Tech Community Building

What if the most important AI role in your organization requires zero technical expertise? This isn't a provocative question for the sake of being provocative. It's a genuine insight from watching dozens of organizations struggle and succeed with AI adoption. Because here's what most miss: This isn't a technology adoption curve. It's a human transformation curve. And the people who catalyze that transformation often aren't the technical experts. If your organization is betting big on AI engineers while ignoring community builders, you might as well light your transformation budget on fire. I watched a Fortune 500 company spend millions on AI talent only to see adoption stall in the low teens %. Meanwhile, a government agency with a tiny fraction of that budget achieved organization-wide transformation mostly through the efforts of a single individual passionate enough to organize monthly Zoom calls. One of my most-beloved recent posts describes how a Facility Manager at the National Park Service—with no technical background whatsoever—built an AI tool in 45 minutes that's saving thousands of days of labor across the park system. Adam, the facility manager, created a simple tool that automated the creation of complex funding request documents. The impact was staggering: what used to take days now took minutes. His colleagues started sharing the tool, and soon facility managers across the country were using it. People loved this story because it showed that AI impact doesn't require a computer science degree or coding expertise. Just curiosity, a clear problem, and 45 minutes. But here's what I didn't tell you in that post: Adam's breakthrough wasn't spontaneous. It was the direct result of someone working upstream. Want to identify the Adams in your organization and unlock opportunities like the one he found? You need to look farther upstream to a role I've rarely seen discussed: the Convener. At the National Park Service, that person is Cheryl Eckhart. While many organizations are busy chasing the shiny new AI tools, Cheryl was doing something far more radical: creating a space where people could actually use them. Months before Adam built his tool, she had taken the initiative to start convening a community of practice around AI. With no technical background herself, she understood something more fundamental: people need spaces to learn, experiment, and share. Cheryl signs every email with "Today is a great day for learning"—a philosophy that defines her approach. She's not an AI expert; she's a catalyst who creates the conditions for others to become AI experts. What does this look like in practice? Detailed notes from what I’ve seen in Cheryl and other conveners in the full post linked below (LinkedIn character limits!). cc Christa Stout

The Paradox of Growth: The Bigger You Get, the Less You Know I came across something that stuck with me: When companies scale, they gain users — but lose understanding. Not because they stop caring, but because their customer feedback starts living everywhere — support tickets, sales calls, forums, surveys, social media, and app store reviews. That thought really made me pause. I’ve seen this firsthand. When a company is small, every piece of feedback feels personal — every bug report or review has a face behind it. But as you grow, those voices scatter across platforms and departments. Support sees the frustration, sales hears the hesitation, leadership sees the numbers — and somehow, everyone’s looking at the same customers, but no one’s hearing them anymore. That, in my opinion, is the quiet cost of growth. This is the problem Enterpret is solving — by helping teams stay in tune with their customers even as they scale. Here’s how it works: → It collects real-time customer feedback from 55+ channels — support tickets, sales calls, social media (X, Reddit, Instagram, Facebook), app store reviews, community forums, surveys, Slack, and more. → It analyzes all that feedback using AI and tells you exactly what to fix or build next. → It maps everything through a customer knowledge graph that connects feedback, complaints, and requests by channel, user, and payment data. → It even provides a chat interface where you can directly ask questions, and AI agents that flag bugs or issues automatically. That’s why teams like Notion, Perplexity, Canva, Chipotle, and The Farmer’s Dog use it — to make sure customer voices never get lost in the noise. In my view, the real lesson here isn’t about using more tools — it’s about staying close to the people you build for. Here’s how I’d approach it: ✅ Centralize every piece of feedback — even if it’s messy. ✅ Look for patterns instead of isolated complaints. ✅ Use AI systems like Enterpret to uncover the “why” behind what customers say. Because in the end, growth shouldn’t make you deaf. It should make you listen better — just faster. How does your team make sure you’re hearing what customers really mean, not just what they say? #CustomerFeedback #AIProducts #ProductStrategy #VoiceOfCustomer #Enterpret #Leadership

Most engineers use open source every single day. But the ones who contribute to it- especially early- end up with a profile that's just different from everyone else in the room. Google Summer of Code is one of the most structured ways to get there. Google pays you to contribute to real, production-level open source projects over 3 months. You build in public, you work on actual codebases, and it's one of those opportunities that can genuinely change the trajectory of an engineering career. So when I found out that 13 students from Scaler School of Technology made it to GSoC this year, I wanted to understand what actually worked for them. What makes it even more unique is that GSoC isn't just for students. Working professionals participate too, which makes every selection from a college batch much more meaningful. SST built a mentorship culture that solves for the three things that hold most students back. 🔸Awareness- Knowing that this opportunity exists and that it is genuinely meant for someone like you. 🔸Navigation- Understanding how the process works, what a strong proposal looks like, and how to approach the right open source organizations. 🔸Belief- Actually believing you have a shot at it. The mentors at SST focused on exactly these three things. They didn't write proposals. They didn't do the work for anyone. They made the path visible, stayed close as accountability partners, and let the learners take full ownership. What made it compound further was SST's peer-mentorship model-  students who went through the process last year mentored this year's batch. The 2 mentors in this cohort were learners themselves not long ago. That's a great way to build a culture- not by telling people it's possible, but by showing them someone exactly like them already did it. 13 students got in this year- 2 as mentors, 11 as contributors. They showed up, did the work, and now they'll be contributing to codebases that millions of engineers use every day. For a college that's only a few years old, having GSoC selections back to back is not a small thing. It means something real is being built here. If you're an engineering student who just found out GSoC exists, do give it a try the next time! Kshitij Mishra Abhimanyu Saxena Vidit Jain Manmeet Singh Akali

I spend my days with people who aren’t just “using AI.” They are reshaping our world by using these tools to solve pressing local challenges and imagining new ways of expressing dignity and agency. When I’m in classrooms, clinics, community centers, and research spaces, I see the early architecture of a future where technology strengthens the systems we rely on and opens universal opportunity.  They name a simple truth: our digital future will be shaped by people who carry responsibility for their communities, not by technologists alone. A few months ago, I opened this feed to my colleagues through the #PJMFLinkedInTakeover. That experiment reminded me that leadership grows when we make room for many voices. This month, I want to extend that spirit outward and highlight partners whose lived experience and judgment guide how AI and data show up in civic life. So this week, I’m launching #InnovationInPractice. Each day, you’ll hear directly from leaders in our community: • A youth organizer building AI literacy with women and older adults in Nigeria. • A paramedic designing digital tools that help communities respond to emergencies. • A climate advocate using data and AI to turn local policy into climate action. • An education leader in Brazil equipping teachers to reach every student. • An Indigenous researcher advancing data sovereignty as a pathway to justice. I hope you will spend time with these stories, share them, and add your own reflections in the comments. The future of AI will not be decided only in labs, boardrooms, or parliaments. It will be shaped by leaders like the ones you’ll meet here, in communities around the world, who are building a more just digital future in real time. Stay tuned for the first story in the series. #InnovationInPractice #AI #TechForGood #AIForPublicPurpose #Leadership #Philanthropy

I do dozens of interviews with top CMOs every year. I always ask what the best performing marketing channel is. And right now everyone is saying events. Post COVID events are back, but also now in an AI world, I think there's a stronger appetite to get out and connect with real people vs. just getting answers from ChatGPT. But: like anything in marketing, running events just because everyone else is doing them is a great way to set money on fire (and still not drive any incremental business). Whether it's a booth at a trade show. A VIP dinner. A 500-person conference. They can all work. They can all flop. The difference: having a real plan and strategy for that event going in. Why do it in the first place? (which continues to be the most important lesson in marketing - what's in it for me? what's the hook? why should people come to our thing?) We talked to two event experts on the Exit Five pod recently Stephanie Christensen and Kristina DeBrito — and here are 5 keys they shared for B2B event success: 1. Pick the right format. Not all events do the same job. Big splash? Go flagship. Want pipeline? Try VIP roundtables. Tiny budget? Host micro-events around existing conferences. Set real goals. 2. “Leads” are not enough anymore. Are you driving awareness? Accelerating deals? Generating pipeline? Define this upfront—or you’ll waste time measuring the wrong stuff. There are more metrics than just "did we get leads from this event" and in today's world leads are tablestalkes. 3. Align your team, bro. Sales and marketing must move in lockstep. Slack alerts for registrations. Sales meeting updates. Leaderboards. It all matters. This is a team effort. 4. Make it memorable. People forget panels. They remember custom pancakes and great venues. Was the food good? Did the WiFi work? Did Oprah show up? Just kidding. Making sure you'r reading. But think surprise and delight, not branded frisbees. 5. Put the work in on the follow up. Events don't close deals - follow-up does. Segment attendees. Create custom offers. Babysit the handoff to sales like your job depends on it. Because it does. You just went shopping and got all these fresh groceries - dont let them spoil. B2B buyers want real connection again. Events can create that. Are you feeling this desire for events? Are you doing events in your business right now? Let me know...

Getting the right feedback will transform your job as a PM. More scalability, better user engagement, and growth. But most PMs don’t know how to do it right. Here’s the Feedback Engine I’ve used to ship highly engaging products at unicorns & large organizations: — Right feedback can literally transform your product and company. At Apollo, we launched a contact enrichment feature. Feedback showed users loved its accuracy, but... They needed bulk processing. We shipped it and had a 40% increase in user engagement. Here’s how to get it right: — 𝗦𝘁𝗮𝗴𝗲 𝟭: 𝗖𝗼𝗹𝗹𝗲𝗰𝘁 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 Most PMs get this wrong. They collect feedback randomly with no system or strategy. But remember: your output is only as good as your input. And if your input is messy, it will only lead you astray. Here’s how to collect feedback strategically: → Diversify your sources: customer interviews, support tickets, sales calls, social media & community forums, etc. → Be systematic: track feedback across channels consistently. → Close the loop: confirm your understanding with users to avoid misinterpretation. — 𝗦𝘁𝗮𝗴𝗲 𝟮: 𝗔𝗻𝗮𝗹𝘆𝘇𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 Analyzing feedback is like building the foundation of a skyscraper. If it’s shaky, your decisions will crumble. So don’t rush through it. Dive deep to identify patterns that will guide your actions in the right direction. Here’s how: Aggregate feedback → pull data from all sources into one place. Spot themes → look for recurring pain points, feature requests, or frustrations. Quantify impact → how often does an issue occur? Map risks → classify issues by severity and potential business impact. — 𝗦𝘁𝗮𝗴𝗲 𝟯: 𝗔𝗰𝘁 𝗼𝗻 𝗖𝗵𝗮𝗻𝗴𝗲𝘀 Now comes the exciting part: turning insights into action. Execution here can make or break everything. Do it right, and you’ll ship features users love. Mess it up, and you’ll waste time, effort, and resources. Here’s how to execute effectively: Prioritize ruthlessly → focus on high-impact, low-effort changes first. Assign ownership → make sure every action has a responsible owner. Set validation loops → build mechanisms to test and validate changes. Stay agile → be ready to pivot if feedback reveals new priorities. — 𝗦𝘁𝗮𝗴𝗲 𝟰: 𝗠𝗲��𝘀𝘂𝗿𝗲 𝗜𝗺𝗽𝗮𝗰𝘁 What can’t be measured, can’t be improved. If your metrics don’t move, something went wrong. Either the feedback was flawed, or your solution didn’t land. Here’s how to measure: → Set KPIs for success, like user engagement, adoption rates, or risk reduction. → Track metrics post-launch to catch issues early. → Iterate quickly and keep on improving on feedback. — In a nutshell... It creates a cycle that drives growth and reduces risk: → Collect feedback strategically. → Analyze it deeply for actionable insights. → Act on it with precision. → Measure its impact and iterate. — P.S. How do you collect and implement feedback?

My nonprofit friends, if you are diving into AI for your nonprofit for the first time, start with your mission, not the tech trends. In my work, here is how I approach it – the "2-for-1" approach: ask two questions instead of one. This post is to give you some examples of this approach. When an organization starts exploring AI, the first step is often finding relevant use cases. Those use-cases often stay limited to task-first examples. But there’s a big difference (and bigger returns) between approaching AI from a task-first mindset vs. a mission-first mindset. Here are some examples of how you can reframe your questions using this "2-for-1" approach to ensure that AI advances your organization’s purpose—not just your processes. Example 1: Task-First: ● How can AI help us handle data faster? Mission-First: ● How can we better understand and serve our community’s unique needs? ● How can AI help in that understanding? Example 2: Task-First: ● What processes can we automate to save time? Mission-First: ● Focusing on which tasks can give us joy and fulfill our goals on commitments towards our communities? ● Are any of them repetitive tasks where we can use AI to save time? Example 3: Task-First: ● What can AI do to help us report our metrics? Mission-First: ● Do our current metrics enable us to track, measure, and report our progress in ways that best serve the community? ● How can AI help track and measure that progress? Example 4: Task-First: ● How do we make sure we’re using AI responsibly? Mission-First: ● How do we, collectively as an organization, act with care, transparency, and ethics on our commitment to our community? ● How do we live those values when we use AI and technology? Mission-first questions can lead to AI solutions that strengthen our promise of community trust and reinforce organizational values. AI can be a powerful ally in this work, but only if we approach it with purpose. In the coming days, I will share more examples in the next edition of Dear Human (the email edition of my newsletter). Sign up via https://lnkd.in/gUK-6M_Y Oh, and the AI Advancement Lab is now open for January enrolment! https://lnkd.in/gVRCHmsk #nonprofits #nonprofitleadership #community

#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

The One Metric I Trust Most on LinkedIn Over three years on LinkedIn, I’ve tracked every community metric I could: week to week, month to month, year over year. I’ve analyzed trends, looked for forward vs. lagging indicators, and tried to understand what truly drives growth. At first, I focused on top-line metrics - like impressions. Then engagements. But the best predictor of long-term success - the one metric I now trust most - is something few people even check: Members Reached (formerly Unique Impressions). If you go into your post analytics, LinkedIn shows you not just impressions but how many unique people saw your content. And I’ve found that growth in this number is the strongest signal that I’m on the right path. Why? Engagements fluctuate. A viral post, a trending topic, or a high-emotion moment can skew the numbers. Many people who value my content don’t engage. Senior professionals, in particular, often prefer to observe rather than publicly interact. Some folks just, increasingly, value anonymity and will discuss seeing my posts but never engage. Members Reached can’t be hidden. Unlike engagements, which depend on visible likes or comments, this metric quietly tracks how many real people are seeing what you share. Metrics should never drive your content - you should create what matters to you. But if you’re looking for a true measure of reach and impact, start paying attention to Members Reached. For me, it’s been the clearest predictor of whether the community will grow - or not - down the road.

What's the difference between mature and immature event strategy? *Note: This isn't about the number of events hosted or years of experience. 1. GOALS. Immature: Attendance numbers, registrations, ticket sales. Mature: Engagement quality, attendee satisfaction, and long-term relationship building. 2. FOCUS. Immature: One-off events with short-term hype. Mature: Integrated event series that build momentum over time. 3. EVENT PLANNING. Immature: - Trying to cram in every trendy gimmick or activity. - Switching plans reactively based on last-minute ideas. - Overloading the schedule with no clear purpose. - Scattered efforts with inconsistent execution. Mature: - Mastering a few key event formats designed for your audience. - Creating repeatable frameworks for planning and execution. - Consistent, purpose-driven events aligned with broader goals. 4. EVALUATING SUCCESS. Immature: Counting heads or social media mentions. Mature: - Measuring attendee feedback, behavior change, and downstream impact (e.g., loyalty or referrals). - Partnering with Marketing Ops to design an attribution model. 5. EVENT STRATEGY. Immature: - Focus on flashy promotion and filling seats. - Broad, undefined audience targeting. Mature: - Deep understanding of their ideal attendees. - Designing experiences that guide attendees through a meaningful journey. - Tapping partners for promotion and cost-sharing. 6. TOOLS & TECH. Immature: - Buying without trying. Failing to test the attendee experience. Most event platforms don't have a free trial motion. Accelevents does. - Duct taped together tools forcing attendees to have multiple accounts Mature: - Defining processes first, then selecting tools to enhance them. - Starting with lean, cost-effective solutions that scale with need. 7. TEAM COLLABORATION. Immature: Disconnected teams (e.g., marketing, logistics, content) working in silos with misaligned priorities. Mature: Teams aligned on shared objectives, with regular check-ins and joint planning sessions. 8. EVENT TEAM. Immature: - Hiring a large team with vague roles (e.g., “event coordinator”) and expecting instant results. - Throwing people at tasks without clear direction. Mature: - Communicating the goal of the event to every involved. - Starting with a small, focused team. - Building core event frameworks first. -------------- It comes down to this. Events are not just for "leads" Events are how you build trust. --- Mature event platforms run on Accelevents. Learn More --> https://hubs.la/Q03dHgb00 ---