Humanixs

𝗠𝗲𝘁𝗮'𝘀 𝗥𝗘𝗔𝗟𝗜𝗧𝗬 𝗖𝗛𝗘𝗖𝗞: 𝗬𝗼𝘂𝗿 "𝗔𝗜 𝗥𝗲𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻" 𝗶𝘀 𝗕𝘂𝗹𝗹𝘀**𝘁 🚨 Let's call this what it is: 𝗠𝗘𝗧𝗔—supposedly at the bleeding edge of AI—is still stuck in the Stone Age running annual performance reviews like it's 1980. You couldn't make this up! 🤡 𝗔𝘁 𝗛𝘂𝗺𝗮𝗻𝗶𝘅𝘀, 𝘄𝗲 𝗰𝗮𝗹𝗹 𝗕𝗦 𝗯𝗲𝗰𝗮𝘂𝘀𝗲 𝘄𝗲'𝗿𝗲 𝗶𝗻 𝘁𝗵𝗲 𝘁𝗿𝗲𝗻𝗰𝗵𝗲𝘀. We start with first principles - actual human data, real experience not behaviour patterns, ground-level reality. Not executive suite fantasies. Let's Get Real: - You claim AI will revolutionise talent management - Yet you're still doing annual reviews like some dinosaur corporation - You preach innovation while practicing prehistoric management - Your "AI-first" company still relies on the same BS performance system Neutron Jack Welch used decades ago 𝗛𝗲𝗿𝗲'𝘀 𝘄𝗵𝗮𝘁 𝗥𝗘𝗔𝗟 𝗵𝘂𝗺𝗮𝗻-𝗳𝗶𝗿𝘀𝘁 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲: - Start with basic human data - not AI fantasies - Accept challenge that you are wrong - Build from ground-level get real data - not boardroom theories - Stop using algorithms that reflect human behavior symptoms - Challenge assumptions with actual evidence - not PowerPoint promises - Accept you have programmed your own mind to lie to yourself The gap between executive suite delusions and ground-floor reality has never been wider. Let's talk about your executive "𝘄𝗲𝗹𝗹𝗻𝗲𝘀𝘀 𝘁𝗵𝗲𝗮𝘁𝗲𝗿": - You preach about 𝗗𝗢𝗣𝗔𝗠𝗜𝗡𝗘 while crushing spirits with stack rankings - You sell 𝗦𝗘𝗥𝗢𝗧𝗢𝗡𝗜𝗡 dreams while creating cortisol nightmares - You lecture about 𝗢𝗫𝗬𝗧𝗢𝗖𝗜𝗡 and trust while fostering fear - You pitch 𝗘𝗡𝗗𝗢𝗥𝗣𝗛𝗜𝗡-𝗱𝗿𝗶𝘃𝗲𝗻 success while building anxiety factories - You babble about 𝗡𝗢𝗥𝗘𝗣𝗜𝗡𝗘𝗣𝗛𝗥𝗜𝗡𝗘 and focus while drowning teams in bureaucratic BS - You worship 𝗠𝗔𝗦𝗟𝗢𝗪'𝘀 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 while keeping people stuck fighting for basic job security 𝗪𝗵𝘆 Humanixs 𝗗𝗮𝗿𝗲𝘀 𝘁𝗼 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲: - We've done the ground work - We've collected the real data - We've seen the human impact - We know what actually works - We know the data is not in AI Your fancy AI won't fix broken human systems if it does not know how you breaking it. Period. We dare to say this because we've built from the ground up, understanding human patterns before jumping to AI solutions. Remember: It only takes 𝟭-𝟯% to start a revolution. The French figured that out centuries ago. We're part of that 1% - building real solutions while you play AI theatre. 𝗦𝘁𝗼𝗽 𝘁𝗵𝗲 𝗔𝗜 𝘀𝗻𝗮𝗸𝗲 𝗼𝗶𝗹. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗵𝘂𝗺𝗮𝗻 𝘁𝗿𝘂𝘁𝗵. Want real transformation? Start with actual human data. Start with what humans are experiencing. Start with ground truth. That's what we do at Humanixs. Not because it's trendy. Because it works. #HumanFirst #Transformation #NoMoreBS #Leadership #AIHype #Humanixs

𝗧𝗵𝗲 𝗚𝗵𝗼𝘀𝘁 𝗼𝗳 𝗝𝗮𝗰𝗸 𝗪𝗲𝗹𝗰𝗵 𝐯𝐢𝐬𝐢𝐭𝐢𝐧𝐠 𝐌𝐞𝐭𝐚: 𝗔𝗿𝗲 𝗪𝗲 𝗥𝗲𝗽𝗲𝗮𝘁𝗶𝗻𝗴 𝗛𝗶𝘀𝘁𝗼𝗿𝘆'𝘀 𝗠𝗶𝘀𝘁𝗮𝗸𝗲𝘀? History echoes in Silicon Valley. As Meta follows the ghost of "Neutron Jack" Welch's infamous bell curve philosophy, we're watching a rerun of a show we've seen before - and we know how it ends. 𝗧𝗵𝗲 𝗜𝗿𝗼𝗻𝘆: - GE's rigid performance system created a culture of fear - Meta (looking to fire 5% of workforce) adopts similar measures - We're using outdated tools to solve modern challenges 𝗧𝗵𝗲 𝗕𝗹𝗶𝗻𝗱 𝗦𝗽𝗼𝘁𝘀 Self-reviews, peer reviews, manager reviews—these processes often generate noise without meaningful insight. Without continuous, unbiased feedback, we find ourselves navigating through fog with broken instruments.Why do leaders, especially in HR, struggle to answer a seemingly simple question: What is your 𝐃𝐄𝐅𝐈𝐍𝐈𝐓𝐈𝐎𝐍 𝐅𝐎𝐑 𝐓𝐀𝐋𝐄𝐍𝐓? It's alarming how organisations, filled with talent management experts who pride themselves on hiring the best, lack a clear definition. 𝙒𝙝𝙚𝙧𝙚 𝙞𝙨 𝙈𝙚𝙩𝙖's 𝘼𝙄 𝙩𝙝𝙖𝙩 𝙨𝙝𝙤𝙪𝙡𝙙 𝙜𝙞𝙫𝙚 𝙩𝙝𝙚𝙞𝙧 𝙖𝙣𝙨𝙬𝙚𝙧𝙨 𝗔 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹 𝗟𝗲𝘀𝘀𝗼𝗻 In 2011, I faced with a challenge in Saudi Arabia: a workforce of 5,000 employees for roles that only required 2,200. The selection process was riddled with nepotism, and employees were illiterate no formal skills . The solution wasn’t found in forced rankings or performance curves; it lay in understanding human potential finding a context to relate to the complexity. The solution -- ignoring 40 years of Harvard University style studies was to turn to first principles of psychology. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 - How can we effectively evaluate talent without access to unbiased human data? - Why are we rushing to implement solutions before fully understanding the underlying problems? - What if the answers we seek are already present in our organisations but obscured by our biased systems? As Michael J. Sandel poignantly asks in "The Tyranny of Merit" https://lnkd.in/dgFn-SnJ went wrong? Perhaps being focused on measuring performance we neglect to measure understanding. 𝗧𝗵𝗲 𝗣𝗮𝘁𝗵 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 Before we follow Meta down the path of algorithmic performance management, let’s take a moment to pause and reflect. The solutions we need do not lie in replicating outdated methods; they exist in the human data we've overlooked. By embracing a more nuanced understanding of talent and performance, we can unlock the potential that lies within our organisations. Humanixs offers direct unbiased access to human potential data that exposes the dynamics in the problems and solutions in the room. To discover how to this work and its simplicity reach out to Humanixs send a DM or email info@humanixs.com #Leadership #PerformanceManagement #AI #Data #Humanixs #FOW #KSA #SaudiArabia https://lnkd.in/dXwPcmkv

𝐖𝐡𝐞𝐧 "𝐅𝐢𝐱𝐢𝐧𝐠" 𝐖𝐨𝐦𝐞𝐧 𝐢𝐧 𝐓𝐞𝐜𝐡 𝐁𝐞𝐜𝐨𝐦𝐞𝐬 𝐏𝐚𝐫𝐭 𝐨𝐟 𝐭𝐡𝐞 𝐏𝐫𝐨𝐛𝐥𝐞𝐦 Looking at this recent poll about barriers for women in tech leadership, something struck me: We're still getting it fundamentally wrong. -- To fix it is easy .. it requires a significant mind shift 𝗪𝗲'𝗿𝗲 𝗔𝘀𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗪𝗿𝗼𝗻𝗴 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 While we debate sponsorship vs. mentorship, gender bias, and work-life balance, we're missing the fundamental issue: We've never properly 𝘿𝙚𝙛𝙞𝙣𝙚𝙙 𝙏𝙖𝙡𝙚𝙣𝙩 𝙁𝙧𝙤𝙢 𝙁𝙞𝙧𝙨𝙩 𝙋𝙧𝙞𝙣𝙘𝙞𝙥𝙡𝙚𝙨. Let that sink in. 𝗧𝗵𝗲 𝗨𝗻𝗰𝗼𝗺𝗳𝗼𝗿𝘁𝗮𝗯𝗹𝗲 𝗧𝗿𝘂𝘁𝗵: - Having "talent" in your title doesn't mean you understand it - Your degrees and DEI initiatives don't matter if the foundation is broken - Working harder at the wrong things just amplifies the dysfunction When Mark Zuckerberg of Meta questions masculinity in programming or companies implement forced rankings going back to Neutron Jack Welch GE era of mismanaging people as we are poorly focused on what matters, are they just adding new layers to an already compromised foundation. 𝗧𝗵𝗲 𝗥𝗲𝗮𝗹 𝗤𝘂𝗲𝘀𝘁𝗶𝗼𝗻𝘀 𝗪𝗲 𝗦𝗵𝗼𝘂𝗹𝗱 𝗕𝗲 𝗔𝘀𝗸𝗶𝗻𝗴: 1. What happes when we defineTalent without including gender, culture, age, or behavioural bias? 2. How can we see true potential when our judgment is clouded by algorithms and preconceptions? 3. What if the solution is simpler than we think, but we're too busy "doing stuff" to see it? 𝘛𝘩𝘦 𝘌𝘮𝘱𝘦𝘳𝘰𝘳𝘴 𝘩𝘢𝘷𝘦 𝘯𝘰 𝘤𝘭𝘰𝘵𝘩𝘦𝘴, 𝘢𝘯𝘥 𝘦𝘷𝘦𝘳𝘺𝘰𝘯𝘦 𝘪𝘴 𝘳𝘶𝘴𝘩𝘪𝘯𝘨 𝘤𝘰𝘮𝘱𝘭𝘪𝘮𝘦𝘯𝘵𝘪𝘯𝘨 𝘵𝘩𝘦𝘪𝘳 𝘸𝘢𝘳𝘥𝘳𝘰𝘣𝘦. ... What happens when the winter sets in? Maybe it does not matter isolated in their temp controlled Ivory Towers? 𝗧𝗵𝗲 𝐖𝐚𝐲 𝗙𝗼𝗿𝘄𝗮𝗿𝗱 -- 𝐓𝐡𝐞 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭 𝐍𝐨𝐭 𝐃𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐭𝐥𝐲: Before we implement another initiative or program, we need to strip everything back to first principles. Humanixs consistently uncovers this fundamental gap: organizations racing to solve problems they've never properly defined. What if the answer isn't in doing more, but in seeing clearly what's already there? What happens when we anonymise and quantify human potential without the noise of perception and opinion? The data exists. The reality is there. We just need the courage to look at it unfiltered. Ready to see what's really holding back talent in your organization? 🔍 Email us at info@humanixs.com or DM for an unbiased, first-principles data to understanding your talent reality. #TalentManagement #WomenInTech #OrganizationalDevelopment #Leadership #UnbiasedData #Humanixs #SystemicChange

🔍 The Hidden Truth About Organizational Complexity We're caught in a dangerous cycle: Leadership, HR, and behavioral psychology are trapped in a time warp, serving oversimplified solutions to overwhelmed executives. Why? Because acknowledging the real problem threatens the status quo. Here's what nobody's talking about: Conway's Law reveals a brutal truth: Your organization's effectiveness cannot exceed your leadership's communication capabilities. Yet we keep playing musical chairs with leadership positions, ignoring the systemic dysfunction staring us in the face. Look at Haier and Bayer - they didn't just reorganize, they eliminated entire layers of middle management. Why? Because they recognized that complexity isn't the enemy - our approach to it is. The 2025 conversation leaders aren't ready for: • It's not about managing complexity - it's about understanding what creates it • It's not about individual performance - it's about systemic patterns • It's not about quick fixes - it's about first principles that define human potential Think about this: What if your organization's struggles aren't about culture, behavior, or skills? What if the data shows it's about the mismatch between human capability and induced complexity? The solution isn't another leadership framework or culture initiative. It's about seeing the patterns hidden in your data - patterns that reveal why your best efforts at change keep hitting invisible walls. 🎯 The Real Question: Are you ready to move beyond the blame game and see what's really holding your organization back? Want to see what your data is hiding? Want to understand why traditional approaches keep failing? Humanixs offers something unambiguously simple: data-driven insights that transform how you understand and address organizational complexity. #OrganizationalTransformation #LeadershipEvolution #ComplexityManagement #Humanixs 👉 Ready for real insights? DM David Howell or me to discover what's really happening in your organization. --- Inspired by groundbreaking work at Haier , Bayer , and recent insights from Fast Company on dismantling leadership double standards. https://lnkd.in/d895agfx

I’ve removed 8,000 LinkedIn connections. Why? Because the QUALITY of my feed was clouded by repetitive, soulless regurgitations about leadership, behavior, and AI—none of which are influencing real change. What I saw were meaningless posts, liked and shared out of habit, filled with buzzwords that show zero cognitive engagement. They’re a clear signal: people *want* change—they’re desperate for it—but they don’t know *what* to change. Instead of diving into complexity, we’re distracted. #AI dominates the conversation, with debates about "AI consciousness" and "AI well-being," as if machines will have a soul tomorrow. Meanwhile, we avoid the very human issues staring us in the face—self-awareness, meaningful connection, and a Collective Social Contract that holds us accountable to something greater than ourselves. Then, I came across the fruit of my work with a cleaner feed. Dr Nadege Claudel's insights from her interview with Dr. Sylvia Earle. Words offering profound clarity: "We Are All Together In This Single Living Ecosystem Called Planet Earth, And The Health Of Our Oceans Is Interconnected With The Health Of Our People And Our Future." Dr. Earle’s message is simple yet powerful: “People Might Not Care Even If They Know, But They Can’t Care If They Are Unaware.” This struck me, resonating with how we created Humanixs. Awareness is the foundation of change. But here’s the rub: awareness without action is meaningless. We’ve become 𝐚 𝐬𝐨𝐜𝐢𝐞𝐭𝐲 𝐚𝐝𝐝𝐢𝐜𝐭𝐞𝐝 𝐭𝐨 𝐤𝐧𝐨𝐰𝐢𝐧𝐠—reading reports, attending workshops, consuming data—yet we fail in the doing. This is why Humanixs exists: to bridge that gap between knowing and doing, turning awareness into tangible action. This is especially evident in Singapore’s context, where knowledge is abundant, but its application fails to engage people effectively as humans. We understand what needs to be done, but we 𝐝𝐨𝐧’𝐭 𝐤𝐧𝐨𝐰 𝐡𝐨𝐰 𝐭𝐨 𝐚𝐝𝐚𝐩𝐭 𝐢𝐭 𝐭𝐨 𝐡𝐮𝐦𝐚𝐧 𝐜𝐨𝐦𝐩𝐥𝐞𝐱𝐢𝐭𝐲. We overlook human fragility, sensitivity to change, and the variability that complexity brings. The real challenge isn’t access to knowledge; it’s understanding how individuals process it and adapt within their environments. And here’s where Dr. Earle’s other insight comes into play: "𝐎𝐧𝐞 𝐜𝐚𝐧𝐧𝐨𝐭 𝐝𝐨 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠, 𝐛𝐮𝐭 𝐰𝐞 𝐜𝐚𝐧 𝐚𝐥𝐥 𝐝𝐨 𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠." But first, we need to 𝐝𝐞𝐟𝐢𝐧𝐞 𝐰𝐡𝐚𝐭 “𝐬𝐨𝐦𝐞𝐭𝐡𝐢𝐧𝐠” is—and that 𝐒𝐭𝐚𝐫𝐭𝐬 𝐖𝐢𝐭𝐡 𝐃𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐓𝐚𝐥𝐞𝐧𝐭. How can we create or support awareness and action without understanding what talent truly means, when most focus on the visible yet ignore the invisible experiences and thoughts? So, my question for you: 𝐖𝐡𝐚𝐭 𝐈𝐬 𝐘𝐨𝐮𝐫 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧 𝐎𝐟 𝐓𝐚𝐥𝐞𝐧𝐭? How does it shape your ability to engage with complexity and drive meaningful change? Choice: _ Avoid superficial solutions _ Ask deeper questions _ Get first-person, human-centred data OR...?

𝐑𝐞𝐝𝐞𝐟𝐢𝐧𝐢𝐧𝐠 𝐓𝐚𝐥𝐞𝐧𝐭: 𝐓𝐡𝐞 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐲 𝐇𝐢𝐝𝐝𝐞𝐧 𝐢𝐧 𝐎𝐮𝐫 𝐁𝐥𝐢𝐧𝐝 𝐒𝐩𝐨𝐭𝐬 What if the way we are not defining talent is holding us back? My daughter had a similar experience being an expat in KSA, going to NEU Boston, USA, with no support system. We had no real insight into her struggles or how to support her. The first year was a nightmare—isolated, unsupported, and unsure of what talent even meant. It was even harder for her to explain what she was going through. Her mental wellbeing was at an ultimate low, and she was placed on a performance monitoring program. The real issue? Schools and universities don’t explain what talent is, how to unlock it, or how it supports balancing studies and mental wellbeing. Reflecting on my journey, I was told to "do what you want" because psychometrics and experts couldn’t define me—excelling at everything but never standing out. It felt like trying things on for size, getting bored, and hoping to find where I belong. By age 32, taking stats from USA Labour the average young person have already cycled through 8 jobs. My path? I started wanting to be an accountant. Military conscription made me a radar operator before I returned to accounting and banking. A "computer interview" unexpectedly led me to mining engineering, where I spent 25 years. Mine accidents and "Fate" then took me to KSA—first building the now Maaden Barrick Copper Company Jabal Sayid mine, and later restructuring a KSA construction icon. I had to selected 2,200 out of 5,000 unskilled, mostly illiterate workers for a massive project involving 400 new Caterpillar machines. The challenges—nepotism, favoritism, and 16 nationalities—shaped my understanding of talent and its link to mental wellbeing, forming the foundation for Humanixs today and our understanding of talent needing to go back to first principles. From school to work, we’re told to replicate knowledge and perform. But what is 𝐭𝐫𝐮𝐞 𝐭𝐚𝐥𝐞𝐧𝐭 when undefined potential lacks context or connection to mental wellbeing? That’s the real issue. The fear of disappointing others drove many of us, giving up never felt like an option. Yet, how many end up lost—or worse—unable to process or contextualise those struggles? A damaged GPA early on raises the question: How is true talent ever recognised when only "the best GPA" get hired? It’s time to ask: 𝐇𝐨𝐰 𝐝𝐨 𝐘𝐎𝐔 𝐝𝐞𝐟𝐢𝐧𝐞 𝐭𝐚𝐥𝐞𝐧𝐭? Why does this question make us uneasy? The system has failed us. It’s time to define talent. Why does this question make us uneasy? The system has failed us, and it’s time for a reset. What’s stopping us from exploring this? Is it our lack of self-awareness? With AI and GenAI shaping everything, how do we humanise talent? If you have access to the right data to reveal it, would we still resist seeing the truth? Humanixs

If you have the largest language models (LLMs) and the most powerful AI, 𝐰𝐡𝐲 𝐝𝐨 𝐲𝐨𝐮 𝐬𝐭𝐢𝐥𝐥 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐡𝐢𝐫𝐞 𝐚𝐝𝐯𝐞𝐫𝐭𝐢𝐬𝐢𝐧𝐠 𝐬𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐬𝐭𝐬? Let’s break this down: - 𝐖𝐡𝐚𝐭 𝐭𝐚𝐥𝐞𝐧𝐭 𝐚𝐫𝐞 𝐲𝐨𝐮 𝐡𝐢𝐫𝐢𝐧𝐠? What sets them apart from the vast knowledge your systems already possess? If your AI is so advanced, why rely on human expertise for advertising? - When I search for a definition of talent, I still as feedback get vague answers. It’s frustrating—whether from ChatGPT or a human, the responses feel generic. - If AI is truly powerful, why depend on advertising? With access to all media and information, what gap necessitates human expertise? Does this suggest a future that’s more dystopian than utopian? What are leaders, who are in awe of AI, missing? These questions should resonate with all business Leaders and HR professionals. When being challenged not finding talent, what does it really mean? Why do experts more often become silent when faced with such simple question? Understanding talent requires clarity about what it truly is. Complexity that surround it is more often an illusion; if we don’t simplify our approach, we’ll struggle to find meaningful answers. Talent begins with self-awareness—recognising how we define success, happiness, and burnout. This self-awareness must connect to a universal framework that acknowledges our shared human nature while embracing our diverse expressions. Without such a framework, definitions of talent remain fragmented and biased, disconnected from the true potential of human adaptability. When we overlook this, we risk turning self-awareness into an isolated exercise rather than a bridge to collective growth. By embedding our understanding of talent within this universal context, we can cultivate environments where potential thrives. The data isn’t hard to access; it’s often our own hubris and lack of curiosity that stands in the way of deeper exploration. A Humanixs experience to how we can approach this much simpler will help us navigate complexity by including everyone in the conversation. 𝐑𝐞𝐚𝐝𝐲 𝐭𝐨 𝐞𝐱𝐩𝐥𝐨𝐫𝐞? It might challenge your thinking, but this is about moving beyond algorithms to engage with unbiased data and the real human context of experience. The first step is being open to what the data reveals—unlocking the true complexity of our humanity through an approach that may appear simple on the surface but operates with speed and scale to transform understanding. Humanixs | info@humanixs.com

The question remains: What exactly is Garry Tan of Y Combinator thanking God for? Is it for the destruction of human livelihoods, only to then laugh and celebrate it?   This highlights the ultimate failure of a society where self-centred, intelligent individuals, guided by a Maslow-driven North Star, gain influence and control over humanity.   We must take a hard look at what’s being missed. On one side, we have these individuals seemingly celebrating the erosion of humanity through technology. On the other, we have voices like Dagmar Monett and Jim Amos speaking truth to power.   Dagmar’s words deserve to be quoted verbatim, demanding full attention:   "The point of Algorithmification, and especially of AI, is to replace people. Several works have explained and demonstrated why over the years. I have done it too, e.g., in 'Deconstructing the AI Myth: Fallacies and Harms of Algorithmification' (Cc Bogdan Grigorescu):   'Those who control the data and the technology, a few unelected players, are increasingly optimising and affecting society’s functioning through AI (Whittaker, 2021; Zuboff, 2019). The vast complexity of social interactions and life itself are reduced to abstract, limited mathematical formulations that cannot cope with novelty, are translated to machinistic (i.e., machine-dependent) and mechanistic expressions, and result in restricted and narrow mathematics-based algorithms that operate in utilitarian ways. Those algorithms are then used “to find, expand, fine-tune, and experiment with [human] processes [and activities] that unfold in time” (Hilbert, 2022, p. 4), thereby allowing for the algorithmification of humans and their behaviours and directly affecting them and their future.'  Why is this approach so pervasive? The answer simply: we’ve failed to define human talent as an innate and fluid sensitivity to surrounding complexity. Instead, we reduce humans to linear processes while ignoring the real Pareto 80/20 split between linear and non-linear complexity. We’ve yet to acknowledge the innate potential within humans or design systems that account for this fluid complexity.   For decades, Maslow’s hierarchy has dominated our understanding of human interaction, driving business design. This has caused profound damage, further compounded by education and leadership industries that use it as a baseline for teaching future leaders. Without an innate talent definition beyond education, skills, or behavioural traits, algorithms box people into narrow, predefined roles, disregarding their humanity.   This misstep has become glaringly obvious in our work at Humanixs. By asking simple—not more complex—questions, we’ve exposed the contradictions in individuals' own experiences. Fascinatingly, when confronted with these insights, many double down, justifying their actions while remaining oblivious to their impact on others.   Time to ask ourselves: How human—or unhuman—do we want our world to be?   https://lnkd.in/gR6KNCDE

🇸🇦 KSA's GenAI Quest: A Challenge of Data, Complexity, and Leadership Saudi Arabia’s pursuit of GenAI and data-driven transformation echoes a global struggle: 𝘼𝙧𝙚 𝙬𝙚 𝙛𝙤𝙘𝙪𝙨𝙞𝙣𝙜 𝙤𝙣 𝙩𝙝𝙚 𝙧𝙞𝙜𝙝𝙩 𝙙𝙖𝙩𝙖 𝙩𝙤 𝙪𝙣𝙡𝙤𝙘𝙠 𝙩𝙧𝙪𝙚 𝙥𝙤𝙩𝙚𝙣𝙩𝙞𝙖𝙡, 𝙤𝙧 𝙖𝙧𝙚 𝙬𝙚 𝙢𝙞𝙨𝙨𝙞𝙣𝙜 𝙩𝙝𝙚 𝙘𝙧𝙞𝙩𝙞𝙘𝙖𝙡 𝙣𝙪𝙖𝙣𝙘𝙚𝙨 𝙤𝙛 𝙝𝙪𝙢𝙖𝙣 𝙘𝙤𝙢𝙥𝙡𝙚𝙭𝙞𝙩𝙮 𝙖𝙣𝙙 𝙘𝙖𝙥𝙖𝙗𝙞𝙡𝙞𝙩𝙮? The lesson from restructuring a Saudi construction company is striking:   - Out of 5,000 employees, 90% were functionally and physically illiterate.   - Yet, traditional methods—skills training and standard metrics—failed to capture the insights needed for impactful transformation. The challenge wasn’t just about data scarcity; it was about the **𝙬𝙧𝙤𝙣𝙜 𝙩𝙮𝙥𝙚 𝙤𝙛 𝙙𝙖𝙩𝙖**—missing context, human complexity, and the relationship between people, technology, and tasks.  GenAI efforts, as Andreessen Horowitz notes, face a similar bottleneck:   > “The availability of training data needed to teach AI models how to behave is increasingly becoming a problem.”*  If training data for AI is limited, **𝙝𝙪𝙢𝙖𝙣 𝙙𝙖𝙩𝙖 𝙞𝙨 𝙚𝙫𝙚𝙣 𝙢𝙤𝙧𝙚 𝙢𝙞𝙨𝙪𝙣𝙙𝙚𝙧𝙨𝙩𝙤𝙤𝙙**. Leadership must shift from collecting superficial metrics to uncovering the deeper, often invisible factors that define job performance, adaptability, and interaction with emerging technologies.  🔑 Key Insights for KSA's GenAI Journey:   1️⃣ 𝐅𝐢𝐫𝐬𝐭 𝐏𝐫𝐢𝐧𝐜𝐢𝐩𝐥𝐞𝐬 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠: Define the foundational complexities of human-technology interaction.   2️⃣ 𝐔𝐧𝐛𝐢𝐚𝐬𝐞𝐝 𝐃𝐚𝐭𝐚 𝐒𝐲𝐬𝐭𝐞𝐦𝐬: Build frameworks to gather and analyze human insights beyond surface-level qualifications and behaviours.   3️⃣ 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐀𝐜𝐜𝐨𝐮𝐧𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Leaders must embrace uncertainty, move past quick fixes, and prioritise sustainable, people-centred solutions context.  Saudi Arabia’s ambitious vision for AI will only succeed if it invests in understanding not just the tools—but the people who will use them. 𝐃𝐀𝐓𝐀 𝐖𝐈𝐓𝐇𝐎𝐔𝐓 𝐇𝐔𝐌𝐀𝐍 𝐂𝐎𝐍𝐓𝐄𝐗𝐓 𝐈𝐒 𝐉𝐔𝐒𝐓 𝐍𝐎𝐈𝐒𝐄. How is your organization preparing for this challenge? There is a Humanixs tool and data answer for creating the human context if you are curious. #KSA #saudiarabia #AI #GenAI #Talent #complexity #transformation #digital #changemanagement https://lnkd.in/drnVEc6n

Unraveling the Question of Talent Leaders often face a key question that needs clear answers from their HR teams: *WHAT IS YOUR DEFINITION OF TALENT?* This simple question is crucial for understanding deeper issues within organizations. Unfortunately, many leaders struggle to answer it, and it often gets lost in conversations about hiring top talent. This confusion is reflected in the frustration seen online regarding the behaviors expected from both leaders and talented individuals. The Complexity of Definition Defining talent goes beyond just qualifications, skills, culture, or attitude; these are merely signs of a deeper issue. Many leaders overthink this question, focusing too much on surface-level indicators instead of getting to the heart of what talent really means. The answer lies in what remains undefined. To truly understand talent, one must look at the context around it. This requires a first principles approach—breaking down the problem to its basics. While this takes effort—nothing valuable comes without work—getting it right can reveal important insights about human potential and adaptability. The Challenge of Perception One major challenge leaders face is recognizing that their view of talent—and how they assess it—might be causing them ongoing stress. This realization can be uncomfortable but is necessary for growth. As Deming famously said, "Without data, you are just another opinion." The choice is between relying on personal views or gathering data that helps understand human behavior and potential. Embracing Simplicity The solution is simple if one can let go of rigid meritocratic beliefs. By looking inward rather than outward, leaders can gain a better understanding of talent. With Humanixs as a guiding principle, we focus on essential human data—free from biases and judgments. We start fresh, setting aside preconceived notions to concentrate on individual realities. Slowing down allows for real progress; one thoughtful step forward can lead to a brighter future. Redefining talent requires self-reflection and a willingness to challenge existing beliefs. By doing so, leaders can develop a clearer understanding of human potential and its complexities. But the question remains: How significant does the pain need to be before taking this step? Does it take reaching a breaking point—where nearly everything is at stake—for leaders to consider this change? Or will they wait until desperation drives them to confront uncomfortable truths? All it takes is a chat to discover how easy it can be. But as many discover it initially is uncomfortable, but then they discover the wasted opportunity within.