Trusted AI Resources for Community Leaders

Charity Leaders & AI: Where Do We Start? 🤖 I've spent the last few years helping charities embed digital (and increasingly AI) into their core mission. AI was today's topic on the Third Sector Lab x SCVO Digital Senior Leaders Programme with me, John Fitzgerald and Maddie Stark Here's the questions charity leaders need to ask plus a few practical ways to move the conversation from hype to strategy 👇 The Big Questions We Need to Ask❓ - Where is AI already affecting our mission—positively or negatively? - How empowered (or anxious) do our staff and volunteers feel about AI? - Which parts of our work could AI actually improve (reach, impact, efficiency)? - Do we understand the risks—data, ethics, trust? How will we keep our values central? - Who else in our network is experimenting with AI and what are they learning? Five Practical Steps for AI-Ready Leaders 5️⃣ AI Impact Mapping 🗺️ Bring your team together. Map every touchpoint where AI could play a role - from fundraising and supporter comms to governance and frontline service. Pinpoint where the real wins and risks are for your charity. Staff & Volunteer Pulse Check 🩺 Run a session where people role-play different AI scenarios. What opportunities and anxieties bubble up? (Be ready for honest feedback!) Use it as a way to shape your AI literacy and support plans. Debate Real-World AI Use Cases 👥 Share case studies: the good, the bad, and the complex. Chatbots for helplines? Automated grant app sorting? Data-driven supporter segmentation? Debate - don’t sell - the practicalities and ethical red lines. Risk & Governance Tabletop 🎲 Role play as trustees, comms, digital leads, service staff—respond to an data breach as a result of AI usage or staff concerns about AI bias in recruitment. Work out who needs to be in the room when things go wrong, and what new protocols may be needed. Quickfire AI Experiment 🧪 Have your team test a popular AI tool - draft a donor email, summarise a board paper, generate a campaign image. Use Co-Pilot, ChatGPT, Perplexity, Claude, Gemini or whatever tool is most relevant to your needs. Compare notes: What worked, what failed, where was human oversight crucial? Make Space for Messy Conversations 🪢 - Is AI use visible or happening “off the books?” - What would success - or failure - with AI look like for us next year? - How can we work across the sector for stronger, more ethical approaches? - What are the values we refuse to compromise on, no matter what shiny AI tool we see? Don’t Forget: Make It Actionable 💪 - Finish your next senior team meeting with a commitment - Run a staff survey on AI - Pilot a small AI project - Join or create a sector AI peer group If you’ve taken baby steps, had a tough internal debate, or even failed spectacularly, or you just want to share a handy resource - I want to hear about it in the comments 👇

#AiDays2025 Round Table : #Community Sourcing for low resource languages In an era where AI is fast shaping the contours of our digital future, VISWAM.AI initiative stands as a timely and transformational one. Their mission to build community-sourced Large Language Models (LLMs), grounded in India’s rich linguistic and cultural diversity, is not just pioneering—it’s redefining how inclusive and ethical AI should be built. By anchoring their work in community participation, linguistic preservation, and ethical co-creation, Viswam.ai offers a people-first approach to AI—moving beyond data extraction to cultural stewardship. Their ambition to mobilize 1 lakh community interns to collect data from underrepresented geographies across India is both bold and brilliant. This isn’t just about building better AI—it’s about building equity, agency, and cultural resilience through AI. 1. Linguistic Equity by Design In India, where linguistic hegemony often privileges English and Hindi, AI systems risk reinforcing this imbalance. The solution? Intentional design. Allocate equal engineering and validation efforts to low-resource languages. Ethical AI must be built on informed consent, community ownership, and fair compensation—because data is not just input, it’s identity and heritage. 2. Decentralized Internship Model By decentralizing AI development, we bridge the urban-rural digital divide. This model should focus on: Capacity building through training in ethics and digital literacy Inclusivity by involving women, Dalit and Adivasi youth Localized platforms using mobile-first tools in native languages Partnerships with Swecha, local NGOs, and institutions serve as trust bridges to ensure mentorship and sustainability. 3. Tools for Low-Resource Languages Many Indian languages are oral-first, with complex dialects and sparse corpora. Community-driven solutions—like collecting voice datasets from folklore, and crowdsourcing annotation—are key. Elders, poets, and storytellers become linguistic technologists, preserving not just language but legacy. 4. Trust & Transparency Bias in AI is structural. To mitigate it: Include diverse dialects and accents in training Conduct bias testing and community validation Promote explainable AI with local language dashboards and storytelling What’s Next? A living white paper on ethics, governance, and technical guidelines A roadmap for the internship program, with toolkits and impact metrics Collaboration with literary and linguistic organizations to enrich model depth VISWAM.AI is planting seeds for an AI movement rooted in language justice, data sovereignty, and community wisdom. Let’s co-create systems that don’t just understand our languages—but respect our voices. DC* Chaitanya Chokkareddy Kiran Chandra Ramesh Loganathan Centific

In my work with organizations rolling out AI and generative AI solutions, one concern I hear repeatedly from leaders, and the c-suite is how to get a clear, centralized “AI Risk Center” to track AI safety, large language model's accuracy, citation, attribution, performance and compliance etc. Operational leaders want automated governance reports—model cards, impact assessments, dashboards—so they can maintain trust with boards, customers, and regulators. Business stakeholders also need an operational risk view: one place to see AI risk and value across all units, so they know where to prioritize governance. One of such framework is MITRE’s ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) Matrix. This framework extends MITRE ATT&CK principles to AI, Generative AI, and machine learning, giving us a structured way to identify, monitor, and mitigate threats specific to large language models. ATLAS addresses a range of vulnerabilities—prompt injection, data leakage, malicious code generation, and more—by mapping them to proven defensive techniques. It’s part of the broader AI safety ecosystem we rely on for robust risk management. On a practical level, I recommend pairing the ATLAS approach with comprehensive guardrails - such as: • AI Firewall & LLM Scanner to block jailbreak attempts, moderate content, and detect data leaks (optionally integrating with security posture management systems). • RAG Security for retrieval-augmented generation, ensuring knowledge bases are isolated and validated before LLM interaction. • Advanced Detection Methods—Statistical Outlier Detection, Consistency Checks, and Entity Verification—to catch data poisoning attacks early. • Align Scores to grade hallucinations and keep the model within acceptable bounds. • Agent Framework Hardening so that AI agents operate within clearly defined permissions. Given the rapid arrival of AI-focused legislation—like the EU AI Act, now defunct  Executive Order 14110 of October 30, 2023 (Safe, Secure, and Trustworthy Development and Use of Artificial Intelligence) AI Act, and global standards (e.g., ISO/IEC 42001)—we face a “policy soup” that demands transparent, auditable processes. My biggest takeaway from the 2024 Credo AI Summit was that responsible AI governance isn’t just about technical controls: it’s about aligning with rapidly evolving global regulations and industry best practices to demonstrate “what good looks like.” Call to Action: For leaders implementing AI and generative AI solutions, start by mapping your AI workflows against MITRE’s ATLAS Matrix. Mapping the progression of the attack kill chain from left to right - combine that insight with strong guardrails, real-time scanning, and automated reporting to stay ahead of attacks, comply with emerging standards, and build trust across your organization. It’s a practical, proven way to secure your entire GenAI ecosystem—and a critical investment for any enterprise embracing AI.

𝟱 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆𝘀 𝗼𝗻 𝗛𝗼𝘄 𝗡𝗼𝗻𝗽𝗿𝗼𝗳𝗶𝘁𝘀 𝗕𝗲𝘀𝘁 𝗟𝗲𝗮𝗿𝗻 𝗔𝗜 The Nonprofit Jam helped us at OpenAI identify strategies for supporting nonprofits to adopt AI with confidence. The biggest breakthroughs came from making AI approachable, giving people space to experiment, and showing real examples from peers they trust. With hands-on practice, ready-to-use resources, and support from our tech mentors, participants left with both the skills and the confidence to keep building. Here are five key takeaways: 𝟭. 𝗢𝘃𝗲𝗿𝗰𝗼𝗺𝗶𝗻𝗴 𝗵𝗲𝘀𝗶𝘁𝗮𝘁𝗶𝗼𝗻 𝗮𝗻𝗱 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲 𝗶𝘀 𝘁𝗵𝗲 𝗳𝗶𝗿𝘀𝘁 𝘀𝘁𝗲𝗽 We learned that the biggest barrier isn’t technology; it’s uncertainty about where to begin. Programs that demystify AI, give permission to experiment, and frame ChatGPT as a collaborator—someone who can refine prompts, draft GPT instructions, or troubleshoot—help spur curiosity. A great first step: ask, “I’m a [job role] at [org]. What are 10 ways ChatGPT could help me?” 𝟮. 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆-𝗯𝗮𝘀𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗰𝗰𝗲𝗹𝗲𝗿𝗮𝘁𝗲𝘀 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 We also found that spotlighting credible, mission-aligned examples from trusted nonprofit peers and rooting learning in local context helps participants see AI as relevant and attainable. Peer examples break down perceptions that AI is “for someone else” and accelerate adoption. 𝟯. 𝗛𝗮𝗻𝗱𝘀-𝗼𝗻, 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝗲𝗱 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗶𝘀 𝗲𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹 𝗮𝗻𝗱 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝘀 𝗶𝗺𝗽𝗮𝗰𝘁 We learned that nonprofit staff benefit from structured, facilitated environments where they can practice with AI and apply it to real challenges. This is more effective than self-study and helps turn abstract potential into practical skills. Tech mentors, peer exchange, and local conveners who foster a trusted, inclusive environment and understand nonprofit contexts dramatically improve learning outcomes and model how to continue experimenting after the event. 𝟰. 𝗧𝗮𝗻𝗴𝗶𝗯𝗹𝗲, 𝗿𝗼𝗹𝗲-𝘀𝗽𝗲𝗰𝗶𝗳𝗶𝗰 𝗿𝗲𝘀𝗼𝘂𝗿𝗰𝗲𝘀 𝗱𝗿𝗶𝘃𝗲 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 Prompt cookbooks, templates, and other ready-to-use resources make it easy to see “what good looks like” and to adapt AI for real-world tasks. These materials help bridge the gap between possibility and action. 𝟱. 𝗔𝗱𝗱𝗿𝗲𝘀𝘀𝗶𝗻𝗴 𝗰𝗼𝗻𝗰𝗲𝗿𝗻𝘀 𝘂𝗽𝗳𝗿𝗼𝗻𝘁 𝗯𝘂𝗶𝗹𝗱𝘀 𝗶𝗻𝗳𝗼𝗿𝗺𝗲𝗱, 𝗰𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝘁 𝘂𝘀𝗲𝗿𝘀 Finally, we learned that answering common questions about AI (e.g. hallucinations, bias, and privacy concerns, and when not to use AI) helps nonprofits make sound judgment calls and adopt the technology responsibly. Read more in our Nonprofit Jam After-Action Report here: https://lnkd.in/efsYYjwi

I'm excited to share our AI checklist for charity trustees and leaders. Nick Scott and I have developed this free resource to help boards, and those who work with them, start the conversation about #ArtificialIntelligence , review progress and plan for the future, whatever stage you are at. The checklist has been a big team effort and we are so grateful to all the charities and organisations who have helped shape it. We've had some great feedback from charities of different sizes and causes, including Hospice UK Wikimedia UK and Christian Aid. Thank you to everyone who participated in the user testing, including the Charity Commission for England and Wales. There is so much happening in AI at the moment it's really important that we all learn together. We hope you find the checklist useful and would love to hear what you think of it. https://lnkd.in/eUBgGW7i Alongside the checklist, and as requested by the organisations we tested it on, we've published a blog for anyone who is new to AI, covering what AI is, how charities are using it and how to take your first steps with it. I'll put the link to this, along with our launch webinar today, in the comments. #Charities #TrusteesWeek #CharityAIChecklist

If my friend and project partner, Michelle shared earlier this week 9 questions you can take from the AI Equity Project, I would like to take it a step further. (here attached are the 9 questions she shared earlier this week). Let's talk about your allyship actions (+ useful resources tagged here). Because allyship must be purposeful, meaningful, and powerful, here are 5 ways you can be an ally to the ideas of equitable, responsible, beneficial AI in the nonprofit sector today: ● Advocate within your circles: Your voice matters. If you're on a board or part of a philanthropic organization, you have the power to influence. Encourage decision-makers to embed AI equity in funding guidelines and project criteria. Use resources linked here to make your case. ● Build bridges: Introduce your staff and sector peers to tech partnerships or consultants who prioritize ethics and inclusivity in AI. Your network can be a bridge to equitable innovation. ● Become a fiscal ally: Support research and capacity-building for nonprofits embracing equitable AI. Consider becoming a fiscal sponsor for research projects focused on AI equity or funding scholarships for nonprofit professionals attending AI training. To learn more about the AI Equity Project's sponsorship details, message me to get a copy. ● Boost the signal: Sharing resources is a powerful way to spread knowledge and foster understanding. Share resources like this report or the ones below, and invite people to present to your team, board, or community. Education is the first step to transformation, and in-depth conversations about the future. ● Center community voices: Support your team and sector peers by asking better questions and including diverse voices in AI evaluations. Help them find ways to elevate marginalized perspectives in their decision-making processes. Meaningful change requires all of us to act—together. As the last AI post for the year, tagging projects I have been involved in/following closely this year: ● GivingTuesday's AI Readiness Report: https://lnkd.in/dSXz92_t ● Donor Perceptions about AI (project by Nathan Chappell, MBA, MNA, CFRE and Cherian Koshy): https://lnkd.in/dnpXdrYt ● AI Equity Project (thanks to the Giving Compass team for their partnership in this year's work on this project): https://lnkd.in/gX8AX-eZ Also tagging some people/groups you should follow when thinking about AI (they all make valuable tools, resources, courses, and more….): the GivingTuesday team, Fundraising.AI, Tim Lockie, Brandolon Barnett, Anne Murphy, Beth Kanter, Rachel Kimber, MPA, MS, Joanna Drew, John Kenyon, David Norris… who else am I missing? If you are interested in finding someone in particular in this AI space, send me a message, and I promise to make the connections....(social media tagging is not my best game). Let's continue building an equitable AI ecosystem, one step at a time! #nonprofits

🔹𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝗟𝗲𝗮𝗱𝗶𝗻𝗴 𝗥𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗹𝘆 𝗶𝗻 𝟮𝟬𝟮𝟱🔹 In my latest blog, "𝗔𝗜 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲: 𝗛𝗼𝘄 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗖𝗮𝗻 𝗠𝗮𝗻𝗮𝗴𝗲 𝗥𝗶𝘀𝗸𝘀 𝗮𝗻𝗱 𝗨𝗻𝗹𝗼𝗰𝗸 𝗩𝗮𝗹𝘂𝗲", I explore why governance is not just a safeguard but a leadership responsibility, and how leaders can act now to manage risks and unlock sustainable value. AI is powering decisions, shaping outcomes, and driving business results. But without governance, the risks can outweigh the rewards. From frozen bank accounts to deepfake scams, we have seen what happens when oversight is missing. AI governance is not a technical detail. It is a leadership responsibility. 💡𝗪𝗵𝘆 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗠𝗮𝘁𝘁𝗲𝗿𝘀: ✅ Protects trust with customers, regulators, and employees ✅ Ensures AI is fair, transparent, and explainable ✅ Reduces bias, compliance gaps, and reputational harm ✅ Creates long-term value by aligning innovation with ethics 🔍𝗙𝗼𝘂𝗿 𝗣𝗶𝗹𝗹𝗮𝗿𝘀 𝗳𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝘁𝗼 𝗔𝗻𝗰𝗵𝗼𝗿 𝗢𝗻: 1️⃣ Transparency: Make AI decisions explainable in plain terms 2️⃣ Accountability: Keep clear ownership, never shift blame to “the algorithm” 3️⃣ Fairness: Audit regularly to test for bias and inequality 4️⃣ Security: Guard against data leaks, misuse, or manipulation 📌𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗔𝗰𝘁𝗶𝗼𝗻𝘀 𝘁𝗼 𝗧𝗮𝗸𝗲 𝗡𝗼𝘄: ✔️ Map every AI system in use and rank its significance ✔️ Create cross-functional oversight to avoid blind spots ✔️ Run audits on data, bias, and security ✔️ Train teams to understand how AI works and where it is applied ✔️ Engage regulators and stakeholders to stay ahead of compliance 🌍𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲𝘀: 🏥 Healthcare: AI supports diagnoses, but doctors remain accountable 🛒 Retail: Bias audits prevent recommendation engines from reinforcing old patterns 🏦 Finance: Oversight committees review all AI deployments before launch The leadership opportunity is clear. AI governance is not about slowing progress. It is about creating trusted innovation that lasts. 🔗 𝗙𝘂𝗹𝗹 𝗯𝗹𝗼𝗴 𝗽𝗼𝘀𝘁 𝗵𝗲𝗿𝗲: https://lnkd.in/g7yAtCY9 What is the toughest AI governance challenge you face right now? Share your thoughts below. #Leadership #AI #Governance #Ethics #RiskManagement #ExecutiveLeadership #BusinessAgility

#AI in cities. This is a great resource for local leaders looking to understand, and leverage, #AI. Tons of real-world use cases that illustrate how the public sector is using #AI. Here are some of the most interesting use cases: ▪️ Memphis, TN: Pothole detection ▪️ Tucson, AZ: Water infrastructure management ▪️ City of Chattanooga, TN: Prompt library to help staff ▪️ Dearborn, MI: Language translation ▪️ Sunnyvale, CA: Language translation in public meetings ▪️ Ann Arbor, MI: "Ask Ann" Chatbot (love the name!) ▪️ Seattle, WA: Traffic pattern analysis ▪️ New York, NY: AI-driven intervention program (increasing graduation rates by 32%) ▪️ Dublin, Ireland: Public sentiment analysis ("The Dublin Beat") ▪️ Montreal, Canada: Fraud prevention with snow removal ▪️ Barcelona, Spain: Inquiry management using NLP The guide is broken out into two sections: (1) Exploring AI & (2) AI in Cities Toolkit. Below are more details on the sections. Exploring AI 🔹Understanding AI for local governments 🔹Responsible AI use for local governments 🔹Harnessing AI for local governments 🔹AI in global cities AI in Cities Toolkit 🔹Getting started with AI 🔹Landscape analysis (Case Study: San Francisco) 🔹AI readiness assessment 🔹Developing a municipal AI use policy or guidelines 🔹FAQ template Report Source: https://lnkd.in/ekNcemEj

Would you like my curated list of 20 go-to resources for understanding AI and its impact on individuals, teams, and organizations? Well - this post is exactly that! 👍👍 I’ve pulled together a curated list (with links) to resources I personally follow, trust, and recommend to anyone looking to stay ahead in the AI space. Whether you’re a leader making strategic AI decisions, a change practitioner driving adoption, or just someone curious about how AI is reshaping work, this list has you covered. You’ll find articles, blogs, podcasts, webinars, and pretty much everything in between. Many of these are ones I have on “periodic repeat” – tucked away for a re-watch or re-read because of the insights loaded within. So, without further adieu... 📚 Enterprise AI Resources, References, and Recommendations – from the desk of Tim Creasey 🔹 The list is divided into five key categories: ✅ Favorites – The essential AI content that has most shaped my thinking. These are definitely on the repeat list. ✅ The State of Enterprise AI (with data) – Reports and surveys that give a data-driven view of organizational AI adoption, today. ✅ People Side of Enterprise AI – How AI is transforming people, teams, and processes. ✅ More Foundation + Year in Review – Contextual and retrospective pieces that track AI’s evolution. ✅ My Own Work – My contributions, insights, and frameworks on AI adoption. A few standout recommendations from the list: 📌 "17 Reflections on Enterprise AI in 2024" – AI Daily Brief (by Nathaniel Whittemore) – A high-level retrospective that sets the stage for what’s coming in 2025. 📌 "Which AI to Use Now" – Ethan Mollick – A practical, research-backed guide on AI capabilities and leading tools. 📌 "2024: The State of Generative AI in the Enterprise" – Menlo Ventures – A data-rich look at AI adoption, investment, and strategy. 📌 "Unlocking AI Adoption" – Prosci – A thought leadership piece on integrating AI successfully within organizations. 📌 "GenAI Exists Because of the Transformer” – The Financial Times – A seminal, visual piece that shaped how I understand how GenAI works. So as of January 29, 2025 – this is what I've been listening to, reading, and watching. AI is evolving at an incredible pace; so should our understanding! Please keep the conversation gong by sharing an insight you took from one of these resources, or one of your favorite go-to AI resources! Drop them below! ⬇️ #AI #EnterpriseAI #AIAdoption #ChangeManagement #AIResources #DigitalTransformation

Everyone's panicking about AI taking their jobs. I'm focused on using it to amplify my truth. Because AI can write your emails. But it can't replace your wisdom. And it can't feel your conviction. The leaders who win will be the most intentionally human. I curated the Top 10 Free AI Courses for leaders who want to master the tools without losing their voice. Authentic AI leadership looks like: ✅ Automating tasks, not your thinking ✅ Enhancing your judgment, not outsourcing it ✅ Using AI to amplify your message, not manufacture it ✅ Leveraging tools to create space for deeper human connection Stop fearing AI. Start filtering it through your values. My top 10: 1. Google Cloud: Introduction to Generative AI Learn what generative AI is, how it works, and how it applies in business. 🔗 https://lnkd.in/e4tvhZ4v 2. Microsoft: AI Skills / Fundamentals Start with AI basics, then move to neural networks and deep learning. 🔗 https://lnkd.in/eY5atwHv 3. OpenAI Academy Learn AI from the team behind ChatGPT. 🔗 https://lnkd.in/e9AUH5fE 4. Harvard CS50: Introduction to AI with Python Learn AI fundamentals with hands-on coding in Python. 🔗 https://lnkd.in/eYy8W_hb 5. AWS: Foundations of Prompt Engineering Master principles and best practices for effective prompts. 🔗https://lnkd.in/eNkkkAcr 6. Vanderbilt: Prompt Engineering for ChatGPT Techniques to design great prompts. 🔗 https://lnkd.in/e8V4Ci9q 7. Coursera: Retrieval Augmented Generation (RAG) Design and build RAG systems that connect AI to your own data. 🔗 https://lnkd.in/eibVq-KX 8. Responsible AI: Applying AI Principles with Google Cloud Learn principles of ethical and responsible AI practices. 🔗https://lnkd.in/eay7tMpS 9. IBM: Generative AI for Executives and Business Leaders Specialization Leverage genAI to drive strategic innovation. https://lnkd.in/eQK8h2EV 10. AI Skills 4 Women (Founderz + Microsoft) AI training designed for women. 🔗 https://lnkd.in/exrwVRzh BONUS: Google AI Essentials Explore Google’s AI tools, tutorials, and learning paths. 🔗 https://grow.google/ai/ The future belongs to leaders who are irreplaceably human AND strategically augmented. That's you. If you choose it. ⬇️ Download the high-res cheatsheet + my AI Starter Pack for Authentic Leaders: https://lnkd.in/eKCnp_Cg Which skill will you add to your leadership toolkit? ---------- ♻️ REPOST to help a leader stay human in the AI age ➕ Follow Tash Durkins 🦋 CPC for authentic AI leadership 📌 Ready to lead the future? https://lnkd.in/edvRXWvu