As businesses increasingly weave AI into the fabric of their operations, a new trend is emerging that could reshape the way companies interact with their customers and employees. Enter “Emotion AI,” a technology predicted to rise to prominence according to PitchBook’s latest Enterprise SaaS Emerging Tech Research report. But as this trend gains traction, it also raises significant questions about its implications.
The Logic Behind Emotion AI
The idea is straightforward: as AI becomes a more integral part of business, from executive assistants to frontline customer service bots, these AI systems must better understand human emotions to function effectively. Imagine an AI assistant deciphering the difference between an angry “What do you mean by that?” and a confused “What do you mean by that?”—a nuance that could be crucial in providing the appropriate response.
Emotion AI is positioned as the next evolution of sentiment analysis, an older technology that attempted to gauge human emotion through text-based interactions, especially on social media. Unlike its predecessor, Emotion AI is multimodal, leveraging visual, audio, and other sensory data combined with machine learning and psychology to detect human emotions during interactions.
Big Tech’s Foray into Emotion AI
Major AI cloud providers have already dipped their toes into the Emotion AI waters. Microsoft Azure’s Cognitive Services offers an Emotion API, while Amazon Web Services provides emotion detection through its Rekognition service—though not without controversy. While these services aren’t entirely new, the rapid rise of AI-driven bots in the workplace suggests that Emotion AI could play a more significant role in the business landscape than ever before.
According to Derek Hernandez, a senior analyst at PitchBook, “With the proliferation of AI assistants and fully automated human-machine interactions, Emotion AI promises to enable more human-like interpretations and responses.” The hardware enabling this technology—cameras and microphones embedded in laptops, phones, and other devices—will become increasingly commonplace. Wearable tech may also open new avenues for Emotion AI applications beyond traditional devices.
A Surge in Emotion AI Startups
This growing interest in Emotion AI has sparked a wave of startups eager to capitalize on the trend. Companies like Uniphore, which has raised $610 million to date, and others like MorphCast, Voicesense, Superceed, Siena AI, audEERING, and Opsis, are at the forefront, securing investments to advance this technology.
However, it’s worth noting that the enthusiasm for Emotion AI is very much a Silicon Valley approach: using technology to solve problems created by technology. But even as AI bots develop automated empathy, there are doubts about the effectiveness of such solutions.
The Skepticism Surrounding Emotion AI
The last time Emotion AI was a hot topic in Silicon Valley—around 2019, when AI/ML was still heavily focused on computer vision—researchers cast doubt on the feasibility of the technology. A meta-review of studies published that year challenged the notion that human emotion can be accurately determined by facial expressions alone. The idea that AI can mimic human emotion detection by interpreting facial cues, body language, and tone of voice might be fundamentally flawed.
Moreover, regulatory challenges could stymie the widespread adoption of Emotion AI. The European Union’s AI Act, for instance, bans the use of emotion-detection systems in certain contexts like education. Additionally, state laws like Illinois’ Biometric Information Privacy Act (BIPA) prohibit the collection of biometric data without explicit consent.
A Glimpse into the AI-Everywhere Future
As Silicon Valley races to build an AI-infused future, we’re left to wonder: will AI bots genuinely understand human emotions, enabling them to excel in roles like customer service, sales, and HR? Or will they struggle with tasks requiring emotional intelligence, relegating us to a future filled with bots that, like Siri circa 2023, are competent but lack emotional depth?
In the end, we might face a choice between two imperfect options: AI bots that attempt to read our emotions, with all the risks and inaccuracies that entails, or bots that perform their tasks without any emotional awareness, potentially missing the nuances that make human interactions effective. As businesses grapple with these questions, the rise of Emotion AI could either herald a new era of more intuitive AI interactions or open a Pandora’s box of ethical and practical challenges.
