Top 10 Emotional AI Examples & Use Cases in 2025

The emotion detection and recognition (EDR) market is estimated to reach at ~$50 Bn in 2024, and is expected to reach ~$173 Bn by 2031.¹ Emotion detection and recognition rely on emotion AI to identify, process, and simulate human feelings and emotions. And businesses have been leveraging emotion AI in numerous applications, ranging from customer service to recruiting.

Explore examples and success stories of businesses leveraging emotion AI in marketing, customer service, healthcare, education, and gaming:

Marketing

1. Brand exposure

Realeyes conducted a study on 130 car ads collected from social media platforms to understand what video features gain audience attention.²

The study revealed a link between high emotional intelligence and social media success, showcasing how emotional AI systems and facial coding algorithms enhance brand performance. For instance, Volkswagen’s “The Force” ad, using humor and narrative, outperformed Ford Fiesta’s product-focused ad in engaging human emotions and boosting customer sentiment.

By leveraging emotion recognition and behavioral signals, brands can amplify social interactions, improve customer experience, and foster positive emotional connections.

Figure 1. Emotional analysis of automotive ads

Source: Coherent Market Insight.³

2. Subway ads

Brazil’s Yellow Line of the Sao Paulo Metro deployed an emotion AI analytics technology to optimize their subway interactive ads according to people’s emotions. This emotion AI software is integrated into security cameras to measure face metrics, such as gender, age range, gaze through rate, attention span, emotion, and direction.

These metrics enabled advertisers to classify people’s expressions into happiness, surprise, neutrality, and dissatisfaction, and change their ads accordingly. ⁴

3. Travel recommendation

Skyscanner, a metasearch engine and travel agency, deployed an emotion AI technology to their Russian website.⁵

The face analysis tool leverages emotion AI to anonymously detect and measure facial expressions like happiness, sadness, disgust, surprise, anger, and fear. Skyscanner used this technology to create an engaging experience with their customers as they book their flights such that a customer would take a picture of themselves and the API will process it, and display face results along with a targeted travel recommendation. For example, if a user displays “sad” emotions, the API would suggest a “fun” travel destination.

Customer service

4. Customer matching to the right agent

A European bank partnered with an emotional AI technology company to maximize the effectiveness of its call center agents. This technology leverages emotional AI and natural language processing to analyze the behavioral signals from user voices, reactions, word choice, and engagement.

The bank deployed the AI-enabled agent-customer matching technology to route customer calls based on their previous calls recorded in their CRM profile data (e.g. non-performing loans (NPLs) historical data, and metadata on actual payments). The call success ratio improved by nearly 11% when customers were matched with the right agent.⁶

Figure 2. Rate of Successful Calls in Bank’s Call Center

Source: Behavioral Signals⁷

You can work with an AI data service to overcome the hassle of gathering AI training data. Check out the following articles:

• Top 6 Data Collection Methods for AI & Machine Learning
• Data Crowdsourcing Platform: 10+ Companies & Criteria

Also, check our data-driven list of sentiment analysis services to determine which option satisfies your company’s needs.

5. AI-based agent coaching

MetLife, a US insurance corporation, implemented an emotional AI coaching solution in 10 of its U.S. call centers to provide real-time guidance to agents while speaking to a customer.

This emotion AI technology leverages Signal-Based Machine Learning, a neural network model that enables incremental, real-time inferences on streamed signal data to understand emotional states and implications during a conversation, and provide real-time conversation tips and resolutions to agents. Deploying an emotion AI coaching solution enabled MetLife to obtain:⁸

• A higher NPS score by 14 points
• 5% improvement in “Perfect Call” scores
• 6.3% improvement in issue resolution
• 17% reduction in call handling time

Education

6. Tutoring program optimization

Vedantu, an Indian online tutoring platform, leveraged an emotion AI solution to optimize their educational content and strategy. This technology relies on eye tracking and facial coding algorithms to analyze emotional triggers and map user journeys.

Student sessions were recorded and passed through the eye-tracking API to generate engagement, attention, and fatigue metrics for both students and tutors. This emotion AI solution claims that the metrics were 92% in correlation with existing ratings. These metrics enabled the tutoring platform to:⁹

• identify areas of improvement in their content and presentation method
• increase students’ attention span

Healthcare

7. Covid-19 crisis monitoring

An emotion AI analytics solution developer created a Coronavirus Panic Index to track consumer sentiments and trends about the pandemic and spread of Covid-19.¹⁰ This solution uses emotional AI to analyze emotional data from social media, blogs, and forums, predicting how populations in specific areas may respond to pandemic-related events.

The insights guide businesses and governments in crafting virus-containment strategies, raising Covid-19 awareness, and addressing physical and mental healthcare needs.

8. Disaster and emergency management

SONAR, a disaster and emergency communication decentralized application, leverages Kairos emotion AI solution to deliver medical help during the Caribbean hurricane.¹¹ Kairo’s emotion AI solution can detect, identify, and verify faces, and understand the liveliness of a face.

SONAR utilized Kairos such that during a disaster, a person will take a selfie which can be scanned, identified, and linked to their personally identifiable information (PII), and their medical condition can be detected at the time of taking the image. This information is then expedited to emergency management and medical agencies to provide help.

9. Blood pressure detection

The American Heart Association developed an app using NuraLogix emotion AI algorithms to detect blood pressure levels from 2-minute videos. The algorithm extracts blood pressure features from:

• facial blood-flow signals (light near skin surface which reflects hemoglobin concentration
• physical characteristics (age, weight, skin tone)

The model was able to detect blood pressure with ~95% accuracy.¹²

Figure 3. Blood pressure signals calculated by AI systems

Source: Smartphone-based blood pressure measurement using transdermal optical imaging technology.¹³

Gaming

10. Biofeedback gaming

Flying Mollusk, a game development studio, leveraged an emotion AI technology to develop an adaptive psychological thriller video game “Nevermind“.¹⁴ The game deploys emotion AI to understand the gamer’s feelings from their webcams and adjust the game experience accordingly.

For example, if a player exhibits stressful behavior, the game atmosphere may get darker and stressful situations may be displayed, such as a flooding room or a falling roof. Same way, when the player calms down, the game will project a calm atmosphere.

To explore emotion AI in detail, feel free to read our in-depth articles:

• What is Affective Computing / Emotion AI: In-depth Guide
• 24 Affective Computing (Emotion AI) Applications / Use Cases

You may also be interested in our sortable/filterable lists of:

• AI Platforms
• AI Consultants
• AI Development Services

External Links

• 1. Emotion Detection and Recognition (EDR) Market Forecast, 2032.
• 2. How Emotional vs Rational Automotive Ads Drive Better Social Media Success - Realeyes. Realeyes
• 3. Emotion Detection and Recognition (EDR) Market Forecast, 2032.
• 4. AdMobilize Audience Analytics Solution Installed on the Main Subway System in Brazil’s Largest City - Sound & Video Contractor.
• 5. Interfaces, Interactions, Time, and the Frontline Nexus: Foundational Constructs and Focus for the Field of Organizational Frontlines - Jagdip Singh, R Gary Bridge, 2023 .
• 6. AI-MC Case Study for Financial Group. Behavioral Signals
• 7. AI-MC Case Study for Financial Group. Behavioral Signals
• 8. The Rise of the AI Coaching System: Powering the Empathic Enterprise. Cogito Corp
• 9. Entropik | AI-Powered Consumer & User Experience | 4x Faster Insights.
• 10. How One Company Is Using A.I. to Predict Panic Over Covid-19. Cognovi Labs
• 11. The Cyberia Tech - Software Solutions At The Highest Quality. The Cyberia Tech
• 12. Luo, H., Yang, D., Barszczyk, A., Vempala, N., Wei, J., Wu, S. J., … & Feng, Z. P. (2019). Smartphone-based blood pressure measurement using transdermal optical imaging technology. Circulation: Cardiovascular Imaging, 12(8), e008857.
• 13. Luo, H., Yang, D., Barszczyk, A., Vempala, N., Wei, J., Wu, S. J., … & Feng, Z. P. (2019). Smartphone-based blood pressure measurement using transdermal optical imaging technology. Circulation: Cardiovascular Imaging, 12(8), e008857.
• 14. Nevermind - Affectiva : Affectiva. iMotions