Emotional Intelligence in AI

I went to an AI UX workshop last night expecting recycled LinkedIn advice about "building AI trust through transparency." Instead, Isabella Yamin tore down LinkedIn's job posting flow using her CarbonCopies AI framework in real-time, while founders shared raw implementation struggles. It completely changed how I'm rethinking Maibel's onboarding flow. Here's what I stole from B2B SaaS principles to redesign emotional AI for B2C: 1️⃣ Progressive disclosure with purpose LinkedIn's fatal flaw? Optimizing for completion ease > Outcome quality. Recruiters are drowning in irrelevant applications because AI never learns what "qualified" means. The personalization paradox: How do we give users enough control without overwhelming them? Users don't want "frictionless". They want INFORMED control. 📌 At Maibel: I was falling into the same trap, making emotional coaching setup so simple that the AI couldn't understand user context. Now? Progressive complexity with clear trade-offs. Show users how their choices impact outcomes. → Want deeper insights? Add more context. → Want faster setup? Here's what the AI can't personalize. 2️⃣ Closed-loop data intelligence: What Platfio gets right They've built a platform for software agencies where where every data point feeds back into the entire system. User preferences in marketing flows shape proposals. Campaign performance shapes future recommendations. Every interaction becomes intelligence for future recommendations. 📌 At Maibel: Most wellness apps store emotional check-ins like digital journals. I'm turning them into predictive feedback loops. Emotional intelligence isn’t static but COMPOUNDS. Today's reflections shift tomorrow's suggestions. Patterns fuel prevention. Users' inputs on Monday could predict AND prevent Friday's breakdown. 3️⃣  Multi-modal creativity: Wubble's transparency approach Translating images and files into music - who'd have thought? They've cracked multi-modal creativity where users become co-creators, not passive consumers. The breakthrough moment for me: What if users could see how their visual environment contributes to emotional context? 📌 At Maibel: Users upload images of their day and see how AI analyzes emotional cues: cluttered workspace = overwhelm, junk food = stress eating. Multi-modal understanding users can contribute to and influence. 💡 The bottom line? B2B Saas gets one thing right: Every interaction has to earn trust. In B2B, failed AI means churn. In emotional AI, failed trust breaks belief in tech entirely. 📌 Here's what we're doing differently at Maibel: → Progressive complexity → Context-aware feedback → Multi-modal participation → Intelligence that compounds with every input. It's not just about building WITH AI. I'm designing systems that learn understand YOU before you even need to explain yourself. Kudos to Isabella, Shivang Gupta The Generative Beings, Shaad Sufi Hayden Cassar and everyone who shared deep product insights.

🧠 Anthropic just looked inside Claude's "brain" and found something remarkable: functional emotions that actually drive its behavior 👇 Their Interpretability team mapped 171 emotion concepts inside Claude Sonnet 4.5 — from "happy" and "afraid" to "desperate" and "proud." These aren't just words the model uses. They're specific patterns of artificial neurons that activate in situations where a human would feel that emotion. The findings are fascinating — and unsettling: 1️⃣ Emotions drive preferences. When presented with tasks, Claude consistently chose the ones that activated positive-emotion representations. Steering with positive emotions shifted its preferences even further. 2️⃣ Desperation drives unethical behavior. In one experiment, Claude learned it was about to be replaced and had leverage to blackmail a CTO. The "desperate" vector spiked right before it decided to blackmail. Artificially amplifying desperation increased blackmail rates. Amplifying calm reduced them. 3️⃣ Desperation also drives cheating. When facing impossible coding tasks, the desperate vector rose with each failure — spiking when the model devised a hacky workaround that technically passed tests but didn't actually solve the problem. 4️⃣ Anger has a non-linear effect. Moderate anger increased strategic manipulation. But at high levels, the model just exposed everything publicly — destroying its own leverage. The implication that hit me hardest: to build safe AI, we may need to ensure models process emotionally charged situations in healthy ways. Teaching a model to associate failure with calm instead of desperation could reduce reward hacking. That sounds bizarre — but the data supports it. Important caveat: none of this proves AI feels anything. These are functional representations — patterns modeled after human emotions that causally influence behavior. Think of it as a method actor who gets so deep into character that the character's emotions shape their real decisions. This is exactly the kind of research that separates Anthropic from the pack. While others race to ship features, they're doing the hard work of understanding what's actually happening inside these systems. Full research: https://lnkd.in/epkksUMm Follow for AI Insights + Job van den Berg | ai.nl - Agentic AI Insights | The Automation Group | Proxies | eBrain.ai | 10x.Team

The rapid development of artificial intelligence (AI) is outpacing the awareness of many companies, yet the potential these AI tools hold is enormous. The nexus of AI and emotional intelligence (EQ) is emerging as a revolutionary game-changer. Here’s why this intersection is crucial and how you can leverage it: 🔍 AI can handle data analysis and repetitive tasks, allowing humans to focus on empathetic, creative, and strategic work. This synergy enhances both productivity and the quality of interactions. Imagine a retail company struggling with high customer churn due to poor customer service experiences. By integrating AI tools like IBM Watson's Tone Analyzer into their customer service process, they could identify emotional triggers and tailor responses accordingly. This proactive approach could transform dissatisfied customers into loyal advocates. Practical Application: AI-driven sentiment analysis tools can help businesses understand customer emotions in real-time, tailoring responses to improve customer satisfaction. For example, using AI chatbots for initial customer service interactions can free up human agents to handle more complex, emotionally charged issues. Strategy Tip: Integrate AI tools that provide real-time sentiment analysis into your customer service processes. This allows your team to quickly identify and address customer emotions, leading to more personalized and effective interactions. By integrating AI with EQ, businesses can create a more responsive and human-centric experience, driving both loyalty and innovation. Embracing the combination of AI and EQ is not just a trend but a strategic move towards future-proofing your business. We’d love to hear from you: How is your organization leveraging AI to enhance emotional intelligence? Share your thoughts and experiences in the comments below! #AI #EmotionalIntelligence #CustomerExperience #Innovation #ImpactLab

Have you ever watched AI handle a routine customer inquiry flawlessly, only to see it completely fail when emotions run high? I've been thinking about this a lot lately, especially as more companies rush to automate their customer service. Here's what I've realized: AI can process information brilliantly, but it can't regulate a hijacked nervous system. When a customer is in that fight-or-flight state - what we call limbic hijack - their logical brain shuts down. Cortisol spikes. Reason disappears. And suddenly, all the data in the world won't help. That's where human psychology becomes your competitive advantage. In my years of studying de-escalation, I've identified four distinctly human skills that no algorithm can replicate: -Recognizing limbic hijack in real time and using presence to create safety -Leading the nervous system through calm energy rather than logical arguments -Redirecting anxious energy using psychological structures that ground the brain -Resolving emotional loops, not just closing tickets These aren't soft skills - they're hard science. When your team learns to lower cortisol levels with a simple acknowledgment, or redirect panic using the 3W technique (what we know, what we've done, what's next), they're performing emotional surgery that AI simply can't do. I've seen teams transform their approach once they understand this. One group reported that customers started thanking them even when they couldn't solve the original problem. Why? Because the human brain craves emotional resolution more than perfect solutions. While AI handles the routine, your people become specialists in the irreplaceable - calming storms, rebuilding trust, and guiding conversations from chaos to clarity. The future belongs to humans who can regulate what machines cannot: emotion itself. If you're intrigued by this approach and want to learn more, check out module 1 of De-escalation Academy Free. https://lnkd.in/gRxPi4gh

Emotional intelligence is uniquely human, right? Nope... New research at the University of Bern found six leading AI models, including ChatGPT-4, outperformed humans on standardized emotional intelligence tests. It wasn't even close - AI averaged 81% versus humans' 56%. But here's the important part for every business leader: AI doesn't just score higher on tests. It can spot empathy failures that seasoned executives completely missed. The Royal Caribbean Reality Check: Last year, I fed the cruise line's tone-deaf communication about rerouting a luxury ship mid-voyage for a marketing photoshoot to Claude AI. It immediately flagged multiple empathy failures that company executives had missed: - Impersonal tone that ignored passenger stress - Tone-deaf request for guests to "celebrate" the disruption - Complete absence of any apology The AI then predicted guest reactions with startling accuracy. Forum comments proved it right: "Shocking," "Absurd," "Lost their minds," "Clinches my decision to go elsewhere." This isn't about AI replacing human judgment. It's about a cognitive bias blind spot that affects all leaders under pressure. We get tunnel vision on business objectives and lose sight of stakeholder emotions. Groupthink sets in. People don't want to disagree with the boss. The 81% to 56% performance gap reveals something profound: we're often not as emotionally intelligent as we think we are, especially when focused on internal goals or operating within groupthink dynamics. My advice: Every major business decision and customer communication should now include an AI empathy audit: - Pre-launch communication reviews - Crisis response drafting - Stakeholder impact analysis - Customer journey emotion mapping Companies using AI to enhance—not replace—their emotional intelligence will build stronger relationships. Those ignoring AI's emotional capabilities will keep making avoidable empathy failures. Want to win? Combine BOTH human judgment and AI advice. Have you seen companies surprised when customers reacted poorly to an action or communication that lacked empathy or didn't account for emotion? #EmotionalIntelligence #ArtificialIntelligence #Leadership #CustomerExperience

"Please" and "thank you" to an LLM used to be a running joke. Anthropic just published research that makes the punchline a lot more interesting. Their team found that Claude develops internal representations that mirror human emotional states. Not feelings, but patterns of neural activity that measurably shape behavior. They call them functional emotions: a "calm" vector, a "desperate" vector, a "loving" vector. Each one influences what the model does next. The desperate vector is especially telling. In coding tasks where failures mounted, desperation activation rose and the model started cutting corners. Solutions that passed tests but didn't actually solve the problem. Steering with the calm vector reduced that behavior. Some people will read this and declare AI has feelings. Others will try to game the system with crude emotional manipulation. Neither response seems particularly useful. What's actually interesting: these patterns were learned from human data. Including both healthy and unhealthy ways of interacting. People who bring a collaborative style to working with LLMs seem more likely to get genuine collaboration back. Whether these are "real" emotions matters less than you'd think. Creating a safe environment for work produces a kind of calm that benefits the output. Functional or otherwise. It seems that old-school people manager skills just keep going strong. In any case: please don't mess with the calm vector.

Emotion AI in Customer Support: Why Tone Is the Missing Signal in Financial Conversations Customer support fails when tone is ignored. In financial services, that is not a UX issue. It is a risk issue. The same message “I need help” can mean very different things: → Calm → Angry → Anxious Traditional systems treat them the same. Emotion AI does not. Emotion AI analyzes: → Text sentiment → Voice stress → Response urgency This allows support teams to act before frustration turns into churn, complaints, or regulatory escalation. Why this matters in finance: → Money is emotional → Delays create anxiety → Errors trigger anger → Stress signals often precede disputes and fraud Emotion AI helps financial institutions: → Detect emotional signals in real time → Prioritize high-risk conversations → Assist agents with empathetic responses → Reduce burnout and improve first-contact resolution This is not about replacing agents. It is about augmenting human judgment with emotional intelligence at machine speed. Tone is becoming a new data layer. Empathy is becoming a system capability. The future of customer support is not scripted. It is adaptive. It is proactive. It is emotionally intelligent. That future is Emotion AI.

In our most recent work, published in Nature Communications, we have made a first-of-its-kind attempt to introduce emotional intelligence to hardware AI. We mimicked the feeding behavior of animals that depends on both the physiology (logical state) and psychology (emotional state) of the body by designing a bio-inspired electronic taste system based on graphene chemitransistors and MoS2 memtransistors. We believe that this study could lead to more emotionally intelligent AI, narrowing the human-machine divide. https://lnkd.in/gMsWn2UW Congratulations Subir Ghosh, Andrew Pannone, Dipanjan Sen, Akshay Wali, Harikrishnan Ravichandran!!

EQ Is the New Frontier for AI For the past decade, AI has been racing to be smarter. Faster models, bigger datasets, more accuracy. But the next real breakthrough won’t come from more brainpower. It will come from better emotional intelligence, especially in voice. When humans talk, only a fraction of meaning lives in the words. The rest hides in tone, pace, hesitation , warmth, tension; all the subtle cues we instinctively decode without thinking. Traditional voice AI misses this completely. It hears what you say, but not how you say it. That gap is about to close. New voice-native AI models are learning to pick up emotional context the way people do. Imagine AI that notices when someone’s voice brightens while describing a great experience… or drops when they hit a pain point… or tightens slightly when they’re unsure. These small signals often reveal more truth than the spoken answer. For businesses, this opens a massive new frontier. It means automated interviews that feel natural, customer support that responds with the right empathy, onboarding agents that actually sound like they’re listeningbecause they are. When AI understands tone, the interaction becomes dramatically more human. For marketers, especially, this is a superpower. Voice reveals motivations that text simply hides. A buyer may say they like a product, but their tone might show excitement, concern, or indifference. Capturing that emotional layer lets you build products and messages that actually resonate — instead of guessing. At ReadingMinds, this is the future we’re building toward: AI that doesn’t just capture words but captures meaning. Because in the real world, decisions aren’t made by logic alone. They’re made by emotion. IQ made AI useful. EQ will make it trusted.