The rise of “emotion AI,” which aims to equip artificial intelligence with the ability to understand human emotions, is becoming a notable trend in business software, according to PitchBook’s recent Enterprise SaaS Emerging Tech Research report. This technology is seen as a step beyond sentiment analysis, promising more nuanced interpretations of human interactions by using multimodal inputs like visual, audio, and psychological data. Despite its potential, the effectiveness and ethical implications of emotion AI remain questionable.

The concept behind emotion AI is straightforward: as businesses increasingly rely on AI for customer service, sales, and other interactions, these AI bots need to distinguish between different emotional cues, such as anger and confusion. Emotion AI intends to make AI assistants more human-like in their responses by analyzing various signals, from facial expressions to voice tones. Major cloud providers, including Microsoft and Amazon, already offer services with emotion AI capabilities, making these tools more accessible to developers.

Derek Hernandez, a senior analyst at PitchBook, notes the growing importance of emotion AI with the proliferation of AI assistants and automated human-machine interactions. Hernandez highlights the role of cameras, microphones, and wearable devices in capturing the necessary data for emotion detection. This growing interest has spurred investment in startups like Uniphore, MorphCast, Voicesense, and others, which focus on developing emotion AI technologies.

However, the push toward emotion AI brings with it significant challenges. Critics argue that the technology might be inherently flawed. Research published in 2019 suggests that human emotions cannot be accurately determined by facial movements alone, challenging the basic premise of emotion AI. Moreover, regulatory concerns, such as those outlined in the European Union’s AI Act, which restricts emotion detection in specific contexts, could limit its application. U.S. state laws like Illinois’ Biometric Information Privacy Act (BIPA) further complicate the use of biometric data without explicit consent.

The debate around emotion AI offers a glimpse into the potential future of AI in the workplace. While emotion AI could enhance customer service, sales, and HR tasks by making interactions more personalized and empathetic, it raises questions about privacy, ethical implications, and the actual effectiveness of such technology. As companies continue to embed AI across various aspects of business operations, the success and acceptability of emotion AI will likely depend on addressing these challenges.