🏗️Wicked Problems in Construction—How AI Can Help Solve Them (Part 2) The construction industry faces complex challenges—from stagnant productivity to safety risks and environmental impact. These aren’t just everyday problems; they’re wicked problems—interconnected, evolving, and challenging to solve. 💡 Can AI be the game-changer? In the second article of our series (co-authored with Mike Hill FBCS (RICS), Jugal Makwana (Autodesk), and James Garner (Gleeds)), we explore how AI can tackle three critical areas and how some products are solving these challenges [The products mentioned in this post/article are examples, including some from the RICS Tech Partner program. In most cases, competing products exist that are not listed here. All information is sourced from publicly available materials, and this is not product promotion. Readers should conduct further research as needed.]: ✅ Reducing environmental impact Construction is responsible for nearly ~40% of global emissions. AI is revolutionizing how we track, measure, and reduce carbon #emissions while improving material efficiency and #circularity. +Early cost & carbon insights: Preoptima, V-Quest, Autodesk Forma, BCIS Life Cycle Evaluator, Nomitech CostOS +Material selection: Emidat, Pathways, Firstplanit +Whole life carbon assessment: Morgan Sindall Construction CarboniCa, One Click LCA, Xylo Systems +Circularity & waste reduction: Urban Machine, Qualis Flow (Qflow) ✅ Enhancing safety & addressing skills shortages The industry faces persistent workforce shortages and safety incidents. AI can predict risks before they happen, automate compliance, and even augment the workforce with robotics. +Real-time hazard detection: OpenSpace, REscan +Predictive safety analytics: HammerTech , Kwant +Automating safety compliance: Saifety.ai, Onwave, SALUS +AI-powered workforce augmentation: Rugged Robotics, Canvas ✅ Boosting construction productivity Construction productivity has improved just 10% in over two decades—far behind other industries. AI is driving design, scheduling, risk management, contract management and supply chain efficiency. +AI-driven design automation: Viability, Augmenta, qbiq +AI for scheduling & contract management: ALICE Technologies, nPlan, Document Crunch +Decision support & process automation: Trunk Tools, Gryps +Supply chain management: Kaya AI, Autodesk Construction Cloud, BuildHub, BuiltSpace Technologies Corp. 🚀 AI isn’t just about automation—it’s about augmenting our decision-making and making construction smarter, safer, and more sustainable. Read the full article here 👉 https://lnkd.in/ezBAeXx2. 💬 How do you see AI reshaping construction? What barriers still stand in the way? It seems the future is about three things—#agents, #agents, and #agents. 🤖 #AIinConstruction #DigitalTransformation #ConstructionTech #Sustainability #Innovation
How Industrial AI Reduces Environmental Impact
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Summary
Industrial AI is transforming how industries reduce their environmental impact by using advanced technologies to lower emissions, cut waste, and conserve resources. By analyzing massive amounts of data, AI helps industries adopt sustainable practices and improve efficiency.
- Monitor and measure: Use AI to track carbon emissions and resource usage in real-time, allowing for quicker identification and correction of inefficiencies.
- Streamline operations: Automate repetitive tasks and optimize processes with AI to save energy and reduce waste across manufacturing and construction industries.
- Improve material choices: Leverage AI insights to select low-carbon or recycled materials, contributing to a circular economy and cutting down on environmental harm.
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🌍 Excited to share our new research published in Environmental Science & Technology (ES&T), a journal with an impact factor of 10.9. Our paper introduces Parakeet, an AI solution that combines large language models with semantic matching to automatically recommend emission factors for life cycle assessments - a critical but time-consuming step in carbon footprint calculations. 📊 Organizations often struggle with inconsistent manual mapping processes that can take weeks of expert time and lack clear documentation for audits. Our algorithm achieves 87% accuracy in fully automated matching and 93% accuracy with human review, while providing transparent, verifiable justifications for its recommendations. 🤖 This development significantly accelerates carbon accounting, especially for complex Scope 3 emissions calculations across supply chains. By streamlining this process, we're enabling organizations of all sizes to more efficiently measure and manage their environmental impact as they work toward net-zero emissions targets. This research represents a major step forward in scaling up carbon footprint assessments across industries. 👥 Work with my wonderful colleagues at Amazon: Fahimeh Ebrahimi, Ph.D. Nina Domingo Gargeya Vunnava Abu-Zaher F. Somasundari Ramalingam Shikha Gupta Anran Wang Harsh Gupta Domenic Belcastro Kellen Axten Jeremie Hakian Jared Kramer Aravind Srinivasan and Qingshi Tu, PhD 📄 Link to paper (open access for a limited time, requires sign in): https://lnkd.in/g4fYzdFb 📄 Open link to previous version of paper: https://lnkd.in/dUtva7Nh #amazonscience #sustainability #carbonfootprint #ai
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🔍 How can AI and ML help us tackle climate change? Imagine this: You're managing a factory, doing everything you can to run things efficiently. But energy costs keep climbing, and your team spends hours redoing tasks because of small errors. Now multiply that across countless factories worldwide, and it's no surprise the impact on emissions and energy use is huge. Enter AI and Machine Learning. These tools are more than just tech buzzwords—they’re reshaping the way we approach climate solutions. In factories, for example, AI can spot where errors usually happen, help your team work smarter (not harder), and cut down on wasted resources. That means fewer emissions and less energy wasted on repeat tasks. A few stats show the difference AI can make: -Error reduction: Automated quality checks can reduce errors by up to 40%, helping teams work more effectively and sustainably. -Efficiency gains: Combining AI with tools like natural language processing can reduce task time by 30-40%, saving both energy and costs. -Material usage: AI helps identify ways to use low-carbon materials, cutting emissions by up to 20% in some cases. Thinking about how AI and ML could change your field? Whether it’s in manufacturing, energy, or logistics, data-driven insights are transforming climate action. Imagine the impact if we each found one way AI could help reduce waste in our work—it could be a small step that adds up to a big change for our planet. Let’s share ideas! How is AI making a difference in your industry? #AIML #DataDriven #ClimateChange #SustainableSolutions
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