SustAInable Climate Actions

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According to a recent report[1] from our business partner Capgemini, AI can help enterprises reach up to 45% of the targeted 2-degree limitation of the Paris Climate Agreement. 

There are not very many technologies like AI, that span across industries on all levels with the potential to optimize energy usage, reduce wastage, and support complex decisions to reduce carbon emissions economically. However, AI in this field is still to a large extent unexploited.

The good thing however is, that organizations are eyeing the possibilities for either improving their environmental footprint of existing operations or creating new business opportunities and both smaller pilots as well as larger implementations are underway, in a number of areas, such as:

Optimizing energy consumption in relation to power demand and availability of green power in the grid.  The IBM Flex Energy solution developed together with the City of Copenhagen and now being implemented by Andel in East Denmark is an example of, where AI I used to predict as well demand of power from buildings and factory plants as the estimated generation of power from wind- and solar farms.

Optimizing production operations:  Using AI to analyze past manufacturing records and output logs to suggest the optimum settings. We have piloted the IBM Watson-based Plant Advisor in a project with an industrial customer, demonstrating that AI may remove the ‘set-and-forget’ syndrome, often seen in production plants:  That machine operators set the machine parameters once, and then forget to revisit the settings, even if raw materials as well as machine efficiency change over time.  The result from applying AI to monitor the performance as well as machine settings were significant savings in energy and wastage.

AI in designing new products: Companies are also looking into how so-called generative AI can be used to suggest new, by the humans often unimaginable products design targeting goals such as minimizing materials usage, weight, while preserving strength and functionality. GM used Autodesk’s generative design tool to design surprisingly ‘organic looking’ new seatbelt bracket designs that are 40% lighter and 20% stronger than the traditional ones.

Sorting wastage to maximize re-use: A Danish company like ReTec Automation has designed AI-power robots that can sort scrap a multitude of materials with up to 98% precision, by using real-time imaging and sensor technology to analyze metal scrap on a rolling conveyor belt.

Emissions Management:  Advanced AI-based technologies are emerging to identify, measure and track emissions of greenhouse gases.  Technologies like satellite-based or airplane spectroscopy of methane emissions with AI-based visual analysis down to the single square-meter, combined with advanced analytics,  can be used for benchmarking – identifying areas for improvement, and for reporting.

Quality control and predictive maintenance: Nearly half of product quality controls include some sort of visual confirmation to ensure position, shape, color, finish, etc. live up to the expected standard.  And with high volume and velocity, this is a task for AI.  AI can learn from human evaluation and validation to spot mistakes in real-time. Combining this with predictive maintenance – using AI to monitor and evaluate results from the quality control, machine parameters, and readings, validation time can be shortened by 80% and errors reduced by 7 – 10%. This means less wastage and less resource wastage caused by unexpected downtime and is what we have seen f.i. in our strategic partnership with ABB, where we combine ABB’s Ability with IBM Watson, creating a transformative Industrial Artificial Intelligence Solution.

Inventory optimization:  Predicting customer demand down to the single SKU and location is critical to ensure not only living up to customer expectations of product availability but also to minimize obsolescence and wastage. Traditional inventory management systems may use linear prediction models combined with manual, human decisions. The use of AI is aimed at making the process more efficient and dynamic, based not only on historic patterns but also on predicted data such as weather, competitive intelligence, supplier monitoring, supply forecasts, etc.  A company like REI – Recreational Equipment Inc, an internet retailer with more than 155 stores and 3 distribution centers, increased sales by more than 100 MUSD by using AI to optimize replenishment, and inventory location. Sales were increased by uncovering unseen inventory, reducing the annual 800.000 non-deliveries. In addition, they were able to reduce the number of split-orders, reducing the carbon footprint in transportation.  

Optimizing transportation.  Being able to take into account real-time data such as weather, traffic, load, driver performance, etc., AI can significantly reduce both distances traveled, fuel consumption, and the need for capacity. A company like Alibaba has reported having saved 30% in distance and reduced the need for capacity by 10% by using the AI-based Cainiao Network logistics platform, which is connecting and optimizing 6.1 million delivery routes globally.

The opportunity for reduction of emissions of course varies from industry to industry. We know that IT and IoT can help reduce energy consumption in buildings between 25 – 40%. Capgemini estimates that AI cross-industry can deliver 11 – 45% of the Paris targets, with retail at the top of 45% while wholesale may deliver 11%.

What are the best-in-class doing?

Climate leaders are far beyond the point of realizing that their GHG emissions matter. They are concerned about how climate risks may threaten their business continuity and that their actions will eventually determine the market’s perception of their reputation and brand.  Mid and long-term this is a threat to their topline as well as profitability forcing them to be innovative in where and how they can apply AI and other exponential technologies in their climate actions.

They align their business strategy, climate ambitions with their data- and AI capabilities, identifying the gaps in targets as well as skills, and have a focus on their own operations (scope 1 and 2) as well as their indirect emissions (scope 3) from suppliers of products and services, like logistics operators, business travel, franchise takers, etc.

They are transparent about their GHS emissions, benchmarking themselves in the industry, and enabling their customers to make informed choices about the products, services, and means of delivery considering the climate impact.  As an example, the container line Hamburg Süd has launched a carbon calculator allowing customers to calculate the carbon footprint of their liner service container (TEU) port-to-port – anytime, anywhere.  Maersk offers today Maersk ECO delivery – allowing customers to choose carbon neutral transports in ships powered by biofuels.

And not least, they ensure that their sustainability teams know how AI can support their climate vision and their data science team in how to take into account the need for data, insights and optimization to minimize the company’s climate impact.

It is advised to take a structured approach in identifying the most promising use-cases for applying data and AI to bring down their GHG emissions, piloting the use-cases to evaluate the feasibility, data gaps, sustainability, and financial potential.

IBM Sustainability Garage

Most companies have now realized that there is no plan B – and they have to act. Applying AI and other exponential technologies can make a huge difference at a relatively low cost.

This is in fact, what we are offering our clients to explore in the proven (for instance with Bestseller) IBM Sustainability Garage.

A Sustainability Garage Workshop, which typically runs over 3 – 5 days, helps our clients create a roadmap of agile, realistic, but ambitious projects to help them reach their sustainability goals. It is a collaborative experience, where we bring to the table, the right people you, from IBM and our eco-system, a catalogue of workflows powered by latest technologies, a new way of working such as applying customized design thinking and agile DevOps for prototyping on the fly.  It can be delivered anywhere in the world – virtually as well as face-to-face.

If you would like to know more about how IBM can help you apply best practices to ignite or accelerate your sustainability journey, please feel free to contact me at

Source[1]: Capgemini Research Institute. “Climate AI – How artificial intelligence can power your climate“. Retrieved from:

Research & Innovation Executive, IBM Research - IBM Watson

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