8 AI Solutions Driving Climate Change Adaptation and Mitigation

Andy Paterson • September 29th, 2025.

Climate change is accelerating, bringing record-breaking heat, extreme weather, and impacting business revenue and supply chains. To mitigate these risks, AI-driven technologies are emerging as a solution to adapt to and address climate change.

While mitigation remains crucial to reducing the long-term risks of climate change, as climate risks have increased recently, adaptation is now equally important. For most companies, the idea of adaptation is new, and they don’t know where to start, what tools they need, or what ROI they can expect. 

For adoption to scale, ROI must be at the forefront of discussions about AI solutions for both mitigation and adaptation.

We’ll explore 8 AI solutions transforming how businesses mitigate and adapt to climate change, covering practical applications, ROI, and real-world examples.

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What Are AI Solutions For Climate Change

When most people think of AI, they think of the massive data centres consuming energy and exacerbating climate change. But, what they might not know is that machine learning accounts for only a fraction of 1% of global emissions and that insights from AI could help reduce global emissions by 5-10% by 2030.

AI insights can both help mitigate emissions and help alleviate impacts through adaptation: 

• Adaptation: AI is helping people, businesses, and governments adjust to the realities of a changing climate. Examples include:
  • Extreme weather forecasting: Machine learning models can analyze satellite, radar, and sensor data to provide hyper-local predictions of floods, hurricanes, and wildfires, giving communities and businesses more time to prepare.
  • Agricultural resilience: AI-powered Growing Degree Days (GDD) trackers, yield forecasts, and pest/disease early-warning systems help farmers adapt planting and harvesting to changing conditions.
  • Water management: AI that predicts droughts and optimizes irrigation scheduling to conserve increasingly scarce water resources.
  • Supply chain adaptation: Tools that combine climate projections with trade and logistics data to help companies reroute goods, change suppliers, or adapt the timing of shipments to reduce disruption from climate-related events and trends.

• Mitigation: AI is also being deployed to directly reduce greenhouse gas emissions and accelerate the transition to a low-carbon economy. Examples include:
  • Energy efficiency: Smart building and grid systems that use AI to cut energy waste, balance renewable inputs, and lower demand.
  • Industrial decarbonization: AI-driven optimization of cement, steel, and chemical production processes to reduce fuel use and emissions intensity.
  • Transportation optimization: Route planning and fleet management tools that minimize fuel consumption and increase EV charging efficiency.
  • Carbon accounting & finance: AI platforms that automate measurements of Scope 1, 2, and 3 emissions across value chains and help companies or investors target high-impact decarbonization efforts.

In addition to having specific use cases across mitigation and adaptation, AI will also reduce R&D cycles for both mitigation and adaptation technologies. 

8 AI Solutions For Climate Change

There are dozens of AI solutions currently available, but we will showcase just a handful of high-impact ones.  We have compiled this list of eight of the best solutions addressing some of the most complex climate challenges currently facing the world.

1. ClimateAi — Weather & Climate Forecasting for Food and Agriculture

• The Challenge: Traditional weather forecasts typically provide only 3–5 days of accuracy and often overlook microclimates, exposing producers and procurement leaders across the agricultural supply chain to both short- and long-term climate risks, as well as price and yield volatility.
  
• The AI Solution: ClimateAi applies machine learning to massive climate datasets, combining them with crop biology to deliver hyper-local, short-term, and long-term forecasts, as well as tools like the Growing Degree Days (GDD) Tracker and AI agents that make it easier for producers to gain insights and ask questions.
  
• Business Value / ROI: The food and agricultural value chain could see the most significant return on investment for adaptation (up to $19 for every $). Through helping producers and buyers anticipate weather disruptions and long-term climate trends, we have seen yields increase and operational costs decrease.
  
• Example in Action: ClimateAi enabled a multinational agribusiness to identify new seed production sites in hours at just 10% of the usual trial cost, forecast a 30% yield decline for tomatoes in India within 20 years, and flag tipping points that put half its locations at risk within four years, helping shape short-term supply decisions and guide long-term investments.

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2. Google — Flood Risk Maps

• The Challenge: Climate change is increasing the frequency and severity of floods, but many communities and businesses lack accurate flood forecasting and risk mapping to protect existing and planned assets and infrastructure. Many communities, especially those in the Global South, lack the early warning systems needed to react in time and reduce loss of life and property.
• The AI Solution: Google’s FloodHub is a free tool that uses AI models and satellite data to give flood forecasts with up to 7 days of advanced warning. It utilizes virtual gauges, which employ machine learning to model the behavior of rivers and make inferences in areas where data currently does not exist.
• Business Value/ROI: Research has shown that the ROI of low-cost flood warning systems can be up to 1.4 times the investment. Google’s Floodhub is a free tool. Therefore, the ROI is calculated based on the lives, infrastructure, and assets saved.
  
• Example in Action: A study in Bihar, India, where a Google FloodHub-powered early warning system led to earlier evacuations, improved preparedness, and proactive measures that prevented injuries and illnesses, resulted in a 30% reduction in medical costs for communities.
  

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3. SupPlant — AI for Precision Irrigation

• The Challenge: Water scarcity is one of the most significant threats to agriculture under a warming climate. This is particularly the case for smallholder farmers, who are increasingly vulnerable to heatwaves and drought, and often lack access to irrigation optimization tools.
• The AI Solution: SupPlant utilizes AI and sensor data to provide precise irrigation recommendations for water management. They also offer a no-sensor option for smallholder farmers, which provides recommendations based on a proprietary algorithm that utilizes more than 2,000 global sensors and other crop and climate data.
  
• Business Value / ROI: SupPlant provides a helpful yield calculator specifically for avocados, which, based on the avoided loss of fruit size, provides an expected savings. For example, a 10,000kg yield of avocados sold at $30 per kilogram can lose $900 per crop due to a 1mm size loss from poor irrigation and watering timing.
  
• Example in Action: SupPlant helped a macadamia producer in South Africa increase yields by 21% with optimized irrigation.
  

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4. ClimateAi — Supply Chain Resilience

• The Challenge: Global supply chains are facing increasing volatility due to more frequent and severe weather disruptions. It’s expected that without adaptation, global supply chains could face up to $25 trillion in climate-related losses by mid-century. Companies still lack the long lead time forecasting to make effective supply chain decisions to mitigate these losses.
• The AI Solution: ClimateAi can forecast extreme weather events long before they materialize, giving companies time to secure supply well ahead of time, to wait until the event passes, choose another route, or diversify their sourcing.
  
• Business Value/ROI: Large companies are currently estimated to be losing an average of $182 million annually due to climate-related supply chain disruptions. Any predictions well ahead of time can help them avoid losses and ensure supply continuity.
  
• Example in Action: A roofing supply company in the US was able to secure roofing shingle supplies well ahead of time after ClimateAi helped them anticipate Florida’s Hurricane Ian, providing valuable insights that enabled them to gain an additional $15 million in sales.

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5. BrainBox AI — Energy-Efficient Buildings

• The Challenge: The built environment is responsible for ~40% of global emissions, but retrofitting existing stock to make it more energy efficient is costly and time-consuming.
  
• The AI Solution: BrainBox AI is a tool that utilizes historical building data, combined with common building temperature settings and external weather factors, to automate HVAC systems and provide building managers with guidance on how to save energy, thereby reducing emissions and costs. 

• Business Value / ROI: In addition to reducing HVAC building emissions by up to 40%, Brainbox AI can also reduce energy costs by 25%.

• Example in Action: The Dollar Tree store used BrainBoxAi across 600 of its stores, which saved the company more than $1 million in energy costs and reduced its emissions by over 5,000 tons of CO2e, contributing to its ambitious climate targets.

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6. CO2AI — Carbon Tracking & Emissions Reporting

• The Challenge: Measuring carbon across companies’ operations and value chains is a complex undertaking, as emissions data are held across multiple business functions and come in different forms. Companies are also facing mounting climate reporting compliance burdens from rules such as California SB 253/261 and the EU’s CSRD.

• The AI Solution: CO2AI automates the collection and calculations of Scope 1, 2, and 3 data and integrates it into a single dashboard for disclosures and decarbonization insights. Users can also utilize agents to automate supplier engagement and select emission factors.
  
• Business Value/ROI: A recent report from CDP found that the ROI of reporting and acting on climate data can be 21-to-1. CO2AI’s own research found that the return on investment of their AI systems yields up to 300% in the first year.
  
• Example in Action: A large transportation company successfully automated supplier and buyer engagement with thousands of companies, leveraging CO2AI’s agents to streamline their data collection processes.
  

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7. ClimateAi — Consumer Behavior & Market Insights

• The Challenge: Extreme weather and climate trends influence consumer demand patterns and behavior. Understanding how consumers behave during extreme weather events or climate trends can provide businesses with a competitive advantage, particularly in disaster-prone areas.
  
• The AI Solution: ClimateAi’s Foundational Intelligence for Climate & Economy (FICE) model analyzes how demand shifts before, during, and after climate shocks, utilizing credit and debit card transactions, as well as ClimateAi’s advanced weather models. The model provides users with a clear understanding of consumer behavior before, during, and after a storm, enabling companies to adopt best practices.
  
• Business Value/ROI: Our data show that grocery stores experienced a positive sales shift of more than 10% twice as often as a negative sales shift in response to extreme weather events. Meaning that grocery stores well-prepared for weather disruptions could see substantial sales increases when weather disruptions occur.
  
• Example in Action: FICE found that companies with well-developed plans for when disaster strikes and a brand reputation built on resilience perform best during these events. Waffle House, for example, has a very mature adaptation strategy and is the most likely casual dining spot to remain open during a hurricane or other event, typically outperforming its competitors substantially.
  

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8. Neara — Digital Twins for Climate-Resilient Utilities

• The Challenge: Power grids and utility infrastructure are highly vulnerable to wildfires, storms, and vegetation overgrowth. Testing how these systems react under certain extreme weather events is costly and time-consuming.
  
• The AI Solution: Neara creates AI-powered digital twins of utility networks, simulating climate and disaster risks to optimize vegetation management, infrastructure planning, and response strategies in the event of a disaster.
  
• Business Value/ROI: Digital twins, such as Neara’s, accelerate R&D cycles by months or years and safeguard costly infrastructure from climate-related risks.
• Example in Action: By creating a digital twin of a US energy provider’s pole network, Neara was able to determine that much fewer condemned poles had to be replaced, resulting in millions of dollars saved.
  

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Climate change is one of the most significant risks facing businesses this century. As global temperatures increase and the weather gets more erratic, the need for solutions is accelerating.  The good news is AI solutions for climate change are already here.

Not only are they here, but there is also a clear business case for adopting these solutions, and a positive ROI backs most AI solutions.

If you’re searching for practical AI solutions for climate change, ClimateAi is helping companies across sectors and value chains avoid losses and build a competitive advantage. Request a demo to calculate your ROI from adaptation.

AI Solutions for Climate Change FAQs