Achieving your Sustainability Goals with Data & AI

1. Many companies struggle with decision-useful data – let the aspiration for perfect data not prevent us from getting started

In Europe, the Corporate Sustainability Reporting Directive (CSRD) entered into force last year which strengthens the rules concerning the social and environmental information that companies need to report and applies for the first time in the 2024 financial year. However, a majority of companies struggle with sustainability data and do not have the necessary infrastructure in place to comply with the new regulation or build more complex use cases. Other challenges include data gaps, data availability and generally low data quality related to sustainability.

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Around 80% of companies still use Excel to track their sustainability data.—Michelle Lancaster, Sustainability Director at Microsoft

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Therefore, companies should initially start by identifying where the most significant data gaps exist. Technology, such as IoT sensors, can help to collect live data from operations and measure carbon intensity at the outset. With a clearer understanding of where data improvements are needed, companies can then develop the necessary infrastructure to process and centralize this data. This approach ensures that the data collected is directly aligned with operational needs, thereby ensuring a more effective integration of data into decision-making and enabling a feedback loop that informs and improves operational processes.

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In order to implement a lot of the internal use cases for manufacturing, for example, there should be a linkage between IT and OT to get real-time data from operations to measure carbon intensity and subsequently fine tune the operations to drive emissions reduction.—Isada Hiranwiwatkul, Managing Director & Senior Partner at BCG

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Initially, the data will not be perfect but that should not prevent companies from getting started. As accuracy increases over time, confidence in the data will grow and can kick-off a flywheel effect. Dietmar Siersdorfer, for example, describes how they experienced this effect at Siemens Energywhen they decided to only publish the sustainability report to capital markets to encourage data collection. What began as small data initiatives across the organization, evolved quickly as teams grew more confident on the data they gathered. Today the creation of the sustainability report runs very efficiently and even drives organizational initiatives.

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There is an impatience around data: everyone wants to have perfect data right away, and yet most are just getting started. It is important to recognise this is a journey, and acknowledge every step as progress.—Anna Stanley-Radière, Director of Climate Transparency at the World Business Council for Sustainable Development (WBCSD)

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2. Data alone will not reduce emissions – people and organizations will

Having better data related to CO2 emissions and other relevant sustainability indicators can lead to more sustainable business decisions, for example, during product development, procurement, or manufacturing. However, most leaders still hesitate to base their decisions on sustainability data due to issues with data availability, quality and liability – but also governance. Companies ultimately need to put a central business intelligence system in place based on sustainability data to enable decentralized decision making.

It’s about getting the data to the point of decision making, supporting those micro-decisions that people take hundreds of times a day.—Nicolas Hunke, Managing Director & Partner, BCG

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However, for organizations and individuals to change their behavior, often more than just data is required. Companies need to communicate their priorities and provide incentives to encourage more sustainable choices. As an example, according to Michelle Lancaster, they introduced an internal carbon tax system at Microsoft. It charges departments based on their actual CO2 emissions which led to several data improvement projects and significant reductions of the carbon footprint. The combination of both sustainability data and the right incentives will lead to an overarching shift in mindset and actions.

Having the right data in your hands, is important to make the right decisions.—Daniel Schmid,Chief Sustainability Officer at SAP

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3. Sustainability is a team sport – collaboration is required to solve the global challenge

Companies must look beyond their internal operations and work together with suppliers and customers to create transparency on emissions across the value chain (scope 3). Today, it is impossible to reliably exchange emission data due to varying carbon measurement methods and limited ability to validate external data. Global standards and trusted platforms are required to easily exchange and verify emission data – both is already being worked on by several organizations but is still in its infancy.

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Companies should not hold their data regarding the sustainability and impact within themselves as silos – there are some growing data exchange platforms in the industrial space, such as Catena-X in the auto industry, to exchange and report data from each stakeholder to ensure the sustainability throughout the wider and end-to-end supply/value chain.—Yushi Nagano, Head of JICA DXLab

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One example for such an initiative is PACT (Partnership for Carbon Transparency) which is bringing together global stakeholders to develop a standard enabling organizations to calculate and exchange consistent, comparable and granular emissions data, creating transparency on the impacts of their products and enabling informed decision-making across value chains in all sectors. Additionally, global platforms enable the tracking of corporate scope 3 emissions and peer comparison to identify additional reduction levers.

Another example is the Japan International Cooperation Agency (JICA) where Yushi Nagano and his team are building a central platform around sustainability data in the transport sector. Therefore, they have developed a standardized data stack to collect the data from different stakeholders and are now working on gradually increasing data quality. While most companies have acknowledged the need to collaborate and the initial momentum is here, a lot more development, with focus on standardization and initiatives to build trust, is required to enable the global exchange of sustainability data along value chains.

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4. Reporting can only be the start, enabling use cases is even more important – AI can act as a turbo charger

The advent of new sustainability regulations is asking companies to comply and put their effort in setting up the required reporting framework. While reporting is the first step in a company’s sustainability journey, real business value is generated from reduction use cases. Nonetheless, many companies struggle with identifying the right use cases that will have lasting business impact. Leaders in this space have shown how analytics and AI can be a turbo charger in this context.

The analytics and AI side is really what will get you to net zero, it’s not the reporting side.—Duan Du ToitGroup, Executive Head of Energy at Vodacom

Starting use cases include the usage of AI to improve the quality of measurements, create structured data from various input sources and automate tasks with little to no value. Companies can for example leverage Generative AI to accelerate the setup of a reporting platform and initially fill in the first content, an otherwise time-intensive manual task.

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In the short term, AI has the biggest potential to help companies improve the quality and efficiency of company emission measurement.—Charlotte Degot, CEO at CO2 AI

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Beyond that AI can enable individuals across the organization to interact with a company’s sustainability data. Michelle Lancaster of Microsoft describes how Generative AI will enable companies to ‘chat with their sustainability data’ to quickly generate intelligent insights. Companies can run ‘what-if’ simulations like switching suppliers based on carbon footprint or engaging suppliers to switch the regional location of their production facility based on grid-level data. In combination with live-data, AI-based solutions can optimize plant operations. By analyzing real-time data from various sensors, companies can optimize overall energy use, predict maintenance needs, and streamline processes. This not only boosts operational efficiency of plants but also contributes to the overall sustainability performance by reducing waste and conserving resources.

In Dubai, for example, we use an AI-based solution to operate gas turbines and have improved their performance by up to 3.5 megawatts and reduced NOx emissions by as much as 10 percent.—Dietmar Siersdorfer, Middle East Managing Director at Siemens Energy

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In the future, AI-based solutions can also help in managing complex systems like energy grids or model and evaluate climate risk scenarios. In any way, business leaders should not be held back by fear of non-compliance but rather identify opportunities to unlock business value from their climate data beyond reporting.

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