How to Balance AI Innovation With Human Interaction

I'm knee deep this week putting the finishing touches on my new Udemy course on "AI for People Managers: Lead with confidence in an AI-enabled workplace". After working with hundreds of managers cautiously navigating AI integration, here's what I've learned: the future belongs to leaders who can thoughtfully blend AI capabilities with genuine human wisdom, connection, and compassion. Your people don't need you to be the AI expert in the room; they need you to be authentic, caring, and completely committed to their success. No technology can replicate that. And no technology SHOULD. The managers who are absolutely thriving aren't necessarily the most tech-savvy ones. They're the leaders who understand how to use AI strategically to amplify their existing strengths while keeping clear boundaries around what must stay authentically human: building trust, navigating emotions, making tough ethical calls, having meaningful conversations, and inspiring people to bring their best work. Here's the most important takeaway: as AI handles more routine tasks, your human leadership skills become MORE valuable, not less. The economic value of emotional intelligence, empathy, and relationship building skyrockets when machines take over the mundane stuff. Here are 7 principles for leading humans in an AI-enabled world: 1. Use AI to create more space for real human connection, not to avoid it 2. Don't let AI handle sensitive emotions, ethical decisions, or trust-building moments 3. Be transparent about your AI experiments while emphasizing that human judgment (that's you, my friend) drives your decisions 4. Help your people develop uniquely human skills that complement rather than compete with technology. (Let me know how I can help. This is my jam.) 5. Own your strategic decisions completely. Don't hide behind AI recommendations when things get tough 6. Build psychological safety so people feel supported through technological change, not threatened by it 7. Remember your core job hasn't changed. You're still in charge of helping people do their best work and grow in their careers AI is just a powerful new tool to help you do that job better, and to help your people do theirs better. Make sure it's the REAL you showing up as the leader you are. #AI #coaching #managers

I've watched companies crash and burn. Duolingo is a prime example. The company thought AI was the answer. But they got it all wrong. Their "AI-first" strategy blew up in their faces. They lost 6.7 million TikTok followers and 4.1 million on Instagram. That's a $7 billion lesson in what happens when you replace people instead of partnering with them. CEO Luis von Ahn decided to cut contractors. He claimed they would only hire if teams couldn't automate their work. Predictably, this led to chaos. Employees revolted. Users were furious. Social media went silent. 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱: • They tossed out human expertise instead of building on it. • They saw AI as a way to save money, not as a partner. • They spread fear, not hope. • They ignored that culture and creativity can't be replaced by machines. 𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗴𝗲𝘁𝘁𝗶𝗻𝗴 𝗶𝘁 𝗿𝗶𝗴𝗵𝘁 𝗸𝗻𝗼𝘄 𝘁𝗵𝗶𝘀: AI is rewriting the rules of business, but it should only be harnessed when it is integrated with human skills, not when it replaces them. They tackle biases in AI to make sure their systems serve everyone. Microsoft found that teams using AI perform better than those that don't. 𝗛𝗲𝗿𝗲'𝘀 𝗵𝗼𝘄 𝘀𝗺𝗮𝗿𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗶𝗻𝗴 𝗔𝗜 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝘄𝗮𝘆: 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆: • Treat AI agents like new team members, onboard them, assign ownership, measure performance. • Set clear human-agent ratios for each function. • Invest in AI literacy training across all levels. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗿𝗲𝗽𝗹𝗮𝗰𝗲𝗺𝗲𝗻𝘁 • Use AI for 24/7 availability and processing power, things humans can't provide • Keep humans in charge of judgment, creativity, and high-stakes decisions • Create "thought partner" relationships where AI challenges thinking leads to ideas 𝗦𝗰𝗮𝗹𝗲 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝗶𝗰𝗮𝗹𝗹𝘆 • Move beyond pilots to organization-wide adoption • Start with functions farthest from your competitive edge • Continuously evaluate and adjust your AI tools The truth is clear. Companies that fail to integrate AI smartly will be left behind. This concerns how AI will change your workforce and how you will lead that change. Will you lift your team up with AI, or will you create fear like Duolingo did? What's your experience with AI integration? Are you seeing partnership or replacement in your industry? The future belongs to those who master human-AI collaboration. Those who don't risk becoming the next cautionary tale. #AIvsEI #BetterTogetherAgency #Duolingo #HumanCentric  

Most AI implementations can be technically flawless—but fundamentally broken. Here's why: Consider this scenario: A company implemented a fully automated AI customer service system, and reduced ticket solution time by 40%. What happens to the satisfaction scores? If they drop by 35%, is the reduction in response times worth celebrating? This exemplifies the trap many leaders fall into - optimizing for efficiency while forgetting that business, at its core, is fundamentally human. Customers don't always just want fast answers; they want to feel heard and understood. The jar metaphor I often use with leadership teams: Ever tried opening a jar with the lid screwed on too tight? No matter how hard you twist, it won't budge. That's exactly what happens when businesses pour resources into technology but forget about the people who need to use it. The real key to progress isn't choosing between technology OR humanity. It's creating systems where both work together, responsibly. So, here are 3 practical steps for leaders and businesses: 1. Keep customer interactions personal: Automation is great, but ensure people can reach humans when it matters. 2. Let technology do the heavy lifting: AI should handle repetitive tasks so your team can focus on strategy, complex problems, and relationships. 3. Lead with heart, not just data (and I’m a data person saying this 🤣) Technology streamlines processes, but can't build trust or inspire people. So, your action step this week: Identify one process where technology and human judgment intersect. Ask yourself: - Is it clear where AI assistance ends and human decision-making begins? - Do your knowledge workers feel empowered or threatened by technology? - Is there clear human accountability for final decisions? The magic happens at the intersection. Because a strong culture and genuine human connection will always be the foundation of a great organization. What's your experience balancing tech and humanity in your organization?

Just read a fascinating piece by Tetiana S. about how our brains naturally "outsource" thinking to tools and technology - a concept known as cognitive offloading. With AI, we're taking this natural human tendency to a whole new level. Here's why organizations are struggling with AI adoption: They're focusing too much on the technology itself and not enough on how humans actually work and think. Many companies rush to implement AI solutions without considering how these tools align with their teams' natural workflow and cognitive processes. The result? Low adoption rates, frustrated employees, and unrealized potential. The key insight? Successful AI implementation requires a deep understanding of human cognition and behavior. It's about creating intuitive systems that feel like natural extensions of how people already work, rather than forcing them to adapt to rigid, complex tools. Here are 3 crucial action items for business leaders implementing AI: 1) Design for Cognitive "Partnership": Ensure your AI tools genuinely reduce mental burden rather than adding complexity. The goal is to free up your team's cognitive resources for higher-value tasks. Ask yourself: "Does this tool make thinking and decision-making easier for my team?" 2) Focus on Trust Through Transparency: Implement systems that handle errors gracefully and provide clear feedback. When AI makes mistakes (and it will), users should understand what went wrong and how to correct course. This builds long-term trust and adoption. 3) Leverage Familiar Patterns: Don't reinvent the wheel with your AI interfaces. Use established UI patterns and mental models your team already understands. This reduces the learning curve and accelerates adoption. Meet them where "they are"" The future isn't about AI thinking for us - it's about creating powerful human-AI partnerships that amplify our natural cognitive abilities. This will be so key to the future of the #employeeexperience and how we deliver services to the workforce. #AI #FutureOfWork #Leadership #Innovation #CognitiveScience #BusinessStrategy Inspired by Tetiana Sydorenko's insightful article on UX Collective - https://lnkd.in/gMxkg2KD

The more we study human/AI collaboration the more we realize how difficult it is to speak in absolutes. We are easily sucked into the idea that #AIautomation will solve all of our problems, until it doesn't. Thx to my good friend Bas van de Haterd (He/His/Him) for sharing this excellent study "Falling Asleep at the Wheel: Human/AI Collaboration in a Field Experiment on HR Recruiters," by Fabrizio Dell'Acqua of Harvard Business School. The study explores the dynamics of human effort and AI quality in recruitment processes and reveals yet another paradox of AI: Higher-performing AI can sometimes lead to worse overall outcomes by reducing human engagement and effort. When it comes to hiring, this finding is pretty significant. Especially when one layers in the presence of bias that (hopefully) can be mitigated by the efforts of recruiters to be objective (We can dream can't we!). Here is a quick summary of the article's findings and implications. Key Findings: 💪 Human Effort vs. AI Quality: As AI quality increases, humans tend to rely more on the AI, leading to less effort and engagement. This can decrease the overall performance in decision-making tasks. 🙀 Lower Quality AI Enhances Human Effort: Recruiters provided with lower-performing AI exerted more effort and time, leading to better performance in evaluating job applications compared to those using higher-performing AI. 🎩 Experience Matters: More experienced recruiters were better at compensating for lower AI quality, improving their performance by remaining actively engaged and using their expertise to supplement the AI’s recommendations. Implications for Talent Acquisition Leaders: ⚖ Balanced AI Integration: While it may be tempting to implement the most advanced AI systems, it’s crucial to ensure that these systems do not lead to complacency among human recruiters. Talent acquisition leaders should focus on integrating AI tools that enhance rather than replace human judgment. 💍 Training and Engagement: Investing in training programs that encourage recruiters to critically assess AI recommendations can help maintain high levels of human engagement and performance. 🛠 Custom AI Solutions: Consider developing AI systems tailored to the specific needs and skills of your recruitment team. Custom solutions that require human input and oversight can prevent "falling asleep at the wheel" and ensure optimal performance.

The Truth About Human-AI Collaboration: When It Works—And When It Fails A groundbreaking meta-analysis of 106 studies, published in Nature Human Behaviour, just revealed surprising insights into human-AI collaboration—and the findings might not be what you expect. 🔑 Key Takeaways: ❌ #AI + Humans ≠ Always Better Performance – On average, human-AI teams underperform compared to the best humans or AI alone. ✅ AI Can Boost Human Performance – Humans assisted by AI do better than those without it, but true synergy (where AI + humans outperform both individually) is rare. 📉 AI Hurts Decision-Making – In many cases, AI assistance lowers decision-making quality due to overreliance or underutilization. 🎨 AI Excels in Creative Work – The biggest gains happen in ideation and execution, where AI acts as a powerful tool rather than a decision-maker. ⚖️ Who’s Better Matters – If humans are better than AI at a task, collaboration improves results. But when AI is superior, human involvement drags performance down. 💡 What This Means for the Future: 🔹 Leverage Strengths – Let AI handle what it does best, while humans focus on creativity, judgment, and ethics. 🔹 Rethink AI in Decision-Making – AI alone may be more reliable for some tasks, while human oversight should be strategic, not automatic. 🔹 Redesign AI Workflows – AI shouldn’t just “assist” humans blindly—workflows must be intelligently structured for maximum impact. The Bottom Line: AI isn’t a magic bullet. Success depends on using AI strategically, not just using it. 🔍 Your Turn: Have you worked with AI in decision-making or creative tasks? Do you trust AI’s recommendations? Let’s discuss! I still see human Plus is better than the best human or best ai (depends on the situation with several key factors *) #AI #ArtificialIntelligence #FutureOfWork #HumanAICollaboration #Innovation

“The art of leadership is balancing opposites without losing momentum.” – Inspired by John Wooden Healthcare leaders know that some of the toughest decisions we face aren’t about choosing one solution over another—they’re about balancing polarities. Patient-centered care vs. operational efficiency. The latest innovation vs. established practices. Individualized care vs. scalable systems. These are not problems to solve but tensions to manage, and how we handle them can shape the future of care. Take the early adoption of AI. Used wisely, AI has the potential to bring high-impact improvements to healthcare, particularly in areas like early screening and clinical documentation. Imagine piloting AI tools that can detect early signs of disease with unprecedented accuracy, or ambient AI “scribes” that free clinicians from note-taking, allowing them to focus entirely on the patient. These are low-risk, high-reward applications where the technology enhances, rather than disrupts, the care experience. Here’s how polarity thinking helps us move forward with confidence: 1. Hold Patient Care as the True North: Innovation in healthcare must ultimately serve patients. By focusing on tools that enhance the patient experience—like AI that supports early detection or improves clinician-patient interaction—we ensure that our exploration of new technologies remains aligned with our core mission. 2. Pilot for Learning and Impact: Testing AI in low-risk, high-impact areas allows leaders to gain experience and build trust with the technology. These pilots are a way to bring meaningful change without compromising safety or overwhelming the system. By starting with focused, impactful projects, we can make sure AI adds value before scaling up. 3. Lead with Purpose and Perspective: Navigating these tensions means making deliberate choices about when to innovate and when to stabilize. We don’t have to choose between tradition and progress—we can find ways for each to strengthen the other. With polarity thinking, healthcare leaders can leverage both sides to create a balanced, responsive system that meets immediate needs while preparing for the future. Balancing innovation with established care practices isn’t easy, but it’s necessary. By focusing on strategic, patient-centered AI pilots and a thoughtful approach to integration, we can make real progress without losing sight of our purpose. What challenges or insights have you encountered as you lead through the complexities of innovation in healthcare? Meagan O'Neill Olalekan Ajayi PharmD MBA FACCC Nadine Barrett, PhD, MA, MS Una Hopkins DNP Jorge J. García PharmD, MS, MHA, MBA, FACHE lJohn Rossman Sean Khozin Debra Patt, MD PhD MBA Sarah Louden Pinaki Saha https://lnkd.in/g8QgshQS

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

AI field note: In a recent post antirez wrote about how Gemini helped him reframe a problem (a good example of consequence-free exploration): “To verify all my ideas, Gemini was very useful, and maybe I started to think at the problem in such terms because I had a ‘smart duck’ to talk with.” This is an understated but important insight. It describes a dynamic I’ve come to think of as "Consequence-Free Exploration", one of three distinct modes of interaction that emerge when we stop treating AI agents as imitations of human intelligence and instead design around their differences (blog linked below!) In this case, the agent wasn’t providing novel insights or even strong opinions. It served as a responsive, always-available counterpart for externalizing thought. The act of explaining the idea—even to something nonhuman—reshaped the idea itself. This aligns with a broader pattern I've written about: the ways AI agents amplify cognitive work not by replacing it, but by enabling new forms of interaction. Here’s one way to frame it: ⚙️ Operational Liberation (Agent-to-Agent), where the majority of operational work will take place. Agents coordinate with each other across systems, handling tasks that don’t benefit from human judgment. 💭 Consequence-Free Exploration (Human-to-Agent), where the majority of creative work will take place. Agents become partners in early-stage thinking. They provide a space where ideas can be tested, rephrased, and challenged without social or organizational cost. What Antirez calls a “smart duck” plays this exact role—not validating or correcting, but catalyzing thought through interaction. 💬 Enhanced Human Collaboration (Human-to-Human), where the majority of economically valuable work will take palce. After individual exploration with agents, humans engage each other with clearer thinking and sharper questions. The quality of conversation improves because participants have already done the messy work of framing their ideas. Each tier builds on the others. What begins as automation in the operational tier becomes cognitive leverage in the creativity tier, which in turn improves the quality of human collaboration in the economic tier. Antirez's offhand comment captures this well. The presence of a nonjudgmental agent reshaped his thinking—not because it thought like him, but because it didn’t. That difference is where the value begins.

Going out on a limb: My class is unAI-able and uncheat-able. Here's how. I turn the tables on AI by prioritizing uniquely human skills as a teaching method. The class is Design Thinking in Education. When you look at what learners do, you can start to see why the class is unAI-able and uncheat-able. 1. Real-world engagement: The class involves direct interaction with a local community organization. This hands-on, real-world experience is something that AI cannot replicate or substitute. 2. Emphasis on teamwork and collaboration: A significant portion of the learning comes from working closely with teammates, which requires genuine human interaction and cannot be easily simulated or cheated. 3. Personal growth and self-reflection: Students are asked to reflect on their own strengths, weaknesses, and experiences throughout the course. 4. Introspection: This level of introspection and personal development is highly individualized and not something an AI could meaningfully produce. 5. Experiential learning: Activities like conducting interviews, site visits, and presenting ideas in real-time require physical presence and spontaneous human interaction, which cannot be automated or faked. 6. Creative problem-solving: The open-ended nature of the design thinking process and brainstorming activities relies on unique, human creativity and the ability to make unexpected connections. 7. Public speaking and presentation skills: Students are required to present their ideas and work, which necessitates real-time, in-person performance that can't be easily substituted. 8. Empathy development: Understanding and connecting with the needs of real people and organizations is a core part of the course, which requires genuine human emotional intelligence. These elements combined create a learning experience that is deeply rooted in human interaction, personal growth, and real-world application, making it extremely difficult, if not impossible, to replicate or cheat using AI or other automated means. Could students use generative AI to support their journey in any of the above? Sure thing. But nothing the LLM spits out will capture the unique creativity sparked by the students' collaboration. HT to Brandeis Marshall, PhD for inspiring me to focus on what's unAI-able. The future belongs to those who can out-create, not just out-compute. #designthinking #generativeAI #teaching #deeperlearning