Digital Leadership in Technology

𝗜𝗳 𝘆𝗼𝘂 𝘄𝗮𝗻𝘁 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮𝗻 𝗔𝗜 𝘀𝘁𝗿𝗮𝘁𝗲𝗴𝘆 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆, 𝘆𝗼𝘂 𝗳𝗶𝗿𝘀𝘁 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗯𝘂𝗶𝗹𝗱 𝗮 𝘀𝗼𝗹𝗶𝗱 𝗱𝗮𝘁𝗮 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗮𝗻𝗱 𝗲𝗻𝗳𝗼𝗿𝗰𝗲 𝘀𝘁𝗿𝗶𝗰𝘁 𝗱𝗮𝘁𝗮 𝗵𝘆𝗴𝗶𝗲𝗻𝗲. Getting your house in order is the foundation for delivering on any AI ambition. The MIT Technology Review — based on insights from 205 C-level executives and data leaders — lays it out clearly: 𝗠𝗼𝘀𝘁 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗱𝗼 𝗻𝗼𝘁 𝗳𝗮𝗰𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗳𝗮𝗰𝗲 𝗰𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗶𝗻 𝗱𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗮𝗻𝗱 𝗿𝗶𝘀𝗸 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁. Therefore, many firms are still stuck in pilots, not production. Changing that requires strong data foundations, scalable architectures, trusted partners, and a shift in how companies think about creating real value with AI. Because pilots are easy, BUT scaling AI across the enterprise is hard. 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗸𝗲𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀: ⬇️ 1. 95% 𝗼𝗳 𝗰𝗼𝗺𝗽𝗮𝗻𝗶𝗲𝘀 𝗮𝗿𝗲 𝘂𝘀𝗶𝗻𝗴 𝗔𝗜 — 𝗯𝘂𝘁 76% 𝗮𝗿𝗲 𝘀𝘁𝘂𝗰𝗸 𝗮𝘁 𝗷𝘂𝘀𝘁 1–3 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀:   ➜ The gap between ambition and execution is huge. Scaling AI across the full business will define competitive advantage over the next 24 months. 2. 𝗗𝗮𝘁𝗮 𝗾𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗹𝗶𝗾𝘂𝗶𝗱𝗶𝘁𝘆 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗿𝗲𝗮𝗹 𝗯𝗼𝘁𝘁𝗹𝗲𝗻𝗲𝗰𝗸𝘀: ➜ Without curated, accessible, and trusted data, no AI strategy can succeed — no matter how powerful the models are. 3. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗽𝗿𝗶𝘃𝗮𝗰𝘆 𝗮𝗿𝗲 𝘀𝗹𝗼𝘄𝗶𝗻𝗴 𝗔𝗜 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 — 𝗮𝗻𝗱 𝘁𝗵𝗮𝘁 𝗶𝘀 𝗮 𝗴𝗼𝗼𝗱 𝘁𝗵𝗶𝗻𝗴:   ➜ 98% of executives say they would rather be safe than first. Trust, not speed, will win in the next AI wave. 4. 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘇𝗲𝗱, 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝘁𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗮𝗹𝘂𝗲:  ➜ Generic generative AI (chatbots, text generation) is table stakes. True differentiation will come from custom, domain-specific applications. 5. 𝗟𝗲𝗴𝗮𝗰𝘆 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗿𝗲 𝗮 𝗺𝗮𝗷𝗼𝗿 𝗱𝗿𝗮𝗴 𝗼𝗻 𝗔𝗜 𝗮𝗺𝗯𝗶𝘁𝗶𝗼𝗻𝘀:  ➜ Firms sitting on fragmented, outdated infrastructure are finding that retrofitting AI into legacy systems is often more costly than building new foundations. 6. 𝗖𝗼𝘀𝘁 𝗿𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀 𝗮𝗿𝗲 𝗵𝗶𝘁𝘁𝗶𝗻𝗴 𝗵𝗮𝗿𝗱: ➜ From GPUs to energy bills, AI is not cheap — and mid-sized companies face the biggest barriers. Smart firms are building realistic ROI models that go beyond hype. 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗮 𝗳𝘂𝘁𝘂𝗿𝗲-𝗿𝗲𝗮𝗱𝘆 𝗔𝗜 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗰𝗵𝗮𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗺𝗼𝗱𝗲𝗹 𝗿𝗲𝗹𝗲𝗮𝘀𝗲.   𝗜𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝘀𝗼𝗹𝘃𝗶𝗻𝗴 𝘁𝗵𝗲 𝗵𝗮𝗿𝗱 𝗽𝗿𝗼𝗯𝗹𝗲𝗺𝘀 — 𝗱𝗮𝘁𝗮, 𝗶𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗥𝗢𝗜 — 𝘁𝗼𝗱𝗮𝘆.

AI isn’t failing because of technology. It’s failing because of leadership. Everyone keeps saying AI is a technology story. The data says otherwise. Boston Consulting Group (BCG)’s latest research shows a quiet but massive shift: AI has moved from the IT agenda to the CEO’s desk. In fact, “72% of CEOs say they're now the main decision-makers on AI in their organizations.” That matters more than it sounds. When AI lives in IT, it improves processes. When AI lives with the CEO, it reshapes the business. What struck me most in this research is not who’s winning with AI—but why. The leaders pulling ahead aren’t more technical. They’re more decisive. They invest before everything is clear. They move faster on skills. They accept uncertainty as the cost of staying competitive. By 2026, AI success won’t come from better models. We all have access to the same ones. It will come from leaders willing to personally own the bet—and treat AI as a strategic responsibility, not a side project. 💡 The full BCG report is well worth reading: https://lnkd.in/etfMrx6t 👉 So here’s the real question for you: Is AI still “a project” somewhere in your organization, or is it a leadership responsibility at the very top? #BCGAmbassador #Leadership #AI #Strategy #DigitalTransformation #FutureOfWork

The real gap between digital leaders and laggards isn’t just in technology—it's in mindset. The 𝐃𝐢𝐠𝐢𝐭𝐚𝐥 𝐃𝐢𝐯𝐢𝐝𝐞 isn’t about who has the best tools; it’s about who knows how to wield them. The difference between average and excellent isn’t in the number of systems implemented but in the strategic intent behind them. True digital transformation isn’t just an IT initiative—it’s a company-wide movement, a reimagining of what’s possible when leadership, innovation, and agility align. 𝐖𝐡𝐚𝐭 𝐀𝐯𝐞𝐫𝐚𝐠𝐞 𝐋𝐨𝐨𝐤𝐬 𝐋𝐢𝐤𝐞: • 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲-𝐅𝐨𝐜𝐮𝐬𝐞𝐝 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩: CIOs and CTOs leading the charge, with an inward focus on IT infrastructure. • 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲 𝐎𝐯𝐞𝐫 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Tracking efficiency and business performance without a broader view towards future capabilities. • 𝐂𝐚𝐮𝐭𝐢𝐨𝐮𝐬 𝐏𝐫𝐨𝐠𝐫𝐞𝐬𝐬: Proceeding with digital steps without the urgency to outpace the evolving market demands. • 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐛𝐢𝐥𝐢𝐭𝐲: Maintaining the status quo in operations, favoring predictability over agility. • 𝐒𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐓𝐨𝐨𝐥 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧: Providing employees with collaboration tools without fostering a culture of digital innovation. • 𝐁𝐚𝐜𝐤𝐞𝐧𝐝 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Concentrating on backend upgrades before considering the customer-facing aspects of the business. • 𝐒𝐢𝐥𝐨𝐞𝐝 𝐃𝐚𝐭𝐚 𝐔𝐭𝐢𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Using data for routine business operations rather than as a cornerstone for transformation and innovation. 𝐖𝐡𝐚𝐭 𝐄𝐱𝐜𝐞𝐥𝐥𝐞𝐧𝐭 𝐋𝐨𝐨𝐤𝐬 𝐋𝐢𝐤𝐞: • 𝐋𝐞𝐚𝐝𝐞𝐫𝐬𝐡𝐢𝐩 𝐟𝐫𝐨𝐦 𝐭𝐡𝐞 𝐓𝐨𝐩: Transformation championed by CEOs, integrating digital priorities within the company’s vision. • 𝐂𝐨𝐦𝐦𝐢𝐭𝐦𝐞𝐧𝐭 𝐭𝐨 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Measuring success through the lens of innovation and digital proficiency. • 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐢𝐜 𝐀𝐜𝐜𝐞𝐥𝐞𝐫𝐚𝐭𝐢𝐨𝐧: Not merely adapting but actively advancing digital initiatives, even in challenging economic climates. • 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐠𝐢𝐥𝐢𝐭𝐲: A culture that embraces operational efficiency as a path to competitive advantage. • 𝐏𝐞𝐨𝐩𝐥𝐞 𝐚𝐬 𝐏𝐫𝐢𝐨𝐫𝐢𝐭𝐲: Investing in employee engagement and digital literacy, recognizing that technology amplifies human potential. • 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫-𝐂𝐞𝐧𝐭𝐫𝐢𝐜 𝐄𝐯𝐨𝐥𝐮𝐭𝐢𝐨𝐧: Prioritizing the customer experience with a strategy that adapts proactively to their needs and behaviors. • 𝐃𝐚𝐭𝐚-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧𝐬: Leveraging AI and data analytics not only to inform decisions but to foster a culture of continuous improvement. 𝐅𝐮𝐥𝐥 𝐚𝐫𝐭𝐢𝐜𝐥𝐞: https://lnkd.in/eU_Cc3ga ******************************************* • Visit www.jeffwinterinsights.com for access to all my content and to stay current on Industry 4.0 and other cool tech trends • Ring the 🔔 for notifications!

“Why are you so quiet in meetings?” “I’m not quiet. I’m processing.” “Can you just jump in more?” “I need a bit of thinking time.” “Silence feels awkward.” “For you” Sound familiar? In many workplaces, we reward speed. Fast answers. Quick reactions. Thinking out loud. But not all brains work that way. For many neurodivergent professionals: • Processing happens internally before speaking • Interruptions derail working memory • Large group meetings drain cognitive energy • Social performance ≠ capability And when silence is misread as disengagement? People start masking. Over-talking. Over-explaining. Over-compensating. And eventually… burning out. Inclusive leadership isn’t about making people louder. It’s about making space. Practical shifts that change everything: ✔ Send questions in advance ✔ Normalise “I’d like to think about that” ✔ Build in written contributions ✔ Don’t equate confidence with competence ✔ Rotate how input is gathered Belonging isn’t about fitting in. It’s about not having to perform to be valued. Leaders – what small shift has made the biggest difference in your team? Neurodivergent professionals, what helps you contribute at your best? #NeurodiversityAtWork #InclusiveLeadership #AutismAtWork #ADHDAtWork #BelongingNotFittingIn #LeadershipDevelopment

I’m so over the "tech bro" narrative in AI. ✋ If we let a handful of guys in a Silicon Valley bubble build the entire future, they’re going to build a world that doesn’t even know women exist. The AI world is starving for people who actually understand how the real world works. These 6 women just showed up and rewrote the rules: 1. Dr. Joy Buolamwini: When she realized AI couldn't "see" her face without a white mask, she didn't just file a complaint. She took the fight to IBM and Amazon until they pulled facial recognition tech from police use. Lesson: When the system is blind to you, don’t ask for glasses. Build a new system. 2. Timnit Gebru: She called out bias from inside Google. They fired her. So, she built her own independent research institute to study AI without a corporate muzzle. Lesson: Getting pushed out by the big players usually means you’re too dangerous for their narrative. 3. Lila Ibrahim: No "startup kid" origin story here. She spent 18 years at Intel before scaling Google DeepMind. Lesson: You don’t have to be the "founder" to be the smartest operator in the room. 4. Jaime Teevan: The brain making sure Microsoft’s AI actually helps humans work instead of just making noise. She’s obsessed with the human result, not just the engineering. Lesson: Empathy is a technical requirement. 5. Noelle R.: She saw a world of executives secretly terrified of AI and built a bridge for them. She’s shown 3.4 million people that AI isn't magic...it’s a tool. Lesson: Confusion is always an opening for leadership. 6. Susan Gonzales: Founded AIandYou to make sure AI education didn’t stay locked in expensive boardrooms. Lesson: Social impact and cutting-edge tech are not opposites. If you feel like you’ve "missed the boat" on AI, throw that thought away. Our perspective, "battle scars," and our lived experience are the only things that will keep this tech from flying off the rails. The future is being written right now. Don’t just read it. Grab the pen. 🖋️ P.S. Who is a woman in tech who makes you think, "If she can do it, I definitely can"? Tag her below and let’s give her some flowers today. 👇💕 #ai #womenintech #executivepresence #leadership #futureofwork

The biggest threat to your data isn’t happening tomorrow. It happened yesterday. If you haven’t heard of HNDL (Harvest Now, Decrypt Later), your long-term data strategy has a massive blind spot. Here is the reality: State actors and cybercriminals are capturing your encrypted data today. They can’t read it yet, so they’re storing it in massive data vaults, waiting for the "Qday"—the moment quantum computers become powerful enough to break current encryption. If your data needs to stay private for 5, 10, or 20 years, it’s already at risk. What’s on the line? ↳ Intellectual Property (IP) and trade secrets. ↳ Government and identity data. ↳ Long-term financial records and contracts. ↳ Sensitive customer health data. How do we solve it? 🛠️ We cannot wait for quantum supremacy to react. The fix starts now: ↳ Inventory: Identify which data has a long shelf-life. ↳ Crypto-Agility: Move toward systems that can swap encryption methods without a total overhaul. ↳ Hybrid PQC: Implement Post-Quantum Cryptography alongside classical methods to ensure traffic captured today remains a mystery tomorrow. The transition to quantum-resistant security is a marathon, not a sprint. Are you tracking HNDL on your current risk register? Let’s discuss in the comments. 👇 P.S. If you want help mapping your exposure or building a PQC migration plan, drop me a message. ♻️ Share this post if it speaks to you, and follow me for more. #QuantumSecurity #PQC

Technical leadership isn’t about knowing everything. It’s about creating a framework where the right answers can emerge. 1 / The best leaders don’t rush to provide solutions. Instead, they frame the problem so the team can see it clearly and then step back. Leadership isn’t about showing what you know — it’s about empowering others to think. 2 / In technical fields, complexity is the enemy of progress. Great leaders aren’t adding layers; they’re stripping them away. They know that simplicity and clarity are often harder to achieve but lead to solutions that last. 3 / And the strongest leaders don’t seek followers; they build thinkers. A team that only executes orders will eventually stall. A team that questions, explores, and challenges assumptions will find breakthroughs you can’t plan for. True technical leadership isn’t about controlling every detail — it’s about fostering an environment where innovation thrives. Thoughts??

India: Beyond Code and Cost As an ecosystem well wisher and participant GCC Leader, Over the past few weeks, I’ve had the privilege of meeting several Fortune company CXOs who are preparing to establish or expand their Global Capability Centres (GCCs) in India — especially in Hyderabad, which now leads the country in attracting new GCC investments. But what’s powering this shift goes beyond code and cost. It’s about Cognitive (AI +NI) fluency — and a cultural fluency that runs thousands of years deep. In today’s VUCA world — defined by volatility, uncertainty, complexity, and ambiguity — India has become a source of stability and scale for global enterprises. The country’s unique blend of technical excellence, adaptability, and human-centered intelligence is helping organizations build resilience amid global disruptions. India’s strength is not accidental — it’s rooted in a 5,000-year-old learning culture. From the Gurukul system, where learning meant living, sharing, and growing together under a Guru’s guidance, to Nalanda University, the world’s first global learning hub in 5th century BC, education in India has always been holistic — developing not just the intellect but the character. That legacy continues today. With 3 million graduates produced annually, India is the talent capital of the world. Our people’s deep cultural grounding and curiosity have evolved into something powerful: cultural fluency, the ability to collaborate across borders with empathy and nuance. Now, this is converging with Cognitive (AI )fluency. Indians are among the fastest adopters of AI globally, bridging technology and human intuition like no other workforce. It’s no surprise that India now hosts 1,800+ Global Capability Centres, employing over 2 million professionals and contributing an estimated USD 64 billion to the global enterprise value chain. These GCCs are not just back offices — they are strategic leadership hubs, driving innovation, transformation, and global decision-making. Whatever business you lead — India based GCC is no longer just an option. It’s the launchpad for global leadership and to be the leader, not a laggard in next economic cycle. Culture and congnitive (AI) fluency are the new catalysts for GCCs now. #GCCs #ANSR #GlobalCapabilityCentres Lalit Ahuja, Prakash Bodla, Pari Natarajan,Sameer Dhanrajani, Sandeep Sharma,Rajesh Kumar Ojha,Kanwar Singh,Sailaja Josyula (She / Her)

We’ve all seen how quickly a single moment on social media can spiral. One tone-deaf comment, one AI-generated response that misses the mark, or just a slow internal handoff and suddenly, your brand is trending for all the wrong reasons. When I started building our AI-First Mindset™ transformation program, I knew we couldn’t just focus on opportunity. We also had to prepare leaders for risk and that includes public-facing crises fueled by speed and automation. That’s why I developed a new module focused on building a social media crisis management plan designed for today’s AI-powered workplace. We cover the essentials: • How to build a clear, flexible crisis communication plan • The best crisis management tools to monitor and respond in real time • How to define team roles across marketing, legal, leadership and tech • And how to account for AI-powered systems that can escalate issues if not handled properly In a world where content and backlash move at machine speed, your people need clarity. That starts with a plan that’s actually usable and practiced before the pressure hits. This isn’t about fear. It’s about preparation. AI adoption comes with incredible potential, but it also changes how we manage trust. A good crisis response needs to e part of your broader AI change management strategy. If your team is using AI but hasn’t revisited your crisis plan, now’s the time. Stay tuned for practical guidance on creating crisis plans that perform under pressure. #DigitalCrisisStrategy #CrisisCommunication #CrisisResponse #DigitalCrisis #SocialMediaCrisis

Hard truth: Most leaders fail their teams during uncertain times. Not because they make bad decisions - But because they disappear when their teams need them most. I've been that leader. Thinking I needed all the answers... Only to create a vacuum filled with anxiety, speculation, and fear. Leadership is easy when things are going well. It matters most when the going gets rough. And here's what your team actually needs from you: Not perfection. Not all the answers. Just your presence and support. This means: • Saying "I don't know yet, and here's what we're doing to find out" • Listening without immediately jumping to solutions • Sharing what you can, when you can—even if it's incomplete • Maintaining optimism while acknowledging real challenges • Showing up consistently, especially when it's uncomfortable 6 ways to put this into practice: 𝟭. 𝗟𝗶𝘀𝘁𝗲𝗻 𝗔𝗰𝘁𝗶𝘃𝗲𝗹𝘆 (𝗥𝗲𝗮𝗹𝗹𝘆 𝗟𝗶𝘀𝘁𝗲𝗻) Ask "Do you want me to just listen, or would you like help solving this?" Try: Set up an anonymous feedback channel 𝟮. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝗰𝗮𝘁𝗲 𝗧𝗿𝗮𝗻𝘀𝗽𝗮𝗿𝗲𝗻𝘁𝗹𝘆 (𝗔𝗴𝗮𝗶𝗻 𝗮𝗻𝗱 𝗔𝗴𝗮𝗶𝗻) Even “no update” is an update. You’re only halfway communicated when you feel done. 𝟯. 𝗖𝘂𝗹𝘁𝗶𝘃𝗮𝘁𝗲 𝗢𝗽𝘁𝗶𝗺𝗶𝘀𝗺 (𝗪𝗶𝘁𝗵 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸𝘀) Start your next meeting with wins. Create a shared space (Slack channel, doc) where the team posts progress. The flywheel: Optimism → Action → Progress → Confidence → More Optimism 𝟰. 𝗞𝗲𝗲𝗽 𝘁𝗵𝗲 𝗧𝗲𝗮𝗺 𝗙𝗼𝗰𝘂𝘀𝗲𝗱 (𝗢𝗻 𝗪𝗵𝗮𝘁 𝗧𝗵𝗲𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹) Draw the Control Circle: What do we control, influence, or just observe? Invest 80% of your energy in what you 𝘰𝘸𝘯. 𝟱. 𝗗𝗼𝘂𝗯𝗹𝗲 𝗗𝗼𝘄𝗻 𝗼𝗻 𝗘𝗺𝗽𝗮𝘁𝗵𝘆 Ask these 4 questions in 1:1s: • What excites you? • What worries you? • What support do you need? • What’s in your way? 𝟲. 𝗕𝗲 𝗔𝘃𝗮𝗶𝗹𝗮𝗯𝗹𝗲 𝗮𝗻𝗱 𝗩𝗶𝘀𝗶𝗯𝗹𝗲 Host office hours and “ask me anything” sessions. Presence builds trust. 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: You can't pour from an empty cup. Prioritize your own well-being—it's not selfish, it's essential for your team's success. Your team can handle uncertainty. They can't handle feeling abandoned in it. Start with one action. Build from there. What would you add to this list? 💾 Save this post for when you’ll need it.