Emotion AI - a market with many perspectives
Emotion AI aims to capture, interpret and respond to human emotions using various technologies such as computer vision (CV), speech analysis and biometric sensors. The goal of Emotion AI is to create systems that accurately analyse, process and respond appropriately to users' emotions. But why is this actually important?
Providing responsive solutions using Emotion AI
Firstly, one of the key drivers of the Emotion AI market is the increasing demand for stress management solutions for employees working in hazardous environments. There is currently a gap in the market for off-the-shelf solutions, which gives technology and service providers the opportunity to develop innovative solutions.
Secondly, also driving AI in the realm of emotions is the increasing adoption of AI avatars in various industries. These avatars can be used to create new, literally "engaging customer experiences" that go beyond simple interactions. For example, an AI avatar can be used in customer service to provide personalised support and assistance, or in healthcare to provide emotional support to patients. The challenge for technology and service providers is now to focus on developing multimodal technology approaches. This includes identifying complementary technologies such as computer vision and speech analytics that can open up new use cases for customers. In addition, providers should capitalise on the unmet demand for stress management and other employee experience opportunities by offering trial versions to their customers.
Technology providers with potential
An overview of vendors that are active and successful in this field is provided by Gartner's market overview of the emotion AI industry published at the end of December 2022 (Gartner®, "Competitive Landscape: Emotion AI Technologies", Annette Zimmermann, Roberta Cozza, 29 December 2022.) "Emotion artificial intelligence (EAI) vendors are integrating combinatorial technologies such as biosensors and audio analytics into their offerings to enable more robust solutions and use cases," according to the authors of the Gartner® research. In the report, Gartner examines five main categories of emotion AI technologies:
- Computer vision-based facial expression analysis
- Speech analysis
- Biometric/other sensors
- Natural language technologies (NLT)/emotional text analysis
- AI avatars
Among others, VIER is also mentioned in this context as a representative provider for NLT/emotional text analysis and audio-based speech analysis - a reason for us to be happy, of course. VIER's goal: We help companies to use the possibilities of Emotion AI to improve their business. The potential for this is significant: Gartner predicts “By 2030, 75% of conversational AI customer-facing business applications will be emotion AI, up from less than 5% in 2022.“. This is a clear indication of the strong potential growth and importance of this technology in the near future.
Analysis of facial expression and voice
Computational visual analysis of facial expressions is one of the most widely used techniques in emotion AI. It uses convolutional neural networks (CNNs) to detect a combination of emotional states based on facial features and muscle movement recognition. This analysis can be complemented by posture and body movement analysis.
Voice analysis, on the other hand, usually uses so-called "recurrent neural networks" (RNNs), especially with a long short memory (LSTM) architecture. The model is trained to recognise a combination of emotional states based on markers in the human voice such as pitch and velocity. Most technology providers can do this in a language-independent way - including tonal languages such as Chinese or Thai.
At VIER, we believe that Emotion AI technology has the potential to revolutionise areas such as IR, HR, PR, marketing and sales. The technology can be used to improve customer interactions, enhance the employee experience and gain valuable insights into customer and employee emotions. I list some application examples below:
- · In the field of public relations, emotion AI can be used to monitor and analyse conversations on social media. This provides valuable insights into how investors think about a company. This can help companies improve their IR strategy.
- In human resources, AI can be used to analyse employee interactions. This provides valuable insights into workforce engagement and satisfaction, helping to identify potential issues and improve the employee experience.
- In public relations, emotion AI can be used to perceive and analyse public opinion towards a company or product. This enables companies to identify potential problems and improve their PR strategy.
- In marketing and sales, emotion AI can be used to personalise interactions with customers to create a more engaging and satisfying experience. In addition, AI can analyse customer sentiment to provide valuable insights into customer preferences and behaviours.
Choosing the right strategy
In summary, Emotion AI is an emerging field with a lot of potential. There are applications in various industries, from customer service to healthcare, and it is expected to play an important role in the near future. The technology and service providers that can identify new use cases and develop multimodal technology approaches will be well positioned to succeed in this market. VIER is named by Gartner as a Representative Vendor in the field of Emotion AI. We believe that this is a confirmation of our capabilities and our strategy. So proud!
*Gartner, “Competitive Landscape: Emotion AI Technologies”, Annette Zimmermann, Roberta Cozza, December 29, 2022.
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