AI in ESG Context: How to Augment and Automate ESG Decision Intelligence with AI

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Artificial Intelligence (AI) has been making inroads into the mainstream in recent years and will change the way all industries work. There is a multitude of drivers that have intensified this interest in AI in the last few years, including the scale and complexity of ESG data, a growing regulatory pressure to make fast and accurate decisions, and the need for transparency. The relationship between nature, human and machine will become increasingly important and, due to the scale and complexity of ESG data, the insight and context needed to drive the right decisions would be impossible without AI. Whether organizing messy ESG data, fighting green washing, monitoring sustainability performance, or looking for sustainable business opportunities, accessing the right information at the right time is critical to any organization’s success.

Leading the way in AI innovation

Businesses started to use AI innovations at the core of ESG Decision Intelligence Platforms to unify data and create ‘context’ to make accurate and reliable ESG decisions. MAS & Singapore government has made a tremendous commitment to making Singapore a global ESG & AI innovation hub. Many start-ups fully support this vision and are positioning themselves as key players to make this become a reality. As such, some of them are making investments in ESG AI research and development over the next three years. This investment will fund new ESG applications of AI, drive further innovation, and create jobs as part of Singapore Project Greenprint.

Start-ups are investing time, money, and resources into the biggest technological breakthrough for generations as it will transform how organizations make decisions. They’re proud to invest in Singapore-based innovations but the impact will be felt by the global economy and will start a ripple effect that will unlock ESG decision intelligence capabilities for businesses and their respective industries.

The promise of ESG AI in enterprise settings

The topic of AI is at the forefront of all business minds currently and they’re having many conversations about how AI can enhance ESG decision-making across enterprises. But we often hear stakeholders wondering where to start.

While AI adoption is growing, many AI projects still fail. Research by Gartner suggests that only 53% of AI projects reach the production stage, and according to McKinsey, only 15% of Machine Learning projects are successful. There are various reasons for these failures. For AI projects and deployments to be successful organisations should focus on three core principles:

  1. A solid data foundation: AI relies on a solid and appropriate data foundation to make accurate predictions. When your underlying data is incomplete, disjointed, and siloed, it can be difficult to build performant models.
  2. Trust and governance: The use of AI within organisations raises a multitude of trust, ethics, security, and legal considerations. The models and approaches used in organisations often lack the transparency and explainability needed for trusted adoption, especially in heavily regulated environments.
  3. Specificity and flexibility: Building models, libraries and systems for specific sustainability scenarios and allowing for adaptability in models is essential in machine learning or AI projects. This enables focused problem-solving, improved decisioning, performance and adaptability, and supports the future-proofing of the solutions.

To get the most from AI, you first need a strong data foundation that you can trust – and to do that you require context. From this foundation, you can then build transparent AI models that are both explainable and adaptable to solve a multitude of business problems from a single instance.

How to make AI humanly possible

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Using a combination of different AI and ML techniques at each stage of the data processing and analysis pipeline can ensure businesses have the data, context, and insight needed to make critical business decisions confidently and efficiently.

ESG AI Stack can be built on three core differentiating principles Context-based learning, Composite AI, and Explainability and trust.

1. Context-based learning

Just like humans, artificial intelligence (AI) relies on complete context to make well-informed decisions. Without context, even the most intelligent machine learning or deep learning algorithms lack the accuracy needed for reliable deployment.

The best approach is to combine both internal and external data at scale to build unified views of entities (organizations, incidents, alerts and locations). By incorporating context-based techniques, we can surface a deeper understanding of data, relationships, and patterns. We visually assemble these views to reveal detailed interactions, such as transactional relationships or company affiliations. This derived context from the data serves as the foundation for AI’s ability to make precise predictions that inform critical business decisions.

2. Composite AI 

Relying on one particular AI model, technique, or approach can lead to limitations in perspective, adaptability, and overall performance. Composite AI Stack uses a combination of ESG subject matter expertise and domain knowledge and a variety of machine learning, natural language processing, and deep learning techniques. By leveraging the strengths and outputs of these different and combined methods can ensure the models are more adaptable, highly accurate, extensible, and overall more effective. As well as this, exclusive access to extensive volumes of both structured and unstructured industry data to train out-of-the-box models.

This holistic approach, coupled with the ability to leverage subject matter expertise and domain knowledge as crucial inputs throughout the model building, testing, and optimization process ensures you have the capabilities to solve complex business problems and made decisions with a higher degree of accuracy and trust. Additionally, use necessary tools and capabilities in your stack to customize models or develop your own models as required.

3. Explainability and trust

In the heavily regulated domains such as banking, insurance, enterprise, and government, ensuring transparency and explainability of decisions is of utmost importance.

This need can be addressed through technology and governance practices, which rely on context-based techniques and composite AI. By leveraging these approaches, both technical and non-technical teams can easily grasp the context they have established, explain the obtained results, and validate the effectiveness of their models. This promotes the adoption of explainable outcomes while mitigating privacy concerns. This methodology enables organizations to maintain a high level of trust and confidence in the decision-making process while adhering to stringent regulatory requirements.

Operationalized AI for transformational ESG outcomes

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Whether businesses are looking to protect, optimize or grow their organizations, those three core principles help them to harness the power of AI in a trusted and productive way allowing them to:

  • Access to a wider scope of information: organizations can process large volumes of both internal and external data to gain a deeper understanding of all available information when making decisions to uncover valuable ESG insights that may otherwise be overlooked.
  • Make better and faster decisions: More effective and efficient decisions by delivering more performant models by addressing specific sustainability challenges.
  • Be flexible and adaptable: Provide cross-industry and use case coverage to support the decisions they can make at scale while also the ability to easily deploy, customize and tune models with ease.
  • Drive augmented intelligence: Through a human/AI partnership, enhance their ability to make critical business decisions, reduce the burden of repetitive tasks, and enable teams to be more productive and allocate more time to high-value activities.

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With AI-enabled ESG Decision Intelligence Platforms, organizations can expedite the process of operationalizing ESG AI, enabling them to improve decision-making, boost productivity, and gain valuable insights in a fraction of the time that it would ordinarily take.

Enabling high-performing teams with AI

When used in the right way, AI is a fantastic tool to help ESG teams be more effective and efficient. AI can provide answers to critical sustainability questions to make stakeholders more productive and ultimately drive greater levels of innovation and decisions.