How to Optimize CX with Multimodal AI in 2025

TL;DR

1. Multimodal AI integrates text, audio, visual and other data types to deliver personalized, context-aware customer experiences across multiple platforms.
2. Multimodal AI transforms industries like healthcare, automotive, and finance by improving customer service, increasing engagement, and boosting conversion rates.
3. The benefits of multimodal AI include seamless cross-channel engagement, real-time context interpretation, and personalized customer interactions.
4. Challenges of multimodal AI include integrating siloed data, processing diverse inputs in real time, and deploying on legacy systems.
5. Businesses must adopt multimodal AI to stay competitive and deliver superior CX through intelligent, real-time, personalized interactions.

You may expect quick and seamless business service with so many communication channels at your fingertips, and why wouldn’t you? But it’s frustrating when businesses fail to deliver the same efficiency level. 

Or, you start with a phone call and then get transferred to a chatbot, only to explain your issue yet again. It’s frustrating, right? This is where multimodal AI comes in to solve the problem.

Multimodal AI systems allow artificial intelligence to process and analyze multiple data types simultaneously. Combining multimodal data will enable businesses to interact with you more personally and adaptively, ensuring a smooth experience across different platforms. 

At the Adobe Summit on March 18, 2025, Adobe launched AI agents, Agent Orchestrator, and Brand Concierge to optimize marketing workflows and deliver personalized customer experiences (CX).

Whether it’s a voice call, text chat, or visual content, multimodal AI makes it easier to engage with businesses on your terms. So, as multimodal models continue to shape the future of customer experience, it’s worth considering how companies like yours can leverage it to enhance service and stay competitive. 

Join the CX Leaders to Explore Great Customer Experience with Convin AI.

What is Multimodal AI?

Multimodal AI is an advanced machine learning model that simultaneously processes multiple modalities or data types. It integrates text, audio, visual and textual data modalities to create a more personalized customer experience (CX). 

It can combine natural language processing (NLP), computer vision, speech recognition, and multimodal generative AI models to create an intelligent system capable of interpreting diverse inputs for context-aware, human-like interactions. 

The multimodal AI models market is projected to generate $15.89 billion in revenue by 2032, growing at a compound annual growth rate (CAGR) of 4.8% from 2025 to 2032.

But what makes multimodal AI systems so powerful? 

A recent survey found that 38% of the industry's C-suite leaders consider improving customer experience and retention as the key reason for adopting applications based on artificial intelligence (AI), machine learning (ML), and large language models (LLM).

Industries such as healthcare, automotive, and finance are increasingly adopting multimodal AI systems to enhance analytics and automation, indicating a broadening application of this technology.

This tells us that multimodal AI’s ability has excellent potential in the industry. Its adaptability and acceptance among businesses will help improve the experience of millions of customers by providing a fresh perspective.

How Multimodal AI Models Will Enhance CX in 2025

With growing demand and greater efficiency in personalized service, companies must depend on multimodal AI systems to process different data streams and provide better customer service. 

But why is this shift necessary? 

AI's role in enhancing multimodal CX goes beyond simple automation. It's about designing context-aware, one-to-one experiences that scale across multiple channels, such as voice, chat, email, and social media. Artificial Intelligence tools like Convin's AI Phone Calls automate routine inquiries, streamlining customer service workflows.

By 2025, 80% of customer service organizations will have implemented generative AI technology to enhance agent productivity and customer experience (CX).

What are Multimodal AI’s Capabilities

Well, multimodal AI is not merely responding to customer queries; it's about grasping context, answering in a personalized way, and continuously improving over time. Here’s how multimodal models improve customer service:

Cross-Channel Engagement: AI platforms can seamlessly switch between data types (text, video, audio data) without losing a beat. This integration allows businesses to offer seamless omnichannel experiences, ensuring customers don’t have to repeat themselves when switching channels.

Context-Aware Conversations: Multimodal AI models can better interpret a conversation's context by combining data types. For example, a voice assistant can hear the urgency of a customer's tone while processing the text input module from a previous chat session, giving a more complete response.

Why does this matter for businesses?

Multimodal AI is anticipated to be involved in 100% of customer interactions, blending AI and human expertise to craft the best customer experiences. This indicates a future where AI is fully integrated into CX strategies. 

Therefore, multimodal AI models ensure that responses are relevant and meaningful by understanding voice tone, intent, and sentiment.

Start Scaling with Convin’s AI model that understands voice, intent and sentiment.

How Multimodal Generative AI Personalize Customer Experience

Multimodal generative AI is especially valuable in personalizing customer experiences. The technology can create personalized responses by analyzing customer data from voice, text, and images, making conversations interactive, human-sounding, and highly attentive.

Companies that use real-time data integration see an impressive 45% increase in customer engagement and 35% improved conversion rates, categorically attesting to the importance of agility in customer experience management.

For Convin, this involves the fact that regardless of whether a customer is calling on the phone, messaging a bot, or interacting through email, the AI continuously collects data from these conversations to provide a response that will make the customer heard and understood. 

Through its real-time agent-assist product in the run, it takes every customer interaction to the next level by leveraging a multimodal generative AI-assisted knowledge base to offer rapid access to correct information with real-time guidance to surpass revenue goals and achieve KPIs.

Challenges of Multimodal AI in Building Multimodal CX

While multimodal AI models bring massive value to CX, they also have real challenges.

First, raw data is often siloed across systems, and AI cannot deliver truly personalized or context-aware responses without unified access to voice, text, and visual data, limiting the effectiveness of customer interactions.

Second, processing multiple data types in real-time requires advanced infrastructure. AI must interpret tone, text, and visuals simultaneously, which puts pressure on system speed and reliability.

Third, training large multimodal models demands large, high-quality datasets. Inconsistent or biased input modules can lead to inaccurate or generic responses.

Lastly, integrating multimodal AI into legacy systems is among the most complex tasks. Many platforms aren’t AI-ready, making deployment slow and resource-heavy.

Despite these roadblocks, platforms like Convin simplify adoption with ready-to-integrate, scalable solutions built for real-time CX.

How Multimodal AI Addresses the Growing Demand for Modern CX

The multimodal AI market is expected to grow at a CAGR of 36.92% from 2025 to 2034, potentially reaching USD 42.38 billion by 2034.

This signifies that modern customers expect faster, more personalized service across all touchpoints. The future of multimodal AI makes this a reality by enabling companies to read data from numerous sources and provide context-based real-time responses. 

But how can companies ensure they’re meeting customer demands without falling behind?

Enhancing Efficiency and Accuracy with Multimodal AI

Speed and accuracy are essential in customer service, and multimodal AI models help businesses by automating mundane tasks, such as answering frequently asked questions or providing order updates. This enables generative AI to handle these simple tasks and frees agents to focus on more complex questions, leading to faster resolutions and increased customer satisfaction.

“ A 2024 survey by TrueAccord found that 60% of consumers prefer self-service options, with 54% having used an online portal provided by a biller, indicating a strong inclination towards AI-driven self-service platforms.”

With the addition of speech recognition and real-time sentiment analysis, Convin's AI ensures that the responses are empathetic, accurate, and relevant and, therefore, deliver a good customer experience, with its clients experiencing a 27% increase in their CSAT scores.

This, in turn, reduces the risk of miscommunication, especially in fast-paced environments like call centers, and ensures that customers receive the correct information the first time.

Omnichannel Integration Through Multimodal AI

Customers now want a seamless omnichannel experience and the ability to switch between communication channels without context loss. Multimodal models enable your businesses to integrate their systems across multiple types of channels. 

It allows customers to transition from a voice call to a chat session or from an email to a video call without missing data.

97% of communications service providers report that conversational AI positively impacts customer satisfaction, highlighting the effectiveness of AI-driven interactions in enhancing CX.

Convin automatically monitors and analyzes 100% of conversations about your organization's specific parameters with its conversational intelligence multimodal AI model. Conversation analysis offers winning behavior detection and last-mile automated agent training.

This capability is relevant in industries like customer service call centers, where customers will begin a query on one channel and complete it on another.

Elevate your Omnichannel Customer Experience (CX) with Convin’s AI Model.

Personalizing Interactions in Real-Time with Multimodal AI

Personalization is a critical component of modern CX, and multimodal systems allow businesses to analyze customer intent, tone, preferences, and previous interactions in real time. By doing so, AI can provide responses tailored to each individual, improving engagement and satisfaction.

Convin's AI Phone Call can detect the emotional undertone in a customer's voice and respond accordingly. This real-time emotional insight into customers is a differentiator that enhances CX and customer relationships.

In such a case, the multimodal generative AI models can sense this subtlety and adjust their response to be more empathetic. They use the background of the conversation to suggest an ideal solution. This level of customization renders interactions as meaningful as possible and increases the likelihood of receiving a desirable solution for the customer.

Multimodal AI vs. Unimodal AI

Regarding related multimodal learning AI models that do the job, traditional unimodal AI typically operates with just one type of data (text, voice, or image). This limitation can hinder customer service experiences because it cannot provide contextual responses from multiple data types. 

In contrast, multiple modalities of multimodal AI integrate diverse data types to create a more complete understanding of customer needs.

Convin's multimodal features include multilingual speech recognition and sentiment analysis, and its in-house patented LLM model has trained over 7 billion-plus data pointers and 35+ regional and international languages.

Why Choose Convin’s AI Phone Calls in Enhancing Customer Experience in Real-Time

Convin’s AI Phone Calls exemplify how multimodal learning can be applied to real-world business challenges. By leveraging voice/speech recognition and sentiment analysis, Convin’s AI can understand not just the words spoken by the customer but also their tone and emotion. This allows Convin’s AI to respond in a natural and empathetic way, ensuring higher customer satisfaction.

1. Convin’s Impact on HealthTech

India’s largest digital healthtech platform with 24/7 healthcare accessibility for millions of customers faced multiple types of challenges, including high customer call influx, poor call quality, misselling, and inefficient call reviews. 

By implementing Convin’s multimodal AI, the platform monitored 100% of agent calls, addressing misselling and compliance issues. Convin’s Auto QA and Win Behavior Analysis uncovered these issues and provided comprehensive visibility into agent performance. This led to:

How Convin’s AI Transformed Operations:

1. 13% improvement in agent call quality: Real-time performance insights improved call quality from 62% to 75%.
2. 4% increase in sales closure rate: Enhanced agent focus on sales skills led to more deal closures.
3. 26% cost savings: The Auto QA feature allowed reallocating resources to hire more agents (from 80 to 170).
4. 8X ROI: Convin’s call quality and sales performance improvements drove an 8X return on investment.

Convin’s multimodal models provided real-time agent monitoring, AI-driven suggestions, and voice data analysis, boosting agent performance and sales.

2. Convin’s Impact on Insurance Sales

Established in 2000, a leading insurance company in India faced several key issues in its customer service operations. The insurance company struggled with misselling, poor first-call performance, and compliance violations across 1,000+ offices.

By adopting Convin’s Real-Time Agent Assist with multimodal data, including speech recognition, sentiment analysis, and real-time insights.

Success Metrics from Convin AI Integration:

1. 31% reduction in misselling: AI monitored tone and sentiment to curb misselling, providing real-time feedback.
2. 38% reduction in compliance violations: AI-powered suggestions ensured adherence to compliance regulations.
3. 36% reduction in negative sentiments: Sentiment analysis helped agents adjust their approach, improving engagement.
4. Revenue increased 23%: Real-time insights and behavioral analysis improved agent performance, leading to higher first-call closure rates.

Through Real-Time Agent Assist, Convin equipped agents with tools to improve performance and conversion rates while supervisors provided live feedback, increasing revenue.

Win Your CX Approach with Convin’s Real-Time Agent Assist to boost revenue by 23%.

Results of Convin AI’s Impact on CX

Businesses leveraging Convin’s AI have seen an astonishing impact:

1. Multilingual AI Agent: Supports multiple languages, empathizes with interruptions, and offers real-time interpretation for smooth conversations.

2. LLM-Powered NLU: Delivers context-aware, human-like conversations with multilingual understanding and low-latency NLP.

3. Seamless Live Agent Handoff: This feature automatically transfers calls to live agents when leads show interest, ensuring smooth follow-ups.

4. Post-Call Communication: Sends follow-up messages via WhatsApp or email with relevant details like tickets or itineraries.

5. Scalable, Customizable Voice Agent: This agent handles 1,000+ leads simultaneously and offers customizable dialogue flows that match your brand.

6. Insight Capture: Automatically collects key customer data for better decision-making and personalized follow-ups.

7. Effortless Integration: Integrates with dialers or telephony systems, updating CRM fields after every call.

Conclusion

Multimodal AI will be at the core of customer experience (CX) transformation in 2025 and beyond. Therefore, integrating text, video and audio data allows businesses to create seamless, personalized, and context-aware interactions across all customer touchpoints.

Hence, Convin helps businesses engage customers more effectively, improve first-call resolution rates, and increase agent productivity through AI-powered voice calls, chatbots, and real-time sentiment analysis. 

Businesses must embrace multimodal AI now to stay competitive and meet evolving customer needs. Convin’s AI offers solutions to help companies enhance every interaction, improve customer satisfaction, and drive business growth.

Ready to lead the future of customer experience? Take the Leap with Convin

FAQs

What are the benefits of multimodal AI in customer service?

Multimodal AI speeds up responses, personalizes interactions, and enables seamless cross-channel communication, automating tasks and improving support accuracy for better customer satisfaction and efficiency.

What advantage does multimodal generative AI offer over unimodality tools?

Multimodal generative AI combines text, voice, images, and video, offering more context-aware responses and delivering personalized, seamless interactions across channels.

Which industries benefit from multimodal AI in CX?

Banking, e-commerce, telecom, and healthcare industries use multimodal AI to improve customer experiences. These include AI voice assistants, personalized recommendations, and AI chatbots.

How can businesses integrate multimodal AI into their support systems?

Businesses can integrate multimodal AI into their support systems by connecting AI platforms to CRM software, chatbots, and phone systems. This integration provides seamless omnichannel support, enhancing efficiency and creating a more personalized customer experience.