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Top 24 Affective Computing (Emotion AI) Use Cases in 2024

Updated on Mar 9
5 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Top 24 Affective Computing (Emotion AI) Use Cases in 2024Top 24 Affective Computing (Emotion AI) Use Cases in 2024

AIMultiple team adheres to the ethical standards summarized in our research commitments.

Thanks to affective computing, also known as emotion AI, computers start to recognize human emotions based on facial expressions, body language, or voice tone. While it might bring controversial surveillance issues, the insights gained from this technology can offer critical improvements for businesses. Companies can understand their customers better and provide more satisfactory services, while they can also monitor their employees’ emotional states to adjust their workload to keep them motivated. 

As emotion AI technology can be introduced to many different industries with various applications, tech giants and startups have started investing in computer vision or voice analysis to recognize human emotions. As a result, between 2023 and 2027, the market for affective computing is anticipated to increase by more than 35% annually. Technology’s applications in different industries are expanded by significant investment in technology where some managers lack knowledge.

Below, we have collected 24 applications of affective computing technology from different resources and categorized them under a wide range of business functions and industries.


Every marketer, at some stage, hears from some marketing guru that marketing should appeal to emotions. Until now, that was a vague, hard-to-measure concept. Now marketers have the ability to put numbers on perceived emotions as well:

  1. Marketing communications: Businesses can analyze what makes their customers engaged and organize their communication strategies accordingly. For example, they can measure customer reactions to their campaigns, products, and services to optimize their marketing strategies.
  2. Market research: Emotion AI can measure consumer reactions to new products and help companies understand what other products do well and what they should do to satisfy customers when they enter a new market.
  3. Content optimization: Affective computing can also help businesses generate contents that resonate well with their customers.

If you want to learn how to analyze your data, read our article on sentiment analysis in marketing.

Customer Service

  1. Intelligent call routing: Businesses can detect angry customers from the beginning of the call, and such calls can be routed to more experienced and well-trained call agents. 
  2. Recommendations during calls: Emotion AI can also provide suggestions about handling customer calls based on similar speech patterns during the conversation.
  3. Continuous improvement: Reviews are time-consuming and completed by only a small share of customers. Amazon sellers share that only 3-5% of their buyers leave product reviews. Like analyzing written reviews, emotion AI can also measure how effective the calls are and if the customer is satisfied at the end of the call by leveraging voice analysis. This data can be used to improve customer service even in cases where customers do not leave reviews.

To find the right AI data partner for your AI projects, check out the following articles:

You can also check our data-driven list of sentiment analysis services. 

Human Resources

  1. Recruitment: Businesses can observe how stressful candidates are and how they communicate emotions during interviews to make better recruitment decisions. Unilever is one of the companies that is currently using emotion AI during job interviews. However, this requires interviewee approval for recording the interview, and HR teams shouldn’t rely too much on the accuracy of affective computing as people can express themselves differently.
  2. Employee training: Affective computing can be used to train employees who interact directly with customers. Employees work with intelligent customer interaction simulations that evolve based on the employees’ responses and emotions, helping them improve their empathy and customer service skills.
  3. Tracking employee satisfaction: HR teams can track employees’ stress and anxiety levels during the job and observe if they are satisfied with their current tasks and workload. However, it also brings an ethical issue of monitoring all employees during work hours and might require their consent to monitor their emotions continuously.


  1. Patient care: A bot can be used not only to remind patients to take their medications but also to monitor their physical and emotional well-being daily to observe any problematic issues.
  2. Medical diagnosis: Affective computing can leverage voice analysis to help doctors diagnose diseases like depression and dementia.
  3. Counseling: Emotion AI can be used in counseling sessions to better track and understand mental states and help doctors support counselees more effectively.

Read our article on sentiment analysis applications in the healthcare industry.


  1. Fraud detection: 27-29% of insurers have admitted to lying to their health and car insurance companies to gain coverage in the US. Insurance companies can leverage voice analysis to prevent such issues and to understand if a customer is lying while submitting a claim.


  1. In-store shopping experience: Emotion AI technology can monitor their customers’ satisfaction levels and reactions while shopping in the store. With the insights gained, retailers can take more effective actions for customer satisfaction.

Check our article on how to benefit from sentiment analysis in the retail industry.

Autonomous driving / Driver assistance

  1. Safety: Automotive companies can leverage computer vision to track drivers’ emotional states while driving. If the driver is too tired, stressed, or angry/sad, it can provide alerts for unsafe driving.
  2. Driving performance: Affective computing can also be used to measure autonomous cars’ driving performance. With cameras and microphones embedded in the vehicle, the technology can monitor the passengers’ emotional state and observe if they seem stressed or satisfied with the driving experience.


  1. Measuring effectiveness: Sensors like video cameras or microphones can be used for students’ emotional states during lessons. Emotion AI can assess how satisfied or frustrated students are with the lessons because a task is too challenging or too simple. As a result, teachers can adapt themselves to tailor class load accordingly. A similar approach can also be used while testing learning software prototypes for online learning.
  2. Supporting autistic children: Another use case in education is to help autistic children recognize other people’s emotions in the school environment.


  1. Testing: Before releasing their games to the market, gaming companies can use affective computing for testing their games. Emotion AI can monitor players’ satisfaction levels, and businesses can improve further to increase player satisfaction.
  2. Adaptive games: Affective computing can leverage computer vision to detect the player’s facial expressions, and the game can adapt to that mental state.


  1. Understanding the general mood of the population: The rise of emotion AI also created new partnerships between technology vendors and surveillance camera providers. The Ministry of Happiness in the United Arabic Emirates has started an initiative to understand the general mood of the population using video analysis cameras in public places.
  2. Tracking/estimating citizen reactions: Governments or political candidates can monitor social media to measure their population’s response to policy proposals and announcements. Political campaigns can also personalize their messages using psychometric models to optimize the emotional reaction of voters. Emotional AI book shares that emotion AI was used as a sentiment analysis tool by Cambridge Analytica in the 2016 US presidential elections.


  1. Integration with IoT: Emotion AI can be integrated into IoT and other smart devices so that these devices can act based on users’ emotional states detected via voice and face analysis. For example, if the customer seems too sweaty, a smart air conditioner might turn on automatically.


  1. Workplace design: Businesses can track their employees in the workplace and conduct sentiment analysis in internal social networks and forum messages to improve physical workspace design and comfort.

If you have questions about effective computing, don’t hesitate to contact us:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources: Traffic Analytics, Ranking & Audience, Similarweb.
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Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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