales enablement and support, where time-sensitive information must be accessed instantly.

For example, a technician in the field can take a picture of a faulty machine part, upload it, and retrieve relevant maintenance logs, videos and troubleshooting steps, all pulled from the enterprise knowledge base via a multimodal model.

5. Product Catalogue Management in E-Commerce and Retail

Large-scale e-commerce and retail operations manage vast product catalogues with millions of SKUs. Adding or updating product listings is often manual and inconsistent—especially when dealing with images, descriptions, pricing, attributes, and third-party sources. Delays in catalogue updates can lead to lost revenue and poor customer experiences.

Multimodal AI can streamline and automate this process. A model can look at a product image, generate a rich, SEO-optimised description and auto-fill attributes like colour, size, material, and even recommend tags. It understands visual style, brand tone, and industry-specific vocabulary—reducing the dependency on manual copywriters and product editors.

Retailers can also use the model to detect duplicates, verify image-to-description accuracy, and standardise listings across languages or platforms. When integrated with product analytics, the AI can suggest which images drive higher conversions and optimise listings accordingly.

Conclusion

As enterprises continue to move from experimentation to real-world implementation, the limitations of single-modality models are clear. The future of generative AI in the enterprise is not just about processing text, it’s about making sense of voice calls, images, diagrams, video streams and structured data. This shift to multimodal AI demands an infrastructure that can match its complexity, scale and performance needs.

To fully explore the potential of multimodal models, enterprises need scalable clusters, advanced networking and high-performance data storage. They need environments purpose-built to support high-volume training and low-latency inference across diverse data types and workflows. Our AI Supercloud delivers exactly that: fully customised, high-performance computing solutions with state-of-the-art hardware such as the NVIDIA HGX H100, NVIDIA HGX H200 and the upcoming NVIDIA Blackwell GB200 NVL72/36. These are optimised with NVIDIA-certified WEKA storage with GPUDirect Storage support and advanced networking solutions like NVLink and NVIDIA Quantum-2 InfiniBand, ensuring seamless performance for large-scale workloads.

FAQs

What is multimodal AI?

Multimodal AI refers to models that can process and understand multiple types of data at once like text, images, audio and tables. This enables more human-like reasoning and richer, context-aware insights across different inputs.

What are the use cases of multimodal AI for enterprises?

Multimodal AI powers advanced use cases like customer support automation, enterprise search, compliance checks, product catalogue management and research acceleration by combining text, visuals and structured data for more accurate and efficient decision-making.

Why should enterprises use multimodal AI?

Because enterprise data is naturally multimodal- spanning documents, images, charts and voice. Multimodal AI helps enterprises extract insights faster, automate complex tasks, improve customer experience and stay competitive in an AI-driven market.

How does multimodal AI improve customer experience?

Multimodal AI can analyse screenshots, product photos and messages to resolve issues faster. By understanding both visual and textual data, it delivers quicker and more accurate responses. This reduces escalations and enhances customer satisfaction at scale.

Which industries benefit most from multimodal AI?

Industries like healthcare, retail, finance, telecom and manufacturing benefit the most when data comes in various formats. Accuracy, speed and automation are critical to operations and compliance.