Which of the following statements accurately describes the impact of AI on Climate change?

1. AI has a positive impact on climate due to its ability to offset carbon dioxide emissions.
2. AI has an adverse impact on climate owing to its humungous energy demands.

Both the statements are true. Optimization of artificial intelligence solutions can be diligently employed to reduce energy wastage and thereby achieve significant resource and energy efficiency gains. Having said that, AI’s carbon footprint could be enormous because of the toll it takes on natural resources to power the data centers. The energy-hungry AI models are likely to increase greenhouse gas emissions into the atmosphere.

Only time will tell whether AI emerges as a net positive force or ends up being a flawed climatic warrior. But what intrigues me is the third dimension to the AI-climate change story that is more evolved and complete. It lies in the technology’s superior ability to predict and mitigate the effects and consequences of climate change. 

It is this predictive AI, or more precisely,y the climate AI, that I intend to talk about – How exactly does this mitigate climate change effects? Who are the direct beneficiaries of Climate AI? What technical expertise is required to leverage these solutions?

Climate AI – Explained in Three Succinct Success Stories

An Australian seed manufacturer was experiencing a downturn in its sales and marketing, mainly due to weather variations. A significant challenge was to accurately predict weather events such as heat waves or precipitation so as to ensure the seeds are planted within the optimal window and not run the risk of losing their quality or sellability. The company used a proprietary artificial intelligence solution (climate AI) to accurately predict a potential rainfall event and accordingly transported the seed well in advance. Farmers were thereby able to plant the seeds early, leading to a 5-10% increase in sales. The solution also helped the company to predict a heatwave and shift the seed-growing facility to an alternative location.

Of all industries, insurers are likely to hugely benefit from climate AI solutions. Take the case of insurers typically operating in the US. Are they making underwriting profits given the extreme weather events that continue to shake parts of the country? If it is not a tornado, then it is a hurricane; if it is not a hurricane, then it is a softball hail that damages vehicles and even homes. Naturally, it is a wonder if an Insurtech like Hippo, after having weathered multiple losses in its balance sheets, is finally leaping towards breaking even. The reason is their use of AI to assess risks and price risks better. They even went a step further to implement IoT solutions around customer homes to proactively warn and prepare them for untoward events.

Next, let’s consider the transportation and logistics industry that is reeling from a sustained disruption owing to many factors, including the ongoing wars, rising fuel prices, Baltimore disruptions, and the Suez Canal blockade. The biggest culprit, however, has been the unprecedented weather patterns, which invariably affect road, rail, and maritime logistics. Storm surges, heightened wave periods, and increased wind speeds impede maritime operations to the tune of 25 billion losses every year. Road accidents – primarily caused by icy pavements, snow, and slushy roads – account for 1300 casualties and 180,000 injuries every year.

Addressing these challenges head-on, a FreightTech company recently created a video telematics solution for real-time fleet tracking, utilizing Trigent’s deep expertise in data and artificial intelligence solutions. The solution provided real-time updates about weather conditions and helped drivers avoid weather-affected routes. It also served to alert fleet managers of any temperature deviations that affected their cargo.

To sum up, Climate artificial intelligence solutions broadly provide three benefits:

1. They offer highly accurate hyper-localized weather forecasts, which aid in short-term planning, allowing proactive actions such as adjusting planting schedules, rerouting shipments, or updating insurance policies.
2. They help you identify alternate locations that share similar climate attributes. These climate analogs can then be used to relocate your vulnerable assets. This could be shifting your farming facility or moving your logistic hubs, or enabling safe investments.
3. Provides long-term insights into evolving climate trends like rising temperatures, shifting precipitation patterns, or sea-level rise. This informs organizations to strategize their investments in crop R&D, infrastructure, and enter/exit portfolios.

How to Build an Effective Climate AI Solution 

A good climate artificial intelligence solution is a result of how well you have grounded your AI model with current, accurate, and high-quality enterprise datasets. The phrase “no data, no AI” cannot be more true in this regard. However, it should be noted that data volume is rarely the issue, given the amount of enterprise data that has ballooned over the last decade. What matters most is the data quality that may come in the way of your AI model development.

Five Data Qualities that Define Your AI Output

Achieving data quality means the ability to make the transition from circle 1 to circle 2, as shown in the figure below. From data that is ‘lost, hidden, inaccurate, incomplete, and inaccessible’ to data that is ‘visible, secure, accurate, complete, and accessible.’

The pressing question is how to accelerate this data quality and speed up the AI transformation. 

Embark on a Twin Transformation of Both Data and AI 

Is it possible even for companies in their early stages of digitization to embark on AI modernization? In other words, what does it take for companies with lower levels of digital maturity to adopt AI Solutions faster?

The answer lies in unifying data and AI workloads. This is where Technology partners like Trigent come into the picture. We have developed deep expertise in data and AI modernization, coupled with extensive industry knowledge that helps you accelerate your data and AI transformation. By leveraging the LakeHouse architecture, here’s how we accelerate the AI and data unification:

Steps to Unify Data and AI Workloads

Centralized Data Management

1. Consolidate structured, semi-structured, and unstructured data into a single unified platform.
2. Eliminate data silos, ensuring seamless access and visibility across the organization.
3. Enhance data reliability, lay the foundation for robust AI models, and make accurate predictions.

Optimized AI Workflows 

1. AI models are supplied with real-time data seamlessly, without delays or bottlenecks.
2. The system is built to manage large data volumes, meet the expanding demands of the business.
3. Designed for both accuracy and efficiency, the AI models optimize computational costs and energy consumption.
4. Automate throughout the AI lifecycle, minimize human intervention, and ensure efficient operations.

Secure, Scalable, and Collaborative Infrastructure 

1. Trigent’s unified platform fosters collaboration between data engineers, scientists, and business users, expediting AI project development.
2. Using advanced tools like Databricks and Delta Lake, we ensure real-time decision-making with adaptive insights.
3. Scalable infrastructure and strong governance mechanisms enable organizations to maintain security while innovating at pace.

API connectors for seamless integration 

1. API connectors serve as the communication backbone between AI systems and enterprise applications, ensuring smooth integration of AI capabilities into your existing workflows.
2. The API connectors should be able to integrate your systems with those of your partner systems for higher supply chain efficiencies.
3. For instance, Trigent offered an air freight solution that seamlessly combined the systems of both the shipper and 15 partner airlines. Through the solution, the shipper was able to select the best carrier based on the delivery speed, cost, and execution.

Far-reaching Effects 

Climate AI’s benefits are likely to extend beyond the aforementioned industries as its ripple effects will be prominent across all sectors that directly depend on natural resources. Food and Beverages will see more control in predicting water availability through artificial intelligence solutions, particularly in water-stressed regions. They could ensure raw material supplies despite climate volatility. Renewable Energy and Utility Companies will benefit from a precise forecast of weather, thus maximizing the efficiency of their sources. For example, an accurate prediction of sunlight intensity and duration helps solar farms estimate daily or weekly production. Energy operators can be assured of greater grid stability, balancing energy demands with supplies. Efficient operations reduce the reliance on non-renewable backup power, ultimately lowering carbon emissions.

A Climatic Warrior in the Making

Globally, reports suggest AI is on track to offset 5-10% of carbon emissions by 2030. At Trigent, we believe the onus is now on enterprises and independent software vendors to harness the capabilities of the novel climatic warrior and bring to light intelligent solutions that don’t just assure competitive advantage but also create a safer and sustainable earth for the next generations to live and thrive. 

Business Benefits	Examples
Enhanced Risk Assessment	Insurers use AI to predict natural disasters and improve underwriting accuracy. Farmers optimize planting schedules based on AI-predicted rainfall patterns.
Cost Optimization	Logistics companies reroute shipments during adverse weather to save fuel and time. Energy firms optimize renewable energy outputs to reduce operational waste.
Strategic Decision Making	Agricultural enterprises select drought-resistant crops using AI-driven insights. Retailers assess climate risks for store locations to ensure safety and profitability.
Improved Sustainability	Solar farms estimate production through AI weather forecasts to enhance efficiency. Utility companies stabilize grids while reducing reliance on non-renewable backups.
Disaster Preparedness	NGOs deploy AI-driven early warning systems for floods and droughts to save lives. Japan’s tsunami warning systems leverage artificial intelligence solutions to manage resources during emergencies.
Supply chain optimization	FreightTech companies track fleets and optimizing routes to avoid weather-related delays. Retailers minimize inventory waste by adjusting stock based on climate predictions.

Featured image by no one cares on Unsplash