With multimodal AI, businesses can manage today’s customer inquiries

When multimodal AI is combined with dynamic case management, businesses can dramatically improve both response time and customer experience.

Today’s customer inquiries can take many forms: a text inquiry about an insurance claim might be accompanied by an image of the damaged car. Or a phone call might be followed by a video of the defective product. These multimodal inquiries are increasingly common, especially in sectors like e-commerce and insurance.

Traditional case management systems struggle to integrate and process this diverse data, leading to long response times and acute customer frustration. Resolution times increase, and customer satisfaction drops, impacting both reputation and revenue.

To address these challenges, businesses need a dynamic case management system that can be integrated with a combination of multimodal, generative and agentic AI. Such a system can help them understand, orchestrate and process these varied customer interactions.

Dynamic case management provides flexible workflows that respond to the many twists and turns that can happen when resolving a customer inquiry. Meanwhile, multimodal large language models (LLMs) process the texts, emails, voice, images and videos that are involved with customer inquiries. This approach enables real-time adaptability to unique customer scenarios, ensuring businesses can respond swiftly and effectively. The following figure illustrates the way this might work.

Figure 1

Meet customers wherever they are

Here are some of the capabilities businesses gain by combining multimodal AI and dynamic case management:

• Handle customer issues in any format. Since customers submit inquiries via email, phone calls, images, videos and voice notes, enterprises need intelligent systems that can understand and respond to all these formats.

• Adapt quickly when problems can’t be resolved using existing processes. Not every issue fits neatly into a predefined workflow. When the unexpected happens, businesses must respond without delay.

• Let the situation shape the workflow. Instead of forcing every issue through a rigid process, intelligent IT systems should let the details of the situation guide what happens next.

• Continuously improve based on real-world cases. No matter how well businesses plan, unusual cases do occur, and businesses need to learn from them. When managed correctly, AI’s ability to learn from “edge cases” is one of its greatest strengths. LLMs can be continuously fine-tuned to handle these cases better next time. This creates a flexible enterprise model where workflows are continuously refined based on feedback from real-time data, improving overall efficiency and customer satisfaction. As multimodal AI evolves, its integration with workflows will drive further innovation in business process management.

These kinds of responses already occur in top companies, whether they serve consumers or other businesses. But the time and human effort required for such course corrections can be dramatically reduced. When a defect is reported, a 72-hour escalation window is no longer acceptable. Why not aim for two hours instead?

Looking ahead with multimodal AI and dynamic case management

With multimodal AI and dynamic case management, businesses can transform how they listen, respond and evolve. When systems can understand customers in their own words, images or voices, service becomes faster, smarter and more human. The future of customer service isn’t just omnichannel—it’s omnilingual, omniformat and always learning.