How to use AI to uncover your Sustainability Data

A few years ago, during the same period when I delved deeply into the realm of AI, Environmental, Social, and Governance (ESG) initiatives also appeared on my radar as compelling emerging topics. It became apparent rather quickly that this was no mere coincidence; the intricate relationship between Artificial Intelligence and Sustainability unfolded before me. AI, with its formidable capacity to manage knowledge (collect, model, measure), emerged as a powerful instrument crucial for realizing sustainability goals.

This article weaves together a mosaic of diverse experiences that we, as Neodata, have been through over the past months and continue to make daily.

These experiences stem from discussions and interactions with various enterprises and organizations, delving into the challenges and opportunities intertwined with sustainability.

It explores how data and AI serve as catalysts for innovative solutions in the pursuit of sustainable practices.

ESG: what does it mean?

Environmental, Social and Governance ESG is a process for evaluating company performance based on criteria that allow the impact of operations to be measured on a series of factors relating to environmental sustainability, social responsibility and company management and governance.

ESG opens the door to a comprehensive, nuanced understanding of a company’s influence, encompassing not only business outcomes but also its multifaceted impact on the environment, employee relations, managerial integrity, and the ability to engage stakeholders contributing to value creation. This includes representatives from local communities where the company operates, emphasizing a holistic approach.

Artificial Intelligence for sustainability and ESG represents a very important support for dealing with a context characterized by an increasingly higher number of variables thanks to an increasingly relevant availability of Data.

This is reality, today, not future scenarios.

Here some examples of concrete actions that AI runs already to make human activities more sustainable:

  • the combined use of IoT sensors and predictive algorithms can optimize energy consumption by reducing waste through intelligent management of demand in a smart city.
  • Planning mobility based on increasingly precise forecasts of many variables behind traffic volumes.
  • A sustainable agriculture model using drones can optimize water consumption and the use of fertilizers, reducing the waste of resources and increasing yields crops through irrigation commensurate with the real needs of the land.
  • Furthermore, integrating meteorological predictive algorithms aids in accurate weather trend assessments, empowering agricultural companies to strategically plan sowing and processing.

All these are example of measurement of Sustainability Data are input to improve processes and strategy of human organizations to reduce their impact on environment.

Data collection is always the starting point.

Data Collection: the relevant information is everywhere inside the company

The data and information on which these ESG parameters analyses are based can arrive from extremely heterogeneous sources: from DBs and excel files, from the HR department, from the IoT installed in the assembly line, from the purchasing department or from the administration. Many sources, very different with different levels of depth, accuracy and language.

Artificial Intelligence and digitalization helps companies to organize a more complete view of these sources, to map them and manage them to obtain the data necessary to determine their ESG impact.

Data domain is the natural element for our company, so no surprise if this is the key aspect we emphasize telling about our projects in ESG area.

When engaging with a new customer, the Enterprise Resource Planning (ERP) system becomes the initial port of call for sourcing essential data. Many ERPs already incorporate ESG functionalities, making them excellent starting points. However, often, the data available here covers only a fraction of the required information. In some cases, companies lack ERP implementation altogether, and critical data are scattered across Excel files or, worse, exist solely on paper. Thus, the first step is to guide the customer in establishing and populating an efficient ERP, serving as the central hub for collecting data related to key company activities.

But also in the best cases, some of key data for ESG calculation never stay on ERP: for example waste management, water use, gas emissions, energy consumption. Third party certifications can provide these info. We had a project with a power companies who want to build a platform allowing its customers to self-extract this kind of information/data (via API or bulk export) from their electricity counters.

Amount of gasoline or plastic or other raw materials can be done in several ways: from ERP, or IoT. When no the final solution is to scan invoices to calculate the total quantity of what purchased. When data are not well organized in databases, process of pdf, word or excel documents to extract data using NLP (Natural Language Processing) or other similar technique is the way.

Crucial ESG-related data, such as social and governance metrics, demand NLP processing of documentation like customer reviews, media reports, annual reports, and codes of conduct.

In our pursuit of excellence, Neodata has developed an AI classifier grounded in NLP, capable of scanning text and providing rankings for each of the 17 ESG parameters.

Communication-related information, including blogs, press releases, and social media posts, neeeds careful processing and evaluation. As Neodata, we have developed an AI classificator based on NLP and able to scan text and running a ranking for each of the 17 ESG parameters.

For one of our customers in the Media sector, the impact of video content on ESG evaluations prompted us to devise an AI-driven semantic analysis of each video frame for ranking purposes.

Many other data contributes to ESG calculation, spanning HR metrics for evaluating gender gaps and territorial differences, contributions to sporting events for disabled individuals, CO2 emissions, and best practices in supplier relationships.

Data collection requires collaboration and contribution from different people and departments. Simple forms facilitating data input, coordination, responsibility assignment, and deadline adherence serve as fundamental tools, sometimes not requiring AI but remaining vital in achieving the goal of comprehensive data collection.

What to do with ESG ranking? Why is it so important?

For each company, collection of data and ranking of ESG parameters is not only an obligation, a way to build the sustainability report required by local and international laws, but a way to re-design the company and its strategy to shape the organization and processes to become a real sustainable company.

Beyond compliance, this endeavor opens the door to numerous business opportunities. Let’s explore some examples:

Brand Reputation

Being a sustainable company is a way to improve own brand reputation that also means additional opportunities for sales: consumers decide to buy a t-shirt evaluating not only the quality, the style or the price, but also because the production process is sustainable.

A company that demonstrates its commitment to sustainability can enhance its reputation and attract more customers, investors, and partners who share its values. A company that aligns its sustainability strategy with its core business can also differentiate itself from its competitors and create a loyal customer base.

An exercise we have done a few months ago was to focus not on numeric datasets but on opinions or perception, measuring the web buzz. How the brand is perceived from web and social media for each of ESG parameters? Is the public opinion aware of effort of the company for sustainability? How they evaluate?
ESG analysis done by AI can provide insights for specific actions and also new content that can help marketing and communication departments.

New design and production of products and services

Artificial Intelligence doesn’t just contribute to better sustainability by developing new products and services and minimizing waste. It plays a crucial role in identifying and activating new business models. Analyzing the predisposition of the target market or potential customers towards evolving product usage into services allows for the development of servitization projects. This strategic shift helps plan the achievement of sustainability objectives by aligning with market demands.

Talent attraction and retention

A company that embraces sustainability can attract and retain talented employees who are motivated by a sense of purpose and social impact. A company that fosters a culture of sustainability can also enhance its employee engagement, productivity, and well-being.

Conclusions

We have explored in this article some of the examples of how sustainability can provide different kind of strategic advantages to companies in various ways.

Be responsible, run business in a sustainable way is not easy and requires a strategic approach that involves the whole organization and its stakeholders and it starts from knowledge and ranking of real data. Mapping and collection of these data is crucial and can be achieved via different approaches: we shared some real experiences from the field.

For these very reasons, sustainability isn’t merely a facet of Neodata’s strategy—it’s a fundamental cornerstone. As we move forward, the synergies between sustainability and AI will continue to shape the trajectory of businesses, and Neodata is committed to leading the charge in this transformative landscape.

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