Integrating Emotional Intelligence into AI

With the constant advancements in artificial intelligence, integrating emotional intelligence (EQ) into AI systems is becoming a key strategy for enhancing both customer and employee interactions. As businesses look to humanize interactions with technology, they would be wise to tap into the experiences of industry pioneers to explore the development and application of emotion AI. Here are some actionable insights these leaders offer for organizations looking to bridge the gap between machine efficiency and human empathy.

Daniel Wax, co-founder of SelfDisrpt, talks about his company's focus on incorporating emotional intelligence into AI, particularly in customer-facing functions.

“At SelfDisrpt, we use AI primarily for top-of-funnel sales and basic customer success inquiries. Our main challenge is to ensure that the AI ​​communicates in a way that not only informs but also makes people feel heard, thus increasing brand appeal,” Wax explains.

As Wax explains, the development process involves training the AI ​​model with curated data to generate “accurate” responses. He emphasizes the importance of training the AI ​​to practice active listening: “It’s critical that the AI ​​is able to recognize and verify the user’s needs before delivering a solution, whether that’s a technical support document or a targeted video response.”

A key aspect of SelfDisrpt's approach is teaching the AI ​​how to deal with situations where information is lacking. Wax highlights a key lesson learned from industry incidents: “There have been cases where AI, like the Air Canada chatbot, provided incorrect information due to training gaps, leading to legal repercussions and customer dissatisfaction. This taught us the importance of training AI to admit when it doesn't have an answer, rather than making up an answer.”

Improving AI through behavioral insights

Jillyn Dillon, founder and executive director of Technology Aloha LLC, spoke about the complexities of integrating emotional intelligence into AI, particularly through understanding human behavior and personality types. “One of the big challenges is the complexity and contextual nature of human communication. We use models such as DiSC to customize AI interactions and enhance our ability to simulate emotional intelligence,” Dillon said.

Dillon uses AI to enhance his own communications by customizing emails to the recipient's personality type and ensuring the message is received as intended: “I'm a questioner (DC) and an INTJ, so I'm usually very direct. Our AI helps me tailor emails to the reader's preferences, reducing friction and improving understanding.”

Looking to the future, both leaders see great potential for emotionally intelligent AI to transform workplace collaboration and customer engagement. Dillon believes AI assistants will play a pivotal role in fostering harmony in the workplace by helping individuals leverage their strengths to improve teamwork.

Wax sees AI being developed for entry-level and intern-level roles, especially in areas like customer success and business development. “The future of emotionally intelligent AI lies in the hands of those who create it,” Wax says. “It's about creating AI that can not only perform tasks but also understand and adapt to human emotions.”

How to build emotional intelligence into AI

Here are some general best practices for businesses and entrepreneurs looking to build emotional intelligence into their AI systems.

1. Start with a clear goal: Identify specific areas within your business where EQ can significantly improve interactions, such as customer service, sales, or employee management. Focus your initial efforts on these areas for maximum impact.

2. Choose the right data for trainingAs Wax pointed out, training your AI with EQ starts with selecting the curated data your AI will use to learn from and refine its responses: Make sure the data is diverse and comprehensive so it covers different aspects of human emotional expression.

3. Practice active listening techniques: Train AI systems to practice active listening. This involves programming them to recognize emotional cues and respond in a way that validates the user's feelings, which, as Wax emphasizes, makes users feel heard and understood.

4. Teach AI to embrace the unknown: Avoid potential false alarms by training your AI to be aware of its limitations. As seen in the Air Canada example, it is crucial to communicate openly when the AI ​​lacks the information it needs to respond accurately, and to transfer queries to humans when necessary.

5. Use behavioral models to enhance interactionsAs Dillon suggests, incorporating psychological and behavioral models such as DiSC will allow the AI ​​to customize responses based on the user’s personality type and behavioral patterns, further personalizing interactions.

6. Continuously update and improve your AI modelsEmotional intelligence in AI isn't a set-it-and-forget implementation: you regularly update the AI ​​with new data and continually refine the algorithms to adapt to new emotional nuances and communication styles.

7. Use AI for personalized communications: Use AI to customize communications, as Dillon does with email. This can span marketing materials, responses to customer feedback, and internal communications, all tailored to the recipient's emotional and cognitive preferences.

8. Prioritize ethical considerations: Always consider the ethical implications of AI decisions. Ensure that your use of AI that understands and responds to human emotions complies with privacy standards and respects user consent.

9. Foster collaboration between AI and human teams: Design systems where AI supports and enhances human work, not just replaces it. This includes AI suggesting communication strategies in real time and providing emotional insights during meetings and customer interactions.

10. Monitoring and measuring impact: Regularly evaluate how integrating EQ into AI is impacting customer satisfaction, employee engagement, and overall business outcomes. Use these metrics to adjust your strategy and continuously improve your system.

By implementing these strategies, businesses and entrepreneurs can effectively integrate emotional intelligence into their AI systems, resulting in more meaningful and productive interactions both internally and with customers.

As artificial intelligence continues to evolve, integrating emotional intelligence into AI represents a fundamental shift in how we think about the role of machines in society. Wax and Dillon's insights highlight the importance of developing AI systems that respect and understand human nuances and ensure technology enhances the human experience rather than detracts from it.