Top 10 Multimodal AI Applications, Benefits and Challenges

When Generative AI arrived, people were overwhelmed. Over time, it could analyze images, text, and voice. However, Gen AI analyzed them separately. So, the results were far from accurate. When people watch a video or read text, they perform multiple tasks simultaneously. These tasks can include the following:

• Listening to Users’ Voice Inputs
• Understanding Facial Expressions
• Sharing Data With Others

The best thing about multimodal AI is that it analyzes data in many formats. Additionally, users don’t have to wait for multimodal AI to analyze each format separately. It can analyze several formats simultaneously. This helps companies acquire a better understanding of real-world scenarios.

The size of the multimodal AI market was said to be at $2.35 billion in 2025. This figure is expected to reach $55.54 billion by 2035. Leading sectors such as healthcare, e-commerce, and manufacturing have embraced multimodal AI. Companies must understand that the switch to multimodal AI can do a lot more than improve performance or refine AI models. Leveraging multimodal AI enables companies to revolutionize product development. It also helps them streamline their operations.

How does multimodal AI work? Which sectors use it the most? What challenges do companies face while using multimodal AI? We know our readers have many questions to ask. This blog will touch on multiple aspects related to multimodal AI. No more waiting, let’s get started.

What Is Multimodal AI?

Multimodal AI is a subset of AI. It can understand diverse types of data at the same time. This includes images, text, voice, numbers, and more. Understanding such data and interpreting it in this way is closer to how humans process information. The best thing about multimodal AI is that it does not consider data from a single source as a separate problem. For instance, there is an image with a text caption. Multimodal AI analyzes the image thoroughly along with the text. Furthermore, it also analyzes the text’s context.

With such an approach, multimodal AI can solve complex real-world problems. It is similar to how humans communicate and make decisions by taking many aspects into consideration. This includes numbers, evidence, behavioral signs, and more. Multimodal AI adopts the same approach. It analyzes changes in voice tone, expressions, words, and others to arrive at a decision.

How Is Multimodal AI Different from Generative AI?

Gen AI focuses on creating new and original content. It considers inputs from just one modal. If a user instructs Gen AI to create an image, it does it. The AI model does not consider aspects such as user intent or context. Over time, the model observes patterns and learns from different inputs.

Compared to Gen AI, multimodal AI considers input from various sources. This includes text, visuals, audio, and video. Multimodal AI doesn’t just consider data from a single modality. It combines inputs from all the sources to arrive at a decision. This leads to better outcomes. The results are as follows:

• Deeper Context
• Human-Like Reasoning
• Greater Personalization 

Are you interested in using multimodal AI for your business? Consider partnering with a company specializing in AI development services for optimal results.

6 Key Elements of Multimodal AI

Below are some of the key elements of multimodal AI.

• Multiple Data Modalities

Considers input from multiple modalities. This includes text, images, audio, video, sensors, data, and more.

• Modality-Specific Encoders

Uses separate models or processors to understand different types of data. Examples include CNN for images and transformers for text.

• Feature Alignment

Converts different types of data (modalities) into a uniform format. This allows the AI model to understand different types of data and their inter-relationships.

• Fusion Mechanism

Combines data from multiple modalities. These modalities include text, images, audio, and more. The AI model may perform this action at the start, end, or both. This improves its ability to understand things.

• Cross-Modal Reasoning

Understands the context between different modalities. For example, it analyzes text and what’s shown in the image. Then, it connects the dots to get the context.

• Unified Output Generation

Analyzes the data that it has gathered from several modalities. Then, it generates an accurate and relevant answer.

Want to learn more about multimodal AI and use it to achieve your business goals? Team up with an AI development company in the USA.

How Does Multimodal AI Work?

Multimodal AI is a type of AI that goes the extra mile. Generative AI does what users tell it to do. All it does is follow instructions. Unlike Gen AI, multimodal AI gathers inputs from diverse data types and analyzes them. Based on the detailed analysis, it generates results. Below is a brief explanation of how multimodal AI works.

Gathers Different Inputs

Multimodal data gathers different types of data. This includes the following:

• Text
• Images
• Voice
• Videos
• Sensor Data

The AI model collects this data, but it doesn’t analyze it separately. It adopts a holistic approach and understands the context.

Converts Inputs Into Numbers

Converts diverse data into numbers that the AI model can understand. This includes text, images, sounds, and visuals. After converting them to numbers, the model can process, learn, and understand data better.

Separate Models for Each Data Type

• Text - Language Models
• Images - Vision Models
• Audio - Speech or Sound Models

Extacts Meanings

Every AI model tries to find useful cues in data. It tries to find subtle details in texts, changes in emotions, or images. Such an approach helps multimodal AI understand the context and respond accordingly.

Summarize Data

After extracting the meaning from data, multimodal AI combines them to gain a thorough understanding. It also considers the context of information.

Identify Relationships Between Inputs

Multimodal AI understands the connection between different data types. Establishing relationships between sounds, images, and text results in better results.

Making Predictions or Decisions

Based on the data gathered and processed from different modalities, the AI model answers questions or performs tasks.

Generates Output

The output can be in different formats, including the following:

• Text
• Images
• Videos
• Speech
• Action

Constant Learning

The AI model improves constantly over time. Be it tasks, mistakes, images, or examples, the AI model learns from each prompt. This helps it refine its output.

Use in Real-World Scenarios

Multimodal AI works in different sectors and scenarios. Examples of real-world scenarios include the following:

• Chatbots
• Self-Driving Cars
• Medical Diagnosis
• Video Analysis
• Virtual Assistants

Do you need step-by-step guidance on leveraging multimodal AI for your organization? Hire AI developers with the right experience and skills for your next project.

9 Most Popular Multimodal AI Platforms

The market is full of multimodal AI platforms. Each of them has its strengths and weaknesses. Below are 8 of the most popular multimodal AI platforms.

1) OpenAI (GPT 4.1/GPT 4o)

Modalities - Includes text, images, audio (input and output), along with code.

What Does OpenAI Do?

• Conversational AI can analyze vast volumes of data. It can view and comprehend various types of data. This includes images, text, voice, and documents. It can also generate text/code.

OpenAI plays an integral role in the following:

• Developing Chatbots
• Creating Coding Assistants
• Online Tutors
• Understanding IMages
• Developing Voice Agents

Overall, OpenAI is best for general-purpose tasks. It can think logically using different types of data. Also, developers can use OpenAI to create real-world applications.

2) Google DeepMind (Gemini)

Modalities - Includes text, images, audio, video, and code.

What Does Gemini Do?

• Gemini can process data from various modalities. It can put them together and understand the context. This ensures accurate and context-aware decision-making.

Robust integration with the following:

• Google Search
• Google Docs
• Gmail
• YouTube

As Gemini is built from the ground up, it can understand various data formats. Not only that, it can remember past conversations and analyze long inputs. Examples of long inputs include lengthy documents and voices. It does all of this without making mistakes.

3) Anthropic (Claude 3.x)

Modalities - Includes text, images, documents, and audio (limited support)

What Does Claude Do?

• Does a great job at reading long documents. It can decode their meaning and explain/summarize complex concepts easily. At the same time, Claude exercises caution. It provides accurate and safe responses.
• Widely used in sectors such as legal, finance, and research.
• Most importantly, Claude does a great job at understanding and explaining complex concepts/topics. It even analyzes long documents and provides detailed clarification. Claude is designed to provide accurate and safe responses. Whenever it generates responses, it avoids biased or misleading information.

4) Meta AI

Modalities - Includes text, images, audio, video, and sensor data.

What Does Meta AI Do?

• Both the AI model and the research are open-source. Anyone can access it. They can even edit it and make improvements to it.
• Converts diverse data into a format that the system can understand.

Meta AI’s biggest strengths include research innovation and its open-source nature.

5) Microsoft Copilot

Modalities - Includes text, images, voice, documents, and code.

What Does Microsoft Copilot Do?

• Microsoft apps such as Windows, Word/Excel, GitHub, and Azure are infused with AI capabilities. This helps developers work smoothly without having to change/swtich apps.
• Built for enterprise productivity and developer assistance.

6) Amazon (AWS AI / Bedrock)

Modalities - Includes text, images, audio, and video.

What Does Amazon Do?

• Allows developers to use advanced AI models using APIs. No need to host or build models from scratch.
• Automatically understands and responds to customer queries. It can analyze and process various types of data. This helps it deliver valuable insights. Businesses can use these insights to operate smoothly and make informed decisions.

Users can use Amazon to run multimodal AI on the cloud. They can also use it to handle small to large workloads. Depending on the workload, companies can scale up and down whenever required. No need to manage servers.

7) IBM Watsonx

Modalities - Includes text, documents, and images (limited audio capabilities)

What Does IBM Watsonx Do?

• Enterprise-grade AI for regulated sectors. This includes banks, healthcare, and government entities that are subject to strict laws and regulations.
• Focuses extensively on governance, explainability, and compliance.

Enterprises and businesses operating in regulated environments can benefit from this platform. It delivers safe results while ensuring transparency.

8) Runway ML

Modalities - Includes text, image, video, and audio.

What Does Runway ML Do?

• Excellent tool to create and edit videos. Also helps generate stunning images.
• Filmmakers and content creators use Runway ML use it for varying degrees.

Runway ML provides the best tools to create and edit videos. The platform makes the process smooth and easy. The reasons why Runway ML include the following:

• Top-Notch Output
• Easy to Use
• Advanced Features

9) Hugging Face

Modalities - Includes text, images, audio, and video.

What Does Hugging Face Do?

• Website with several free and open-source AI models. Anyone can use and experiment with these models to build advanced AI apps.
• Excellent tool to train, refine, and deploy AI models.

Hugging Face has the best tools and models that developers can use. The platform’s community continuously creates apps, plugins, and makes improvements. This shared innovation spearheads the platform’s growth.

These are just some of the multimodal AI platforms. Thinking about using a multimodal platform to develop key business apps? Partner with a company specializing in AI development services.

8 Benefits of Using Multimodal AI for Businesses

• Combines all insights, including audio/video, images, and text. This improves decision-making.
• Multimodal AI makes interactions more natural and accurate. This greatly improves customer service efforts.
• Automates complex, multi-format workflows. This boosts operational efficiency.
• Refines unstructured data to extract valuable insights. If each modality were to be analyzed separately, the results would be nowhere near.
• Checks data across various modalities. This ensures accuracy and reliability.
• Enables companies to personalize their offerings. Helpful in marketing, sales, and support-related tasks.
• Integrates multiple AI tools within a single platform. This reduces costs significantly.
• Analyzes multiple clues simultaneously. This increases the efficiency of fraud detection and risk analysis efforts.

7 Industry-Wise Use Cases of Multimodal AI

Below are some of the top use cases of multimodal AI.

1) Healthcare

Gathers data from several sources. This includes medical images, clinical notes, lab reports, and patient voice notes. These sources include the following:

• Medical Images
• Clinical Notes
• Lab Reports
• Patient Voice Notes

Using this approach, clinics and hospitals can make faster and more accurate diagnoses. Diseases can be diagnosed faster.

Besides speedier detection, treatments can even be personalized.

Detailed voice and video analysis enables real-time monitoring of patient conditions.

Streamlines and automates tasks such as documentation and record-keeping.

2) Finance and Banking

Analyze huge data volumes instantly. Data includes the following:

• Transaction Details
• Documents
• Voice Calls
• Facial Recognition

By analyzing data thoroughly, multimodal AI can improve and automate the following:

• Fraud Detection
• Customer Verification
• Risk Assessment

AI agents can serve customers better and at any time of the day. They understand text, voice, and other modalities simultaneously. This gives them the edge.

3) Retail and E-Commerce

Gathers data from multiple sources. These sources include the following:

• Product Images
• Customer Reviews
• Browsing Patterns
• Voice Queries

Gathering and analyzing this data helps companies personalize their offerings.

With multimodal AI, customers can conduct visual searches and try products online.

Predicting customer demand becomes easier. This helps immensely with inventory tracking and maintaining the necessary levels.

Customer service will become more improved and that too across multiple modalities.

4) Manufacturing

Integrates data from multiple sources, including sensors, machines, video feeds, and maintenance logs.

This data helps companies predict equipment breakdowns. Technicians can adhere to maintenance schedules. This ensures quality control and significant savings.

Workers can detect machine issues in real-time. Workers feel safe as machines are in working condition. Production planning becomes more streamlined.

5) Education

Combines various modalities such as text, speech, handwritten notes, and videos.

This makes it easy to implement personalized tutoring. Students can be graded accordingly.

Suppose a particular group of students has learning or other disabilities. In such cases, multimodal AI can provide accessibility tools for them.

Using voice and visual methods can ensure interactive learning experiences.

6) Security

Gathers data from multiple sources. This includes video footage, audio, biometric data, and people’s behavioral patterns. This helps to organize and detect threats and anomalies.

Organizations can also automate and improve the following with multimodal AI.

• Facial Recognition
• Intrusion Detection
• Crowd Monitoring
• Public Safety Response Systems

7) Marketing

With multimodal AI, marketing and advertising companies can analyze various types of data. This includes the following:

• Images
• Videos
• Text
• User Feedback
• Customer Behavior

Based on the analysis, companies can create content to appeal to their target audience. This includes the best images, videos, and text. Also, they can decide when and whom to target with paid ads. This will maximize ROI on spending. Increased visibility and reach on social media and the internet will also help in the long run.

Interested in leveraging multimodal AI for your business? Partner with a reputed AI development company in the USA. The right partner can implement tried and tested strategies and make the most of multimodal AI.

8 Challenges of implementing multimodal AI

In the above sections, we saw how multimodal AI can benefit businesses. They have their share of benefits, but they also have plenty of challenges. Below are some of the most formidable challenges of implementing multimodal AI in businesses.

Data Mismatch

The solution lies in using clean, organized, and standardized data from all sources.

High Computing Costs

Optimize AI models and use top-quality hardware.

Complex Model Design

Use modular and pre-trained AI models.

Hard to Train AI Models

Train in smaller batches. Use better pipelines.

Integration Complexities

Standardize inputs and outputs early.

Poor Data Quality

Ensure that data is filtered, labeled, and validated before training AI models.

Algorithm Bias and Across Modalities

Train AI on diverse data. Identify and fix biases in the algorithm, if any.

Difficulty in Debugging

Errors can creep in from any output. So, it is important to maintain clear logs. This will help companies understand the type of data that causes the problem.

Cost of Implementing Multimodal AI

The cost of implementing multimodal AI depends on various factors. These factors include integration requirements, custom model building, and infrastructure costs. Maintenance costs can also be high, especially for small and emerging companies. Below are some of the costs of implementing multimodal AI in business operations.

• MVP Product - $50,000 - $150,000
• Medium-Complexity Multimodal AI - $150,000 - $500,000
• Enterprise Multimodal Platform - $500,000 - $5M+
• GPU Cluster Operation (Monthly) - $50,000 - $500,000+
• Annual Maintenance - 20% to 30% of the initial cost.

These costs are just to give our readers a brief idea about implementing multimodal AI in operations. Consult a reputed AI development company in the USA for the best results.

Concluding Remarks

Multimodal AI is not a new concept. It is just the way AI has evolved in the past few years. Compared to Generative AI, multimodal AI analyzes and processes data from many sources. This ensures that the results it generates are richer and more accurate. Multimodal AI thinks like a human and considers text, audio, video, and emotions. But it does things better and faster than humans.

As simple as it may sound, implementing AI in businesses can be tricky. This is why it is crucial to gain a thorough understanding of it. If companies lack the relevant knowledge to implement it, it would make sense to partner with a company specializing in AI development services. Some years ago, voice-powered searches were considered a breakthrough. However, multimodal AI has changed the scene. Even highly regulated industries such as healthcare and banking are adopting multimodal for greater operational efficiency.

Also, if a company wants to micro-manage things and have total control over AI model development, hiring AI developers would make sense.