Integrating AI and Sustainability in Irish Business

AI has no place in sustainability. There’s a familiar stance I hear a lot in sustainability circles. AI uses a lot of energy. So using it for sustainability sounds… contradictory. But that argument misses the bigger picture. AI isn’t just consuming energy. It’s helping us use less of it too. Used well, AI is already solving real sustainability problems. Not hypotheticals. Not R&D lab demos. Live, operational tools that help businesses reduce emissions, speed up reporting, and make better decisions. Here’s what that looks like in practice: 1. Energy grid optimisation In the UK, the National Grid is using AI to forecast solar energy production by analysing satellite images and weather data. If clouds are expected to lower solar output in, say, Cornwall 30 minutes from now, the grid can prep alternative sources in advance. That means fewer blackouts and lower emissions from fossil backup plants. DeepMind did something similar for wind power. Their AI predicted wind farm output 36 hours in advance, which increased the commercial value of wind energy by around 20 percent. Why? Because energy providers could schedule when to send power to the grid with more certainty. 2. Streamlined carbon accounting AI tools now scan invoices, utility bills and PDF reports to pull out emissions data automatically. They match spend categories to emissions factors and calculate Scope 1, 2 and 3 outputs in seconds. That turns carbon accounting from a once-a-year headache into a real-time management tool. 3. Transparent supply chains Unilever has tested AI platforms that combine satellite imagery with supply data to flag illegal deforestation in palm oil regions. If a patch of rainforest is cleared where it shouldn’t be, AI catches it fast and alerts their team. No need to wait for an audit or third-party tipoff. 4. Faster climate simulations Traditional climate models take weeks or months to run. New AI-driven models can simulate complex climate scenarios up to 25 times faster. That unlocks planning tools for city councils, small businesses and insurers who can’t wait months to model flood risks or heat exposure. Yes, AI needs energy to run. But if it helps avoid 10 times more emissions than it creates, the trade-off makes sense. So the question isn’t whether AI belongs in sustainability. It’s whether we’re serious about using every tool we have to solve the problems in front of us. At Leafr, we’ve seen consultants use AI to cut time and cost on energy audits, validate supplier claims, and surface risks early. When paired with the right human expertise, AI becomes a multiplier. Because the planet doesn’t care if a human or a machine found the emissions. It just cares that they’re found and cut. Follow Gus Bartholomew (Leafr 🌿)for more and repost if you found useful. Use Leafr to find the sustainability specialists you need to support your AI efforts

Digital and Sustainability Integration 🌍 This framework developed by Bain & Company shows how digital technologies can strengthen sustainability strategies when they are applied in a coordinated way across the business. The approach is presented as a game plan, highlighting different actions to drive transformation, build resilience, and capture opportunities. It organizes the strategy into three key zones: offense, midfield, and defense. Each zone addresses different levers companies can use to combine digital tools with sustainability goals, creating a balanced and forward-looking approach. The offense zone focuses on renewing strategies, innovating with agility, and building collaborations across value chains. These actions help accelerate the benefits of integration while anticipating risks, testing new solutions, and forming partnerships that expand reach and impact. In the midfield, the emphasis is on measuring performance, optimizing processes, reducing waste, and augmenting work models. Accurate measurement and transparent tracking enable better decision-making, while digital optimization unlocks efficiencies in resources, materials, and operations. Reducing waste and improving circularity remain central, supported by digital technologies that make recycling, reuse, and redesign easier to scale. Hybrid work models are enhanced through digital platforms that expand access to talent and strengthen inclusion. The defense zone covers reskilling employees, protecting stakeholders, and securing systems. Preparing the workforce for the era of automation ensures adaptability, while strong digital safeguards protect people, data, and operational integrity. Digital and sustainability agendas are becoming increasingly interconnected as businesses recognize that one enables the other. Digital tools provide the data, automation, and analytical capabilities needed to measure impact, optimize resource use, and scale sustainable practices across entire value chains. At the same time, sustainability priorities guide the focus of digital investments, ensuring technology is applied where it delivers environmental and social value alongside financial returns. This convergence is accelerating because market expectations, regulations, and competitive pressures demand solutions that are both efficient and responsible. From AI-powered energy management to blockchain-based supply chain traceability, the technologies driving digital transformation are also unlocking new pathways for sustainable growth. #sustainability #business #sustainable #esg

ExpectAI - Profitable Climate Action - Net Positive AI If the energy transition is to succeed, it must make economic sense. This applies to businesses making investment decisions, shareholders directing capital, and customers choosing where to spend their money – for example whether or not to buy an electric vehicle – or how to heat or cool their premises. Words are nice but economics drive choices. This is one of the reasons I said yes when Anand Verma (Dr.) – the Founder and CEO of ExpectAI - asked me to become Chairman. ExpectAI’s mission is clear “Higher Profits, Lower Emissions”. Let me explain a little bit about what we do. Believe it or not - there are over 330 million small to medium sized businesses (SMBs) globally. These SMBs represent over 40% of corporate emissions – and yet attract little attention or support. SMBs are unlikely to have a Chief Sustainability Officer. They certainly will not be hiring an expensive management consultant to help them figure out how to reduce their energy costs and their emissions. And let’s face it – it’s a really complex space with different regulatory and incentive schemes depending on where you are in the world. And here is where ExpectAI comes in. We use artificial intelligence and public data to help these companies grow their profits and reduce their emissions – profitable climate action! Our AI super agent, Una, is a brilliant advisor. Una makes tailored recommendations on how the SMB can lower its energy costs and take advantage of subsidies and incentives. It then matches users with local suppliers (think roof solar panels) and simplifies filling out all the forms. Over time we can imagine ExpectAI to provide the SMB with financial support – either through a relationship with a lender or a dedicated SMB fund. Una also helps with compliance and reporting (of which there is a lot!) and effectively acts as a business’s Chief Sustainability Officer. This is what net positive AI is all about. How to make sure the benefits of AI vastly outweigh the downsides. At Prometheus Hyperscale our goal is to build sustainable date centres and minimise energy (and water) use. Minimising the negative. At ExpectAI, we’re on the opposite side of the equation, using AI to help SMBs grow profits while lowering emissions. Maximising the positive. Now we need to go and make it happen. Watch this space! https://lnkd.in/eipSenZE

𝗕𝗮𝗹𝗮𝗻𝗰𝗶𝗻𝗴 𝗔𝗜 𝗨𝘀𝗲 𝘄𝗶𝘁𝗵 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗚𝗼𝗮𝗹𝘀: 𝗔 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝗲𝗿'𝘀 𝗥𝗼𝗮𝗱𝗺𝗮𝗽 🌍 In my latest blog post, we explore critical strategies for tech leaders to effectively align AI use with sustainability commitments. This topic is crucial as we navigate the complexities of modern technological advancements and environmental responsibility. 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗔𝗿𝘁𝗶𝗰𝗹𝗲: * Assessing AI Necessity: Leaders must critically evaluate the need for AI, ensuring that its deployment adds significant value and does not unnecessarily increase the environmental burden. * Optimising AI Deployment: Choosing suppliers committed to sustainability and enhancing AI systems for energy efficiency are pivotal steps towards minimising the ecological impact. * Enhancing Transparency: Demanding clear data on AI systems' carbon emissions from suppliers ensures informed decisions and encourages industry-wide accountability. 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗦𝘁𝗲𝗽𝘀 𝗳𝗼𝗿 𝗧𝗲𝗰𝗵 𝗟𝗲𝗮𝗱𝗲𝗿𝘀: * Vendor Selection: Opt for AI technology providers demonstrating strong environmental sustainability practices. * Energy Efficiency: Implement AI solutions that require minimal energy consumption while maintaining optimal performance. * Regulatory Compliance: Ensure compliance with current and upcoming environmental regulations affecting AI applications. 𝗦𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗔𝗜: * Embrace AI tools that bolster sustainable business practices, contributing positively to achieving broader corporate social responsibility goals. 𝗖𝗮𝗹𝗹 𝘁𝗼 𝗔𝗰𝘁𝗶𝗼𝗻: I invite you to read the article to better understand how to integrate AI with sustainability. Join the conversation and share your insights on leveraging AI for positive environmental impact. 👉 Read the full article for comprehensive strategies on how tech leaders can effectively balance AI advancements with sustainability goals. Let's pioneer the responsible use of AI in technology! #Sustainability #AI #TechLeadership #BusinessStrategy #EnvironmentalImpact #Innovation #CorporateResponsibility

Sustainability has never been for the faint of heart. It will only get harder if we don’t apply the same data strategies and AI investments that other CxOs are adopting.   AI has jumped from talking point to your colleagues' budget: 72% of organizations have implemented AI in at least one business function (McKinsey 2024).    When done right, CSOs’ use of AI can create a flywheel effect that integrates sustainability data into CxOs’ tools, what-if scenarios, and business cases —driving sustainable decision making.   To get there, they need to deploy AI solutions that automate resource-intensive tasks, like: 🔷 Sourcing: Screening suppliers for attributes that contribute to your KPIs and capture those to build towards annual goals. 🔷 Product claims: Transferring those attributes from suppliers to product claims, extracting data from sustainability declarations and analyzing images— reducing errors and ensuring supply chain compliance. 🔷 Carbon footprints: Expediting data collection by mapping emission factors— SAP's Sustainability Footprint Management customers report up to 80% reduction in manual effort and time. 🔷 Reporting: Aggregating those KPIs into auditable, public reports in minutes— freeing their team to focus on strategy and execution.   The tools exist and the data is there. Sustainability leaders need the same level of access to AI that their colleagues have to meet their mandate. #SAPSustainability #AI #Sustainability #BusinessAI

Reducing carbon footprints is critical in today's business world. Here are 10 ways AI can optimize supply chains to reduce carbon emissions: Supply Chain Optimization: AI analyzes data on transportation, logistics, & inventory to minimize energy use & emissions in supply chain operations. This optimization includes better planning of routes & schedules, leading to significant reductions in fuel consumption & greenhouse gas emissions. Demand Forecasting & Inventory Management: AI predicts consumer demand accurately, optimizing inventory levels to prevent overproduction & reduce waste. By reducing excess inventory, companies can cut down on the resources & energy used in production, storage, & disposal of unsold goods. Efficient Logistics: AI algorithms analyze real-time data on traffic, weather, & fuel prices to optimize delivery routes, reducing fuel consumption & emissions. This results in fewer miles travelled & less idling time, contributing to lower carbon footprints. Energy Management: AI-powered systems monitor & optimize energy use in supply chain facilities like warehouses & factories, lowering energy consumption. AI can adjust lighting, heating, cooling, & machinery operation times to align with actual needs, reducing unnecessary energy usage. Renewable Energy Integration: AI improves forecasting of renewable energy generation, better integrating these sources into supply chain operations. By predicting availability & optimizing the use of solar, wind, & other renewable energies, AI helps reduce reliance on fossil fuels. Quality Control: AI-powered quality control systems identify defects in real-time, reducing waste from faulty products. Early detection of issues means less material waste & lower energy consumption in producing replacements. Water Management: AI monitors & optimizes water usage in supply chain processes, contributing to water conservation efforts. Efficient water use not only saves this vital resource but also reduces the energy required for water treatment & distribution. Supplier Evaluation: AI analyzes supplier data to assess environmental, social, & governance (ESG) performance, enabling more sustainable sourcing. Companies can choose suppliers who align with their sustainability goals, reducing overall supply chain emissions. Lifecycle Analysis: AI models the environmental impact of materials across their entire lifecycle, informing decisions to reduce emissions. This analysis helps identify stages in the material lifecycle where improvements can be made to lower environmental impact. Real-Time Monitoring: AI systems continuously monitor supply chain operations, quickly identifying & addressing inefficiencies. By maintaining optimal performance & swiftly correcting issues, AI ensures that energy & resource use is kept to a minimum. By leveraging these AI capabilities, businesses can significantly reduce their supply chain carbon footprint while also improving operational efficiency & cost savings. 🌍🌿

𝕋𝕙𝕖 𝕌𝕟𝕔𝕠𝕞𝕗𝕠𝕣𝕥𝕒𝕓𝕝𝕖 𝕋𝕣𝕦𝕥𝕙 𝕒𝕓𝕠𝕦𝕥 𝔸𝕀, 𝕀𝕟𝕟𝕠𝕧𝕒𝕥𝕚𝕠𝕟 𝕒𝕟𝕕 𝕊𝕦𝕤𝕥𝕒𝕚𝕟𝕒𝕓𝕚𝕝𝕚𝕥𝕪?   Without AI, our sustainability goals are just corporate fantasy.   Here's why:   𝗧𝗵𝗲 𝗰𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 𝗼𝗳 𝘁𝗿𝘂𝗲 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗶𝗿𝗰𝘂𝗹𝗮𝗿𝗶𝘁𝘆 ... exceeds human cognitive capacity. We've spent years making incremental improvements while the fundamental challenge remains: our industrial systems weren't designed for sustainability and circularity, and redesigning them requires processing connections and possibilities far beyond what traditional approaches can handle.   𝗗𝗶𝗴𝗶𝘁𝗮𝗹 𝘁𝘄𝗶𝗻𝘀 𝗽𝗮𝗶𝗿𝗲𝗱 𝘄𝗶𝘁𝗵 𝗮𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗔𝗜 ... aren't just helpful technologies – they're the only realistic path to transformation. When AI analyzes thousands of material streams across interconnected supply networks, it uncovers circular opportunities invisible to even our best sustainability teams. What we call "waste" is simply a failure of our limited human imagination.   𝗠𝗼𝘀𝘁 𝗽𝗿𝗼𝘃𝗼𝗰𝗮𝘁𝗶𝘃𝗲𝗹𝘆: the truly circular products of tomorrow won't be designed by humans at all. AI systems unconstrained by conventional thinking will create entirely new approaches to material use, product design, and business models that our human minds – trained on linear economy principles – simply cannot conceive.   The companies waiting for perfect sustainability roadmaps before embracing these technologies will be left behind.   𝗜 𝘁𝗵𝗶𝗻𝗸 𝗚𝗲𝗻𝗔𝗜 𝗮𝗻𝗱 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝘄𝗶𝗹𝗹 𝗱𝗿𝗶𝘃𝗲 𝗯𝗿𝗲𝗮𝗸𝘁𝗵𝗿𝗼𝘂𝗴𝗵 𝗶𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻.   𝗔𝗜-𝗮𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗥&𝗗 ... will transform how industrial companies approach innovation. By analyzing vast datasets from experiments, simulations, and historical projects, AI can identify non-obvious patterns and suggest novel material combinations, designs, or manufacturing processes that humans might overlook. This can dramatically accelerate the discovery-to-implementation timeline.   𝗧𝗵𝗲 𝗾𝘂𝗲𝘀𝘁𝗶𝗼𝗻 𝗶𝘀𝗻'𝘁 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝗔𝗜 𝘄𝗶𝗹𝗹 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 𝘀𝘂𝘀𝘁𝗮𝗶𝗻𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝗶𝗮𝗹 𝘀𝗲𝘁𝘁𝗶𝗻𝗴𝘀 – 𝗶𝘁'𝘀 𝘄𝗵𝗲𝘁𝗵𝗲𝗿 𝘆𝗼𝘂𝗿 𝗰𝗼𝗺𝗽𝗮𝗻𝘆 𝘄𝗶𝗹𝗹 𝗯𝗲 𝗮𝗺𝗼𝗻𝗴 𝘁𝗵𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗿𝘀 𝗼𝗿 𝘁𝗵𝗲 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗲𝗱.   What's your experience? Is your organization leveraging AI to break through sustainability barriers? Or are you still trying to solve tomorrow's circular economy challenges with yesterday's tools? #TechnologyForSustainability #AI #Sustainability #CircularEconomy Siemens Digital Industries Software Siemens Industry

🔍 New Research: How Integrating AI with Sustainability Triples Impact At Google, we see how technology can be a catalyst for solving complex challenges. A new report from the Project Management Institute shows that organisations integrating AI into their sustainability strategies are achieving outcomes that are three times stronger than those treating the two separately. Key findings: 📉 Emissions reduction: Firms combining AI and sustainability strategies achieve a 26% cut in carbon emissions, compared to 3% for others ⚙️ Operational improvements: AI is being used to optimise energy efficiency, predict maintenance needs, and streamline supply chains 🔄 Compounding progress: Early sustainability gains from AI adoption are creating a positive cycle of reinvestment and greater impact 📊 Success factors: The biggest differentiators are not just technology — but strong data foundations, cross-functional collaboration, and leadership alignment 🚀 Strategic advantage: Organisations embedding AI into sustainability are three times more likely to deliver successful outcomes The integration of AI and sustainability is not a future trend — it’s already differentiating leaders today. The challenge now is to embed it thoughtfully, responsibly, and at scale. Check out the full report: https://lnkd.in/et8c9aBp