Enterprises today deal with a mix of structured and unstructured data. This includes structured data in organized tables and unstructured data such as text, audio, images, and video clips. Analyzing such diverse data sets together has traditionally been a complicated process because it requires using different tools. However, all the associated complexities and challenges are being overcome with the help of multimodal AI analytics.

This field of AI-based analytics is changing the way CDOs and their teams approach data analysis. It provides exceptional capabilities for understanding complex data while creating a holistic approach to decision-making.

Moving from Unimodal to Multimodal Analytics

The move from unimodal analytics to multimodal AI analytics is a major technological shift. Initial AI systems have been quite specialized. They involved the use of image classifiers capable of identifying objects. However, they couldn’t comprehend the associated text. Similarly, natural language processors (NLPs) were capable of analyzing sentiments but missed out on visual cues that offered important context.

The limitations of unimodal data analytics can be realized from the following example:

A customer service chatbot that analyzes only text has a very high chance of missing out the frustration apparent in the voice tone of a customer. A CCTV-based security system that relies solely on video feeds can entirely ignore audio cues that may indicate potential security threats.

This is where multimodal AI data analytics comes into the picture.

What is Multimodal AI Data Analytics?

Multimodal AI data analytics refers to AI systems designed to process and comprehend information from different modalities at the same time. Here, modalities refer to different types of data sets and sources, including:

  • Image
  • Text
  • Audio
  • Video
  • Time
  • Sensor data

And more. Multimodal AI is capable of learning the inter-relations between all the different data forms. This helps in enhancing information processing and comprehension as compared to unimodal AI systems. Multimodal AI is capable of working similarly to the human brain in terms of integrating information from different senses to create a complete picture.

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Benefits of Multimodal AI Analytics

Your business can gain a significant edge in the current data-based world by leveraging the benefits of multimodal AI analytics. Let’s first explore multimodal AI’s benefits in two key areas. The first is business operations and the second is business strategy.

Impact on Business Operations

Multimodal AI basically changes the way businesses function. It integrates different AI systems to process and analyze different data types and generate valuable insights. Your business will be able to benefit from a vast data pool, ensuring greater accuracy and richer insights.

For example, companies can rely on multimodal AI to analyze the following data sets for a deeper understanding of their customers:

  • Customer feedback
  • Social media interactions for sentiment analysis
  • Voice conversations
  • Behavior on the website

The application of multimodal AI can also improve your business process efficiencies. For example, you can have a system that uses voice recognition to record meetings, generative AI to develop minutes of the meeting, and NLP to comprehend context. Such systems can enable your employees to focus on core and most profitable tasks by streamlining operations and freeing up time.

Impact on Business Strategy

When integrated into your business strategy, multimodal AI analytics can help drive creativity, revenue, and decision-making. It can help you predict customer requirements, improve your marketing strategy, and enhance product design. For example, you can leverage generative AI for developing customized marketing campaigns around customer behaviors and preferences.

Additionally, multimodal AI is also capable of increasing cost savings. For example, the use of multimodal AI customer service can help reassign valuable resources to more productive areas. Human resources can then be used only for the most high-touch scenarios.

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Advantages of Multimodal AI Analytics

Now that you understand how multimodal AI analytics works on improving business operations and strategy, it is important to learn about its unique advantages over conventional data analytics approaches.

i. Better Contextual Comprehension

When different data types get integrated, multimodal AI can capture distinctions in not just language, but also in environments and emotions. This improves its output accuracy.

ii. Increased Predictive Analytics Accuracy

When different data sources are combined, the accuracy of pattern detection increases significantly. The predictions become more reliable in marketing, logistics, and other areas critical to your business’s success.

iii. Greater Adaptive Capabilities

Multimodal AI models are capable of adapting readily to novel and complicated contexts. They can adjust outputs around a wide range of inputs received.

iv. Automating Complex Tasks

Generative multimodal AI systems can automatically generate reports and handle multichannel customer support. They can allow you to automate tasks without any further need for human intervention.

Applications of Multimodal AI Analytics

Multimodal AI is changing the way different industries engage in improved decision-making, leading to better innovation and user experiences. Businesses are developing more advanced and context-based AI solutions by benefiting from data across modalities.

Here are a few examples of how multimodal AI is benefiting different industries.

i. Healthcare

The use of multimodal AI in healthcare can help enhance patient outcomes while improving the care process. Clinicians can rely on different data types when diagnosing patients and creating their treatment plans. This can include:

  • X-Rays & MRI scans
  • Lab results
  • Patient records

For example, AI models can analyze medical images and text reports to detect anomalies such as tumors. Similarly, the latest lab results and patient history can be integrated with radiology images to predict the progression of Alzheimer’s or cancer.

ii. Retail

The use of such systems in the retail and e-commerce sector is transforming the way businesses and customers interact with each other. Recommended engines and visual search allow customers to upload images or use their phone camera to look up products. Such systems combine text-based product descriptions and reviews and image recognition to generate highly accurate product recommendations.

Some e-commerce AI-based virtual assistants can rely on natural language processing and speech recognition to help customers query order or product details. Such systems can also analyze shopping history and preferences. Multimodal AI can also enhance in-store experiences with the help of voice monitoring. Such monitoring can revolve around customer feedback or behavior on social media or mobile apps.

iii. Education

The use of multimodal AI in the education sector has transformed the way teachers instruct and students learn. AI-based learning systems have emerged that use text, audio, images, and video to address different learning styles. Such systems enable students to engage in more interactive ways.

For example, a student can receive a combination of content in text, image, and video tutorial form. Multimodal AI systems can send such a combination of content formats to explain topics based on the student’s learning preferences.

The system is also finding application in feedback and assessment systems. AI can evaluate grammar and pronunciation by analyzing written and spoken responses in online examinations or language learning apps. This evaluation can be used to provide custom feedback to students. Instructors can, on the other hand, rely on this tech to keep track of each student’s progress and provide customized learning resources.

iv. Customer Service

The application of advanced virtual assistants and chatbots is making a big impact on the quality of customer service offered by companies. Such systems are capable of handling customer queries across different channels, including:

  • Text
  • Voice
  • Visual interface

Multimodal chatbots and virtual assistants integrate NLP, speech recognition, and image recognition to respond to queries. This allows them to offer more useful and relevant answers.

A customer service chatbot used in the retail sector can leverage speech recognition to comprehend voice queries about the availability of products. It can also analyze product images at the same time to offer visual recommendations. They can also rely on sentiment analysis for evaluating customer emotions. Their responses can be adapted to deliver positive customer experiences.

The use of multimodal AI in call centers involves analyzing the customer’s voice tone and conversation content to detect their sentiments. Different input forms are combined to improve the overall customer experience by providing more accurate and responsive interactions similar to human-based interactions.

v. Manufacturing

The use of multimodal AI in the manufacturing sector helps improve efficiency, quality control, and safety. Industrial settings can use such systems to integrate data from different sources, including sensors, cameras, and sound analysis for equipment monitoring, anomaly detection, and production process optimization.

For example, such systems can analyze video feeds from cameras to detect production line issues. It can also use sensor data for tracking machine performance. AI systems can also monitor machinery and identify signs of premature mechanical failure.

The use of multimodal AI in quality control can combine data from cameras and other sources to ensure smooth manufacturing processes. This can include combining data from sensor readings to detect product defects. Such a level of modalities integration can increase precision and benefit decision-making, resulting in the production of high-quality products.

vi. Supply Chain Management

Multimodal AI analytics can have a big impact on operational efficiency. Integration of data sets and data streams from sources such as GPS, sensors, and inventory systems can help you gain a better understanding of your supply chain dynamics. When these modalities come together, it helps create a holistic approach to logistics. This allows your business to make informed decisions, respond promptly to evolving demands and challenges, and streamline processes.

Embracing Multimodal AI

Strategic planning and having access to quality resources are important requirements when it comes to embracing multimodal AI analytics. It is recommended to take the following steps if you want to get started:

i. Evaluate Existing AI Maturity

It is recommended to assess your organization’s current AI capabilities and find areas where multimodal capabilities can deliver value. You can start with pilot projects that bring together just a couple of modalities before more complex implementations can be made.

ii. Build Partnerships for Data Capabilities

Next, consider whether you will be building internal data collection capabilities or building partnerships with specialized providers. When you use comprehensive data catalogs, it can help speed development and ensure quality.

iii. Improving Your Infrastructure

It is important to invest in the infrastructure that can support your organization’s AI requirements. The key areas include:

  • Scalable storage for different data types
  • Data versioning & experiment tracking tools
  • Powerful processors for model training

iv. Build Cross-Functional Teams

Your data analytics team should include data scientists, stakeholders, and domain experts. Build cross-functional teams that understand your business goals and the respective technical requirements.

v. Create Policies

Next, you will be creating policies related to:

  • Data usage
  • Ethical practices
  • Model governance

These policies lay the foundation for implementing multimodal AI systems in your organization. They will impact your critical business decisions.

 

Best Practices for Implementing Multimodal AI

It is further recommended to follow these best practices when implementing multimodal AI systems:

i. Define Use-Case Scenarios

You should avoid the mistake of implementing multimodal AI systems without defining the problems or challenges you want solved. Consider the different use cases where the AI system can help provide greater value over single-modality systems.

ii. Embrace Progressive Development

Instead of developing comprehensive multimodal systems, effective implementation starts with two modalities and then gradually adds more. For example, if you have a retail business, you should start by combining product descriptions and images. You can then gradually implement other modalities, such as behavioral data and sentiments from reviews.

iii. Emphasize Explainability

It is important to implement explainability features, as they can help build trust with all stakeholders. It can also contribute to continual model improvement. Emphasizing explainability can simplify the process of understanding how multimodal AI analytics systems make decisions.

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Conclusion

Multimodal AI-based analytics has brought a shift in the way machines comprehend and interact with data and data sources. As the types of data that are created and collected by businesses evolve, it has become a necessity to be able to process and understand these different modalities at the same time. Effective implementation of multimodal AI requires a strategic approach and investment in quality data, advanced infrastructure, and ethical policies. While there are still challenges, the benefits offered make multimodal AI a crucial technological investment.

If you want to embrace the benefits of multimodal AI data analytics, you can count on the expertise of the Infojini Consulting team. Reach out to book your free consultation today.

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