Innovation

This isn’t a luxury. This is a $200 wheelchair redefining what’s possible. For millions, standing wheelchairs have always been out of reach. Until now. At R2D2, IIT Madras, a team dared to ask: What if mobility wasn’t a privilege, but a right? Their answer is a simple innovation -no Big Tech: A wheelchair that lets you stand—on your terms Ingenious gas-spring technology for seamless movement: -Supports up to 242 pounds -Priced at $200 (when others cost $2,000 or more) But the true breakthrough isn’t just in the engineering. It’s in the lives transformed. → Physical freedom is restored. Stand tall when you choose. Reach the top shelf. Cook your own meals. Keep your body strong and active. → Health is protected. Standing improves circulation. Strengthens bones. Prevents pressure sores. Aids digestion. Reduces heart risks. → Social inclusion becomes reality. Converse at eye level. Join meetings—no barriers. Participate fully in community life. Experience true belonging. Ask yourself: When was the last time you had to look up just to be heard? For millions, that’s every day. This isn’t only about standing. It’s about dignity. It’s about independence. It’s about living fully. And for the first time, it’s within reach for those who need it most. When innovation meets accessibility, lives change. This is technology for humanity. Follow me, Dr. Martha Boeckenfeld for more stories of tech that matters. ♻️ Share with your network to learn more about how simple innovation can change people's live. #TechForGood #Innovation #Healthcare

Had to share the one prompt that has transformed how I approach AI research. 📌 Save this post. Don’t just ask for point-in-time data like a junior PM. Instead, build in more temporal context through systematic data collection over time. Use this prompt to become a superforecaster with the help of AI. Great for product ideation, competitive research, finance, investing, etc. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰ TIME MACHINE PROMPT: Execute longitudinal analysis on [TOPIC]. First, establish baseline parameters: define the standard refresh interval for this domain based on market dynamics (enterprise adoption cycles, regulatory changes, technology maturity curves). For example, AI refresh cycle may be two weeks, clothing may be 3 months, construction may be 2 years. Calculate n=3 data points spanning 2 full cycles. For each time period, collect: (1) quantitative metrics (adoption rates, market share, pricing models), (2) qualitative factors (user sentiment, competitive positioning, external catalysts), (3) ecosystem dependencies (infrastructure requirements, complementary products, capital climate, regulatory environment). Structure output as: Current State Analysis → T-1 Comparative Analysis → T-2 Historical Baseline → Delta Analysis with statistical significance → Trajectory Modeling with confidence intervals across each prediction. Include data sources. ⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰⏰

Somewhere along the way, maintenance became a checkbox. A calendar event. A cost to control. But the factory floor is evolving. And so must the mindset. We don’t just repair anymore... We predict. We prescribe. We optimize. And when you optimize consistently, you stop reacting to problems…and start unlocking performance. That’s the real promise of Maintenance 4.0. Not just fewer breakdowns, but smarter resource planning, tighter production schedules, and data-driven capital decisions. It’s maintenance, yes. But not as you know it. To appreciate the significance of Maintenance 4.0, it's essential to understand its evolution of maintenance strategies: • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟏.𝟎 focused on reactive strategies, where actions were taken only after a failure occurred. This approach often led to significant downtime and high repair costs. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟐.𝟎 introduced preventative maintenance, scheduling regular check-ups based on time or usage to prevent failures. However, this method sometimes resulted in unnecessary maintenance activities, wasting resources. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟑.𝟎 saw the advent of condition-based maintenance, utilizing sensors to monitor equipment and perform maintenance based on actual conditions. This strategy marked a shift towards more data-driven decisions but still lacked predictive capabilities. • 𝐌𝐚𝐢𝐧𝐭𝐞𝐧𝐚𝐧𝐜𝐞 𝟒.𝟎 builds upon the foundations laid by its predecessors by leveraging advanced predictive and prescriptive maintenance techniques. Utilizing AI and machine learning algorithms, Maintenance 4.0 can anticipate equipment failures before they occur and prescribe optimal maintenance actions. In addition, the data-driven insights provided by Maintenance 4.0 can facilitate strategic decision-making regarding equipment investments, production planning, and innovation initiatives through better integration with other programs and systems, such as Enterprise Asset Management (EAM) and Asset Performance Management (APM). 𝐅𝐨𝐫 𝐚 𝐝𝐞𝐞𝐩𝐞𝐫 𝐝𝐢𝐯𝐞: https://lnkd.in/djjfivw8 ******************************************* • 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!

AbbVie and Johnson & Johnson contribute data to fuel new molecular prediction drug discovery model, OpenFold3 💊AlphaFold 3 is a leading AI tool for predicting protein structures, but it lacks enough data on how proteins interact with drug molecules, limiting its utility in drug discovery 💊In response, AbbVie and Johnson & Johnson have recently teamed up to use their own data enhance a new AI model, OpenFold3, within the AI Structural Biology Consortium (AISB) 💊The consortium also includes Sanofi, Boehringer Ingelheim, and Takeda pooling collective pharma expertise and proprietary data to push AI-driven drug discovery forward 💊AbbVie alone is contributing over 9,000 proprietary protein-ligand structures, with other companies likely contributing similar volumes locked in private repositories 💊The data will remain confidential, protected by Apheris’ federated learning platform, which enables joint AI training without exposing or transferring sensitive data 💊The model will be developed collaboratively in a way that safeguards intellectual property and will remain accessible only to consortium members, not the public or external researchers 👇Link to articles in comments #pharma #digitalhealth #ai

In our new paper we ran an experiment at Procter and Gamble with 776 experienced professionals solving real business problems. We found that individuals randomly assiged to use AI did as well as a team of two without AI. And AI-augmented teams produced more exceptional solutions. The teams using AI were happier as well. Even more interesting: AI broke down professional silos. R&D people with AI produced more commercial work and commercial people with AI had more technical solutions. The standard model of "AI as productivity tool" may be too limiting. Today’s AI can function as a kind of teammate, offering better performance, expertise sharing, and even positive emotional experiences. This was a massive team effort with work led by Fabrizio Dell'Acqua, Charles Ayoubi, and Karim Lakhani along with Hila Lifshitz, Raffaella Sadun, Lilach M., me and our partners at P&G: Yi Han, Jeff Goldman, Hari Nair and Stewart Taub Subatack about the work here: https://lnkd.in/ehJr8CxM Paper: https://lnkd.in/e-ZGZmW9

Scaling from 50 to 100 employees almost killed our company. Until we discovered a simple org structure that unlocked $100M+ in annual revenue. In my 10+ years of experience as a founder, one of the biggest challenges I faced in scaling was bridging the organizational gap between startup and enterprise. We hit that wall at around 100~ employees. What worked beautifully with a small team suddenly became our biggest obstacle to growth. The problem was our functional org structure: Engineers reporting to engineering, product to product, business to business. This created a complex dependency web: • Planning took weeks • No clear ownership  • Business threw Jira tickets over the fence and prayed for them to get completed • Engineers didn’t understand priorities and worked on problems that didn’t align with customer needs That was when I studied Amazon's Single-Threaded Owner (STO) model, in which dedicated GMs run independent business units with their own cross-functional teams and manage P&L It looked great for Amazon's scale but felt impossible for growing companies like ours. These 2 critical barriers made it impractical for our scale: 1. Engineering Squad Requirements: True STO demands complete engineering teams (including managers) reporting to a single owner. At our size, we couldn't justify full engineering squads for each business unit. To make it work, we would have to quadruple our engineering headcount. 2. P&L Owner Complexity: STO leaders need unicorn-level skills: deep business acumen and P&L management experience. Not only are these leaders rare and expensive, but requiring all these skills in one person would have limited our talent pool and slowed our ability to launch new initiatives. What we needed was a model that captured STO's focus and accountability but worked for our size and growth needs. That's when we created Mission-Aligned Teams (MATs), a hybrid model that changed our execution (for good) Key principles: • Each team owns a specific mission (e.g., improving customer service, optimizing payment flow) • Teams are cross-functional and self-sufficient,  • Leaders can be anyone (engineer, PM, marketer) who's good at execution • People still report functionally for career development • Leaders focus on execution, not people management The results exceeded our highest expectations: New MAT leads launched new products, each generating $5-10M in revenue within a year with under 10 person teams. Planning became streamlined. Ownership became clear. But it's NOT for everyone (like STO wasn’t for us) If you're under 50 people, the overhead probably isn't worth it. If you're Amazon-scale, pure STO might be better. MAT works best in the messy middle: when you're too big for everyone to be in one room but too small for a full enterprise structure. image courtesy of Manu Cornet ------ If you liked this, follow me Henry Shi as I share insights from my journey of building and scaling a  $1B/year business.

🌟 𝐒𝐭𝐨𝐩 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐁𝐢𝐠 - 𝐒𝐭𝐚𝐫𝐭 𝐓𝐡𝐢𝐧𝐤𝐢𝐧𝐠 𝐖𝐢𝐝𝐞! The biggest breakthroughs don’t happen by digging deeper into one area - they happen when ideas, industries, and technologies collide. Think about it: AI combined with IoT has transformed healthcare. Sustainability powered by cloud solutions is opening new markets. The magic lies at the 𝐢𝐧𝐭𝐞𝐫𝐬𝐞𝐜𝐭𝐢𝐨𝐧𝐬 - where fresh opportunities emerge. 🚀 𝐖𝐡𝐲 𝐓𝐡𝐢𝐬 𝐌𝐚𝐭𝐭𝐞𝐫𝐬 1️⃣ 𝐅𝐚𝐬𝐭𝐞𝐫 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧: Combining technologies like AI and cloud accelerates growth. 2️⃣ 𝐍𝐞𝐰 𝐌𝐚𝐫𝐤𝐞𝐭 𝐑𝐞𝐚𝐜𝐡: Partnerships across industries unlock untapped customers. 3️⃣ 𝐒𝐡𝐚𝐫𝐞𝐝 𝐕𝐚𝐥𝐮𝐞: Cross-industry collaboration lowers costs and drives new value. At Deloitte, I’ve seen the power of collaboration. By partnering with organizations like #Celonis, #Schaeffler, #HumboldtInnovation, and #GermanEntrepreneurship, we’ve established the European non-profit AI ecosystem, #KIPark. This initiative brings together players from different industries to unlock innovation. For example, we’ve developed an ESG platform, marking a significant step toward sustainable solutions that are robust and business-relevant. 🛠️ 𝐓𝐡𝐫𝐞𝐞 𝐖𝐚𝐲𝐬 𝐭𝐨 𝐒𝐭𝐚𝐲 𝐀𝐡𝐞𝐚𝐝 1️⃣ 𝐋𝐨𝐨𝐤 𝐎𝐮𝐭𝐬𝐢𝐝𝐞 𝐘𝐨𝐮𝐫 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲: Who could you partner with to create something new? 2️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐌𝐢𝐱𝐞𝐝 𝐓𝐞𝐚𝐦𝐬: Pair data scientists with operations or customer-facing teams. 3️⃣ 𝐄𝐱𝐩𝐞𝐫𝐢𝐦𝐞𝐧𝐭 𝐁𝐨𝐥𝐝𝐥𝐲: Start small pilots that combine tech and business ideas. 🌍 𝐓𝐡𝐞 𝐁𝐨𝐭𝐭𝐨𝐦 𝐋𝐢𝐧𝐞 The future belongs to businesses that connect the dots others don’t see. Breadth - not just depth - is the key to growth and resilience. 💬 𝐘𝐨𝐮𝐫 𝐓𝐮𝐫𝐧 What’s one unexpected partnership or idea you’ve seen recently that sparked innovation? Let’s exchange ideas. Who knows what new intersections we might uncover together? #Deloitte #AI #Innovation #Leadership #BusinessStrategy #Partnerships 𝐴𝑟𝑡𝐵𝑎𝑠𝑒𝑙. 𝐶ℎ𝑎𝑛𝑔𝑒𝑂𝑓𝑃𝑒𝑟𝑠𝑝𝑒𝑐𝑡𝑖𝑣𝑒. 𝐹𝑜𝑢𝑛𝑑 𝑎𝑡 @𝑔𝑎𝑏𝑟𝑖𝑒𝑙𝑙𝑒𝑒𝑒𝑟𝑢𝑡ℎ

Keeping track of AI governance, policy and regulations is a never-ending task Here are the key tracker resources you need to follow to stay ahead 𝐀𝐈 𝐈𝐍𝐂𝐈𝐃𝐄𝐍𝐓𝐒 & 𝐑𝐈𝐒𝐊𝐒 ➡️ AI Risk Repository [MIT FutureTech] A comprehensive database of 700 risks from AI systems 🔗 https://airisk.mit.edu/ ➡️ AI Incident Database [Partnership on AI] Dedicated to indexing the collective history real-world of harms caused by the deployment of AI 🔗 https://lnkd.in/ewBaYitm ➡️ AI Incidents Monitor [OECD - OCDE] AI incidents and hazards reported in international media globally are identified and classified using machine learning models 🔗 https://lnkd.in/e4pJ7jcA 𝐀𝐈 𝐑𝐄𝐆𝐔𝐋𝐀𝐓𝐈𝐎𝐍𝐒 & 𝐏𝐎𝐋𝐈𝐂𝐈𝐄𝐒 ➡️ Global AI Law and Policy Tracker [IAPP - International Association of Privacy Professionals] Resource providing information about AI law and policy developments in key jurisdictions worldwide 🔗 https://lnkd.in/eiGMk9Rm ➡️ National AI Policies and Strategies [OECD.AI] Live repository of 1000+ AI policy initiatives from 69 countries, territories and the EU 🔗 https://lnkd.in/ebVTQzdb ➡️ Global AI Regulation Tracker [Raymond Sun] An interactive world map that tracks AI law, regulatory and policy developments around the world 🔗 https://lnkd.in/ekaKzmzD ➡️ U.S. State AI Governance Legislation Tracker [IAPP - International Association of Privacy Professionals] Tracker which focuses on cross-sectoral AI governance bills that apply to the private sector 🔗 https://lnkd.in/ee4N-ckB. 𝐀𝐈 𝐆𝐎𝐕𝐄𝐑𝐍𝐀𝐍𝐂𝐄 𝐓𝐎𝐎𝐋𝐊𝐈𝐓𝐒 & 𝐑𝐄𝐒𝐎𝐔𝐑𝐂𝐄𝐒 ➡️ AI Standards Hub [The Alan Turing Institute] Online repository of 300+ AI standards 🔗 https://lnkd.in/erVdP4g7 ➡️ AI Risk Management Framework Playbook [National Institute of Standards and Technology (NIST)] Playbook of recommended actions, resources and materials to support implementation of the NIST AI RMF. 🔗 https://lnkd.in/eTzpfbCi ➡️ Catalogue of Tools & Metrics for Trustworthy AI [OECD.AI] Tools and metrics which help AI actors to build and deploy trustworthy AI systems 🔗 https://lnkd.in/e_mnAbpZ ➡️ Portfolio of AI Assurance Techniques [Department for Science, Innovation and Technology] The Portfolio showcases examples of AI assurance techniques being used in the real-world to support the development of trustworthy AI 🔗 https://lnkd.in/eJ5V3uzb Happy tracking!

🧠 “How We Brainstorm And Choose UX Ideas” (+ Miro template) (https://lnkd.in/eN32hH2x), a practical guide by Booking.com on how to run a rapid UX ideation session with silent brainstorming and “How Might We” (HMW) statements — by clustering data points into themes, reframing each theme and then prioritizing impactful ideas. Shared by Evan Karageorgos, Tori Holmes, Alexandre Benitah. 👏🏼👏🏽👏🏾 Booking.com UX Ideation Template (Miro) https://lnkd.in/eipdgPuC (password: bookingcom) 🚫 Ideas shouldn’t come from assumptions but UX research. ✅ Study past research and conduct a new study if needed. ✅ Cluster data in user needs, business goals, competitive insights. ✅ Best ideas emerge at the intersections of these 3 pillars. ✅ Cluster all data points into themes, prioritize with colors. ✅ Reframe each theme as a “How Might We” (HMW) statement. ✅ Start with the problems (or insights) you’ve uncovered. ✅ Focus on the desired outcomes, rather than symptoms. ✅ Collect and group ideas by relevance for every theme. ✅ Prioritize and visualize ideas with visuals and storytelling. Many brainstorming sessions are an avalanche of unstructured ideas, based on hunches and assumptions. Just like in design work we need constraints to be intentional in our decisions, we need at least some structure to mold realistic and viable ideas. I absolutely love the idea of frame the perspective through the lens of ideation clusters: user needs, business problems and insights. Reframing emerging themes as “How-Might-We”-statements is a neat way to help teams focus on a specific problem at hand and a desired outcome. A simple but very helpful approach — without too much rigidity but just enough structure to generate, prioritize and eventually visualize effective ideas with the entire team. Invite non-designers in the sessions as well, and I wouldn’t be surprised how much value a 2h session might deliver. Useful resources: The Rules of Productive Brainstorming, by Slava Shestopalov https://lnkd.in/eyYZjAz3 On “How Might We” Questions, by Maria Rosala, NN/g https://lnkd.in/ejDnmsRr Ideation for Everyday Design Challenges, by Aurora Harley, NN/g https://lnkd.in/emGtnMyy Brainstorming Exercises for Introverts, by Allison Press https://lnkd.in/eta6YsFJ How To Run Successful Product Design Workshops, by Gustavs Cirulis, Cindy Chang https://lnkd.in/eMtX-xwD Useful Miro Templates For UX Designers, by yours truly https://lnkd.in/eQVxM_Nq #ux #design

Should you try Google’s famous “20% time” experiment to encourage innovation? We tried this at Duolingo years ago. It didn’t work. It wasn’t enough time for people to start meaningful projects, and very few people took advantage of it because the framework was pretty vague. I knew there had to be other ways to drive innovation at the company. So, here are 3 other initiatives we’ve tried, what we’ve learned from each, and what we're going to try next. 💡 Innovation Awards: Annual recognition for those who move the needle with boundary-pushing projects. The upside: These awards make our commitment to innovation clear, and offer a well-deserved incentive to those who have done remarkable work. The downside: It’s given to individuals, but we want to incentivize team work. What’s more, it’s not necessarily a framework for coming up with the next big thing. 💻 Hackathon: This is a good framework, and lots of companies do it. Everyone (not just engineers) can take two days to collaborate on and present anything that excites them, as long as it advances our mission or addresses a key business need. The upside: Some of our biggest features grew out of hackathon projects, from the Duolingo English Test (born at our first hackathon in 2013) to our avatar builder. The downside: Other than the time/resource constraint, projects rarely align with our current priorities. The ones that take off hit the elusive combo of right time + a problem that no other team could tackle. 💥 Special Projects: Knowing that ideal equation, we started a new program for fostering innovation, playfully dubbed DARPA (Duolingo Advanced Research Project Agency). The idea: anyone can pitch an idea at any time. If they get consensus on it and if it’s not in the purview of another team, a cross-functional group is formed to bring the project to fruition. The most creative work tends to happen when a problem is not in the clear purview of a particular team; this program creates a path for bringing these kinds of interdisciplinary ideas to life. Our Duo and Lily mascot suits (featured often on our social accounts) came from this, as did our Duo plushie and the merch store. (And if this photo doesn't show why we needed to innovate for new suits, I don't know what will!) The biggest challenge: figuring out how to transition ownership of a successful project after the strike team’s work is done. 👀 What’s next? We’re working on a program that proactively identifies big picture, unassigned problems that we haven’t figured out yet and then incentivizes people to create proposals for solving them. How that will work is still to be determined, but we know there is a lot of fertile ground for it to take root. How does your company create an environment of creativity that encourages true innovation? I'm interested to hear what's worked for you, so please feel free to share in the comments! #duolingo #innovation #hackathon #creativity #bigideas