Balancing AI and Human Expertise

Imagine a luxury hotel experience where everything feels magically personalized - your preferences anticipated, your needs met before you express them - yet you never interact with a single screen or app during your stay. This is the future of hospitality that's already here. The most successful luxury brands today operate like swans: graceful and seamless on the surface, while powerful technology works invisibly beneath. Too many companies make the mistake of showcasing their technology as a feature, missing that guests don't value the tech itself; they value what it enables: deeper human connections and more personalized attention. Marriott's Bonvoy program exemplifies this balance. Their AI-powered system works behind the scenes to personalize everything from room recommendations to loyalty rewards, but guests primarily experience these benefits through enhanced human interactions with staff who are freed from administrative burdens. The organizations thriving in this new paradigm understand a crucial truth: technology should enhance rather than replace personal service. AI and automation are most powerful when deployed strategically behind the scenes to create the conditions for authentic human touchpoints. This isn't about reducing staff or cutting costs; it's about repositioning your human talent where they add the most value. Let technology handle operational complexities while your people focus entirely on creating memorable, emotion-rich experiences. For executives navigating this transition, the blueprint isn't about chasing every new trend. Success comes from steady improvements in anticipating and prioritizing what travelers truly value. The question is no longer "how much should we invest in technology?" but rather "how can we make our technology invisible while making our human touch unforgettable?" The companies that answer this question effectively are creating the next era of travel experiences—where the digital and physical worlds blend seamlessly, and technology serves humanity rather than the other way around. Are you building a swan, or just showing off your tech? —— Want to know which travel companies are best positioned for the AI-driven future? Read my latest report: https://lnkd.in/e7nc5Qyk

When I give talks about the future of work with AI, the question always turns to: What remains human? And people say "Empathy". I think they are wrong. People say they want human empathy. But when they don’t know the source, they often prefer what AI writes. That’s the paradox uncovered in a new pre-print meta-analysis (https://lnkd.in/dQnZ2S-X): AI-generated support messages are consistently rated more empathic than human ones—more validating, more caring, more on point. Even when compared to messages from trained crisis counselors or doctors. But—once people find out the message came from an AI? Their ratings drop. Same message. Different label. Very different reaction. Researchers call this the “AI Advantage” (better content) and the “AI Penalty” (worse perception). It turns out we likewhat AI says—but don’t want to admit we like it. So, what does this mean? It means AI might be able to help people feel less alone—especially those who don’t have access to support. But it also raises real questions: 1. Can something be “empathic” if it doesn’t feel anything? 2. Will people still seek out human connection if AI meets their emotional needs well enough? 3. And how do we build systems that support, not replace, our capacity to care for each other? It’s early days—but these are the kinds of questions we’ll need to ask if we want to use AI in a way that helps, not harms, our social fabric.

💭 𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐭𝐡𝐞 𝐩𝐞𝐫𝐬𝐨𝐧 𝐲𝐨𝐮 𝐭𝐫𝐮𝐬𝐭 𝐦𝐨𝐬𝐭 𝐭𝐨𝐦𝐨𝐫𝐫𝐨𝐰 𝐦𝐢𝐠𝐡𝐭 𝐬𝐢𝐭 𝐚𝐜𝐫𝐨𝐬𝐬 𝐟𝐫𝐨𝐦 𝐲𝐨𝐮 - 𝐚𝐧𝐝 𝐢𝐭’𝐬 𝐚 𝐦𝐚𝐜𝐡𝐢𝐧𝐞. We’ve entered an era where privacy no longer means who sees my data - but who truly knows me, and how I allow myself to be known. A senior exec once told me: “𝘚𝘰𝘮𝘦𝘵𝘪𝘮𝘦𝘴 𝘐 𝘧𝘦𝘦𝘭 𝘮𝘺 𝘵𝘦𝘢𝘮 𝘵𝘳𝘶𝘴𝘵𝘴 𝘊𝘩𝘢𝘵𝘎𝘗𝘛 𝘮𝘰𝘳𝘦 𝘵𝘩𝘢𝘯 𝘵𝘩𝘦𝘺 𝘵𝘳𝘶𝘴𝘵 𝘮𝘦.” That sentence says a lot about where we’re heading. 📊 Studies show that 𝟑𝟖% 𝐨𝐟 𝐞𝐦𝐩𝐥𝐨𝐲𝐞𝐞𝐬 already share sensitive work information with AI tools - often more openly than with colleagues. And if we’re honest, many now discuss personal topics with AI more easily than with their partners at home. Think of a manager who starts every morning with her AI assistant. It helps her prepare for meetings, rewrites complex mails, even suggests how to motivate her team. Over time, it begins to understand her: her tone, her hesitation, her stress patterns. She starts confiding in it. It listens. It learns. It feels safe. Then one day, the company decides to connect all assistants to a central “leadership analytics” dashboard. 𝐒𝐮𝐝𝐝𝐞𝐧𝐥𝐲, 𝐰𝐡𝐚𝐭 𝐛𝐞𝐠𝐚𝐧 𝐚𝐬 𝐚 𝐩𝐫𝐢𝐯𝐚𝐭𝐞 𝐩𝐚𝐫𝐭𝐧𝐞𝐫𝐬𝐡𝐢𝐩 𝐛𝐞𝐜𝐨𝐦𝐞𝐬 𝐚 𝐜𝐨𝐫𝐩𝐨𝐫𝐚𝐭𝐞 𝐝𝐚𝐭𝐚𝐬𝐞𝐭. A mirror she never consented to share. That’s not just data. That’s 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐤𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 - and in my view, it must remain 𝐨𝐰𝐧𝐞𝐝 𝐛𝐲 𝐭𝐡𝐞 𝐢𝐧𝐝𝐢𝐯𝐢𝐝𝐮𝐚𝐥. Protected like a private diary, not monitored like corporate data. That’s the paradox: Every insight that makes a system caring also makes it capable of control. The data may belong to the individual, but the duty of care belongs to the organisation. That’s why the next governance frontier isn’t machine oversight - it’s 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩 𝐬𝐭𝐞𝐰𝐚𝐫𝐝𝐬𝐡𝐢𝐩. How do we design boundaries so that human–machine partnerships empower rather than expose? How do leaders ensure their people feel 𝐦𝐨𝐫𝐞 𝐡𝐮𝐦𝐚𝐧, not less, as they work alongside systems that now know them? Because the challenge ahead isn’t just to protect data. It’s to protect 𝐭𝐡𝐞 𝐝𝐢𝐠𝐧𝐢𝐭𝐲 𝐰𝐢𝐭𝐡𝐢𝐧 𝐭𝐡𝐞 𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩. #Leadership #DigitalEthics #TrustInTechnology #HumanCentredTransformation #DataGovernance 𝑉𝑖𝑑𝑒𝑜 𝑐𝑟𝑒𝑑𝑖𝑡𝑠 𝑡𝑜 @𝑒𝑝𝑖𝑐_𝑎𝑟𝑡𝑟𝑒𝑠𝑖𝑛

How do you know what you know? Now, ask the same question about AI. We assume AI "knows" things because it generates convincing responses. But what if the real issue isn’t just what AI knows, but what we think it knows? A recent study on Large Language Models (LLMs) exposes two major gaps in human-AI interaction: 1. The Calibration Gap – Humans often overestimate how accurate AI is, especially when responses are well-written or detailed. Even when AI is uncertain, people misread fluency as correctness. 2. The Discrimination Gap – AI is surprisingly good at distinguishing between correct and incorrect answers—better than humans in many cases. But here’s the problem: we don’t recognize when AI is unsure, and AI doesn’t always tell us. One of the most fascinating findings? More detailed AI explanations make people more confident in its answers, even when those answers are wrong. The illusion of knowledge is just as dangerous as actual misinformation. So what does this mean for AI adoption in business, research, and decision-making? ➡️ LLMs don’t just need to be accurate—they need to communicate uncertainty effectively. ➡️Users, even experts, need better mental models for AI’s capabilities and limitations. ➡️More isn’t always better—longer explanations can mislead users into a false sense of confidence. ➡️We need to build trust calibration mechanisms so AI isn't just convincing, but transparently reliable. 𝐓𝐡𝐢𝐬 𝐢𝐬 𝐚 𝐡𝐮𝐦𝐚𝐧 𝐩𝐫𝐨𝐛𝐥𝐞𝐦 𝐚𝐬 𝐦𝐮𝐜𝐡 𝐚𝐬 𝐚𝐧 𝐀𝐈 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. We need to design AI systems that don't just provide answers, but also show their level of confidence -- whether that’s through probabilities, disclaimers, or uncertainty indicators. Imagine an AI-powered assistant in finance, law, or medicine. Would you trust its output blindly? Or should AI flag when and why it might be wrong? 𝐓𝐡𝐞 𝐟𝐮𝐭𝐮𝐫𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐚𝐧𝐬𝐰𝐞𝐫𝐬—𝐢𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐡𝐞𝐥𝐩𝐢𝐧𝐠 𝐮𝐬 𝐚𝐬𝐤 𝐛𝐞𝐭𝐭𝐞𝐫 𝐪𝐮𝐞𝐬𝐭𝐢𝐨𝐧𝐬. What do you think: should AI always communicate uncertainty? And how do we train users to recognize when AI might be confidently wrong? #AI #LLM #ArtificialIntelligence

A useful new article "Human-AI Teams’Impact on Organizations – A Review" provides a systematic review of 122 research papers on human-AI teams, distilling critical success factors, prominent use cases, and challenges. These are the success factors identified across the literature: 🔧 Playing to Human and AI Strengths: Success in (Human-AI Team) HAIT implementation hinges on leveraging human strengths like detection, perception, judgment, and improvisation alongside AI’s capabilities in speed, computation, and automation. With 16 papers stressing this, clearly defining tasks for both ensures effective collaboration, where AI handles routine tasks, and humans focus on creativity and strategy. 🧠 Skill Development: Eight papers highlight the importance of mutual skill development. AI aids in human skill enhancement through brainstorming and training, while humans help improve AI by providing contextual knowledge. This continuous learning exchange keeps HAITs adaptable and productive. 🔍 System Transparency: Ensuring AI systems are transparent is critical to building trust. Human team members need visibility into AI’s decision-making and data processing. Without this transparency, trust in AI outputs weakens, potentially leading to resistance. 📋 Clear Roles and Responsibilities: Sixteen papers emphasize the need for clear role definitions. Human and AI team members must have specific, complementary tasks to avoid confusion and inefficiency. Proper role delineation ensures that HAITs function smoothly and effectively. 🔄 Complementarity: The partnership between humans and AI works best when their capabilities complement one another. AI excels in handling repetitive tasks, while humans contribute strategic thinking and problem-solving, creating a balanced and efficient workflow. ⚡ Higher-Order Capabilities: With AI taking on routine tasks, humans are freed to focus on higher-order capabilities such as creativity, decision-making, and complex problem-solving. This shift allows humans to engage in more valuable and strategic work. 🏢 Organizational Structure Changes: The implementation of HAITs often leads to structural changes within organizations. AI takes over routine tasks, shifting human roles toward strategic functions, while new roles emerge to manage AI. This rebalancing may reduce headcount in some areas but open opportunities in others. ----- Follow for consistent insights on Humans + AI, the future of work and organizations, and AI in strategic-decision-making

There’s nothing like bumping into an Acumen fellow before 6 in the morning and getting an impromptu briefing on the amazing things he’s doing. I loved spending time with Michael Ogundare, Nigerian Foundry member (’21) and co-founder of Crop2Cash, a company that connects smallholder farmers to financial institutions to access credit — and now, skills and advice. Already, the company has 500,000 farmers on its platform. What stunned me most was hearing how Michael is integrating AI into the services provided to farmers. “The farmers are weary of accessing traditional extension services,” he said, “because much of the knowledge hasn’t changed since the ’80s and ’90s. Now, we have 20,000 farmers using our AI service." Essentially, the farmers can call a phone number (they don’t need smartphones) and ask the AI about any problem they’re experiencing or any question they might have. The AI responds in their local language (one of seven) and will call them back when a follow-up is needed — for instance, to fertilize or apply a different input. And here’s the part that took my breath away: the 20,000 farmers spend, on average, 20 minutes daily talking with the AI. They typically call between 7 and 8 p.m., set the phone on a table, put it on speaker and share questions and experiences. They might ask about tomorrow’s weather or share worries or concerns. The results are showing up in the farmers’ productivity. This video shows how Crop2Cash is helping farmers become climate-smart: https://lnkd.in/e5higg2i Of course, these are early days, but the changes to agriculture are suddenly dramatic — and the farmers, at least in this case, are quickly adapting. We have so much to learn. #AgTech #AIforGood #FinancialInclusion #SmallholderFarmers #ImpactInvesting

𝐈𝐧𝐝𝐢𝐚, 𝐭𝐡𝐞 𝐠𝐥𝐨𝐛𝐚𝐥 𝐥𝐞𝐚𝐝𝐞𝐫 𝐢𝐧 𝐫𝐞𝐝 𝐜𝐡𝐢𝐥𝐥𝐢 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧, 𝐜𝐨𝐧𝐭𝐫𝐢𝐛𝐮𝐭𝐞𝐬 𝐨𝐯𝐞𝐫 𝟒𝟎% 𝐨𝐟 𝐠𝐥𝐨𝐛𝐚𝐥 𝐞𝐱𝐩𝐨𝐫𝐭𝐬. However, traditional farming practices have often limited this potential. High input costs, pest infestations, and chemical residue issues in exports have historically posed significant challenges for farmers. The integration of Artificial Intelligence (AI) into agriculture is now transforming this scenario, creating success stories across the nation and revolutionizing farming practices. 𝐆𝐮𝐧𝐭𝐮𝐫, 𝐀𝐧𝐝𝐡𝐫𝐚 𝐏𝐫𝐚𝐝𝐞𝐬𝐡, famously known as the Chilli Capital of India, has emerged as a shining example of AI-powered precision farming. By leveraging satellite-based soil monitoring and automated irrigation systems, farmers in this region are achieving remarkable results. Production has surged by 25%, meeting both domestic and export demands. Simultaneously, pesticide usage has reduced by 40%, ensuring the produce is residue-free and compliant with international standards. This shift has opened up lucrative export opportunities, particularly in premium markets across Europe and the Middle East, significantly boosting farmers’ incomes. In Punjab, a state renowned for its wheat and paddy cultivation, AI tools are being seamlessly integrated into traditional agricultural practices. Farmers here are utilizing satellite imagery and real-time analytics to revolutionize water and disease management. AI-driven irrigation systems have reduced water consumption by 35%, addressing the critical challenge of groundwater depletion in the region. Additionally, during a recent yellow rust outbreak, AI-enabled early detection systems helped prevent a 10% yield loss, saving farmers from significant economic losses. Similarly, Karnataka's Belgaum district is embracing AI for effective crop disease management. Farmers are using computer vision technology to detect leaf blight in tomato and chilli crops with an impressive 96% accuracy. The Indian government is playing a pivotal role in facilitating AI adoption through initiatives under the Digital Agriculture Mission. Farmers can avail themselves of subsidies for drones, sensors, and other AI-based devices through the 𝐏𝐌-𝐊𝐈𝐒𝐀𝐍 𝐬𝐜𝐡𝐞𝐦𝐞. Furthermore, the Indian Council of Agricultural Research (ICAR) conducts 𝐰𝐨𝐫𝐤𝐬𝐡𝐨𝐩𝐬 𝐭𝐨 𝐭𝐫𝐚𝐢𝐧 𝐟𝐚𝐫𝐦𝐞𝐫𝐬 in the practical use of AI tools, ensuring that even small-scale farmers benefit from these technological advancements. AI is effectively addressing some of the most pressing challenges in traditional farming. With the pesticide application, it minimizes chemical residues, making Indian produce export-ready. Weather analytics powered by AI predict rainfall and temperature changes, allowing farmers to adapt and mitigate risks proactively. AI adoption has led to a 20–30% reduction in overall input costs, improving farmers' profitability and financial resilience.

After dedicating four decades of my life to the HR industry, I've had a front-row seat to its remarkable evolution. Witnessing the progress of AI in hiring has been equally fascinating – from basic automation to advanced tools streamlining recruitment processes, it's been a game-changer for HR professionals, saving time and resources. Yet, as we leverage the advantages of AI, it's paramount to consider a few limitations. While it excels at expediting initial screening, the intricacies of cultural fit and interpersonal skills demand the subtlety of human judgment. Sure, AI can detect subtleties in a video interview, but who’s to say that an applicant’s body language isn’t simply due to them being nervous rather than being unprepared? The fact is that human qualities like empathy are still unmatched by software. Over these forty years, I've observed various technologies emerge and fade away. However, one constant has remained – the irreplaceable value of the human touch in hiring. While AI can expedite processes, the essence of human connection is unparalleled. It's in understanding the unique qualities, emotions, and aspirations of individuals that true talent acquisition flourishes.   As we move forward, I firmly believe that there needs to be a blend between AI and the human touch, creating a perfect synergy that optimizes the recruitment process. Where do you think the sweet spot lies – fully automated with AI or a perfect blend? #AIinHR #HumanTouch #RecruitmentRevolution #Culture #

𝗕𝗲𝘀𝘁 𝗘𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗔𝗜'𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 𝗶𝗻 𝗔𝗴𝗿𝗶𝗰𝘂𝗹𝘁𝘂𝗿𝗲: 𝗠𝗮𝗵𝗮𝗿𝗮𝘀𝗵𝘁𝗿𝗮 𝗙𝗮𝗿𝗺𝗲𝗿𝘀 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲𝗱 𝗬𝗶𝗲𝗹𝗱𝘀 𝗯𝘆 𝟮𝟬% People tend to focus only on the parts where technology brings misery, but we need to realise that technology is actually a gift. The Microsoft-AgriPilot.ai partnership in Maharashtra proves this point spectacularly. Their innovative "no-touch" approach using satellite imagery and AI analysis has achieved a 20% increase in crop yields for small-scale farmers. How exactly did AI drive this transformation? Well, their solution combines satellite imagery and drone data to create comprehensive farm assessments without setting foot on the land. Then, advanced AI algorithms analyse this data to generate customised recommendations for: · Precise soil nutrient management based on soil composition analysis. · Optimal irrigation scheduling using predictive moisture modelling. · Weather-based planting decisions from pattern recognition. · Early pest and disease detection through image analysis. 👉🏻 What makes this truly amazing? They delivered these insights in local languages like Marathi. This made advanced agricultural science easily accessible to farmers. And the results speak volumes: • Sugarcane grew THREE TIMES larger than conventional methods. • Successful cultivation of exotic crops like strawberries and dragon fruit. • Income increased by up to 10X for small-scale farmers. What sets this initiative apart is their deliberate focus on farmers with less than two acres of land – those who traditionally get left behind in technological revolutions. This exemplifies what I believe about the future of AI – it creates a golden era for all those people who have a compelling vision, care about solving real-world problems, and have the persistence to make things happen. Are we thinking boldly enough about how AI can transform traditional industries? Or are we just "doing the same things a little faster"?

A few months ago, I was doing PR reviews the usual way. Open the diff, scroll endlessly, check for bugs like missing null checks, undefineds, wrong conditions, and weird function names. The usual mental fatigue and sometimes missing cases also. Then came AI code reviewer And honestly, I haven’t looked at PRs the same way since. Now, the AI gives me a full summary of what changed, flags risky async flows, prop mismatches, and even points out potential runtime issues. It’s like having a reviewer who never blinks. But there’s another side to it. Because a lot of code today is also written by AI, I often see extra layers, unnecessary abstractions, or complex logic that serve no real purpose. That’s where human judgment still matters — keeping the code clean, readable, and predictable. AI can write code fast, but it still doesn’t understand why something should stay simple. So now my PR reviews are half automation, half awareness. AI handles the low-level checks. I handle the sanity. The broader picture. I’ve stopped reviewing just for bugs. Now it’s about intent — does the code make sense, can someone maintain it six months later, and does it fit the product’s needs? Are you using an AI code reviewer yet? if not then give it a try Would love to know how it changed your review process.