The GenAI Building Blocks

Generative AI Unleashed for Sustainability

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  • Article
  • 18 minute read
  • 24 Jan 2024

Written by Andreas Feiner, Saskia Becke, Nimanshi Jha (PwC) and Dr Laura-Marie Töpfer (Microsoft). The fusion of technology and sustainability is poised to revolutionize the global landscape, ushering in an era that transforms businesses, driving increased revenue, optimizing resources, all the while safeguarding the environment. In this dynamic landscape, Generative AI emerges as a groundbreaking catalyst that challenges the conventional norms of artificial intelligence and brings forth an unprecedented opportunity for sustainable transformation. By delving into the question, how does the distinctive nature of Generative AI set it apart from traditional AI, and what specific advantages does it offer in driving the incorporation of sustainable practices within businesses?

This paper navigates the uncharted territories of technology-driven sustainability, not only offering a breakaway from traditional standards, but also ushering in a revival of corporate methodologies for sustainability. The unique capabilities of Generative AI not only redefine the boundaries of traditional AI models but also offer a strategic catalyst for embedding sustainable practices at the core of business operations, promising a future where innovation aligns seamlessly with environmental responsibility. As the narrative explores the uncharted territories of technology-driven sustainability, the focus sharpens on Generative AI’s transformative potential and its implementation. To lay the groundwork, this paper first initiates an exploration into the concept of sustainability and its paramount importance for the corporate organizations.

The Confluence of Sustainability and GenAI 

Drivers for Companies regarding Sustainability

Within the business landscape, corporate sustainability signifies a comprehensive approach that balances economic, environmental, and social goals while focusing on meeting the needs of future generations just as it is capable for the present one. For companies, adopting sustainable practices optimizes risk management, saves costs, engages employees, fosters stakeholder relations and enhances reputation. As today’s world grapples with an unprecedented environmental crisis, the call for a shift towards sustainable practices reverberates globally. What’s nudging companies towards this shift? Increased focus by companies regarding sustainability is driven by three key factors.

  1. One of the factors being regulatory pressure towards compliance both in existing as well as future regulations, where non-compliance to such regulations carries both legal and reputational risks. Continuous adaptation is thus necessary for being assured of adhesion to the most updated requirement.
  2. A second more important integration driver of environmental, social and governance aspects in companies can be seen in risk management. This will motivate the company to always better manage environmental and social risks, increasing its resilience while protecting its reputation and financial stability.
  3. Finally, it is economic potentials that arise out of a sustainable corporate orientation and take care of new business opportunities ensuring the long-term value creation of a company.

Building upon the conceptual understanding about sustainability and its paramount importance in the contemporary business environment, the narrative now directs attention to the current challenges faced by businesses in their pursuit of the journey to achieve net zero goals.

Current Challenges for Companies regarding Sustainability

Despite growing awareness, organizations encounter various challenges in integrating sustainability into their operations. Sustainability covers a range of interconnected issues, such as climate change, depletion of resources, environmental degradation and social fairness, which adds complexity. The existing landscape of sustainability is characterized by the following challenges for businesses:


Compliance with sustainability regulations and the implementation of corresponding measures is initially associated with high costs in some cases, which exerts greater pressure on companies. In addition, there is often a lack of awareness of the associated financial potential and its realization. In the long term, only those sustainable measures that also have a financial advantage will establish themselves.

The dynamic nature of sustainability requirements plus changes in regulations on a yearly basis takes organizations into a continuous challenge. Adapting swiftly to new standards and norms is crucial and adds an additional layer of complexity, requiring organizations to stay vigilant and proactive in their approach to sustainability.

Demonstrating the tangible benefits of sustainability remains a challenge. Some organizations struggle to showcase the value of their sustainability efforts, hindering widespread support and commitment from stakeholders.

Many companies are still using outdated technologies to measure their progress. This prevents them from realizing their full potential. Embracing innovative solutions unfolds the ability to effectively balance sustainability with commercial interests, meet evolving expectations and regulations, and recognize the intrinsic value of sustainability efforts.

Climate change, deforestation, and biodiversity loss, to name some of them, pose significant risks to ecosystems and human well-being.

Unsustainable consumption patterns strain finite natural resources, leading to shortages and environmental damage. Addressing this issue requires a shift in consumption habits and a commitment to efficient resource utilization.

Effective sustainability decision-making requires accurate and comprehensive data, which is often lacking or fragmented.

Translating sustainability commitments into tangible actions remains a challenge for many organizations.

Developing sustainable strategies requires a skilled and diverse workforce well-versed in sustainability principles as well as related topics e.g. enabling technologies.

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Navigating these challenges demands a strategic and adaptive approach that can be accelerated by GenAI. As organizations strive to stay ahead in the ever-evolving realm of sustainability, the commitment to innovation, education, and a harmonious integration of principles becomes imperative. By acknowledging and addressing these challenges head-on, we pave the way for a more resilient and sustainable future.

Understanding GenAI

The use of AI in sustainability is not only widely discussed in the literature but is also successfully applied in practice. In the unfolding narrative of artificial intelligence, GenAI stands as a compelling protagonist, pushing the boundaries of conventional models. While the foundation models are not entirely novel, the distinctive feature lies in their generative capabilities. To comprehend the efficacy of GenAI, it is essential to scrutinize the constraints inherent in conventional AI methodologies. Traditional algorithms demonstrate proficiency in effectively handling structured data organized in tables, they grapple with contextual understanding, often confined to singular tasks. However, sustainability challenges often present unstructured data in the form of text documents, images, and graphs, eluding the analytical grasp of conventional AI.

Dr. Laura-Marie Töpfer

“To meet and exceed sustainability requirements, companies need to measure to manage. This is where AI is a real game changer. Combining Microsoft technology expertise with PwC industry knowledge can help inform action for our customers across all phases of their ESG journey.”

Dr. Laura-Marie Töpfer,Chief Sustainability Officer Western Europe at Microsoft and co-author of this article

In contrast, GenAI exhibits versatility, seamlessly transitioning between tasks such as creating comprehensive reports, developing social media strategies, and automating workflows across diverse domains. A key distinction lies in the alignment of GenAI systems with human values. Unlike earlier models that exhibited misalignment, GPT-3+ (Generative Pre-Trained Transformer and its successors) have been trained on positive reinforcement learning, effectively mirroring human preferences and aspirations. This alignment stems from the incorporation of reward engineering and inverse reinforcement learning techniques, enabling AI systems to internalize human values and pursue objectives aligned with our societal norms. Furthermore, the sheer magnitude of GPT-3’s neural network, with over 175 billion weights, where weights refers to its parameters associated with connections between the neurons, enables its remarkable ability to process and generate complex information.

How GenAI Surpasses Conventional Approaches

GenAI, with its ability to excel in unstructured data analysis, model complex relationships, handle ambiguity, and provide possibility of human AI collaboration, stands as a transformative solution to previously mentioned sustainability complexities and challenges:

Unstructured Data Analysis

Traditional AI struggles with unstructured data, such as text, images, and social media posts, hindering their ability to extract meaningful insights from these rich sources of information. Whereas Generative AI thrives in extracting insights from text, images, and social media posts related to sustainability, due its nature of data-driven learning and by employing techniques to decipher patterns, trends, and sentiments embedded within these data types and probabilistic modeling, deep learning architectures, and knowledge graphs. By analyzing unstructured data, Generative AI can provide valuable insights that would otherwise remain hidden, enabling organizations to make informed decisions and optimize their sustainability efforts.

Complex Relationship Modeling

Traditional AI, often referred to as rule-based AI, relies on explicitly defined rules and knowledge bases to make decisions and solve problems. This approach works well for well-structured domains with clearly defined relationships and patterns. However, when faced with complex, nuanced, and ambiguous situations, traditional AI often struggles to grasp the underlying interdependencies and make accurate inferences. Generative AI, on the other hand, employs a more data-driven approach, relying on massive amounts of training data to learn the complexities of the world. Knowledge graphs, which comprise intricate networks of information, have a crucial role in improving the capacity of generative AI systems to comprehend and grasp intricate relationships. Serving as cognitive maps, these graphs efficiently represent the interconnectedness between entities, concepts, and ideas.

Ambiguity and Uncertainty

Sustainability challenges are characterized by inherent ambiguity and uncertainty. The dynamic nature of environmental and social systems, coupled with incomplete data, poses a formidable challenge for traditional AI algorithms. Generative AI, on the other hand, excels in these situations due to its probabilistic nature. Probabilistic models allow generative AI to assign probabilities to different outcomes, even when faced with uncertainty. This enables generative AI to make informed decisions even in the face of incomplete or contradictory data, making it a powerful tool for navigating the complexities of sustainability challenges.

Human-AI Collaboration

Sustainability, as a domain, frequently demands the harmonious integration of human expertise with AI insights. Traditional AI systems may falter in aligning with human intuition or knowledge, leading to conflicting outputs. The need for collaborative systems that learn from human experts is imperative. Generative AI, with its active learning and human-in-the-loop capabilities, bridges the gap between AI-generated insights and human wisdom.

In the rapidly evolving landscape of artificial intelligence, it is advisable for companies to take a holistic approach that combines the benefits of traditional AI and generative AI. To make the right decision, it is necessary to comprehend the unique abilities of each approach and their suitability for specific organizational requirements. Traditional ML excels in tasks that demand precise pattern recognition and predictive analytics demonstrating more accuracy in task specific applications. Following strict and precise rules it proves invaluable in applications such as fraud detection, risk assessment, and customer segmentation, where the emphasis is on accuracy and efficiency. On the contrary, GenAI thrives in environments that require flexibility, adaptability, and the ability to generate creative solutions – novel ideas, concepts, and content. Its applications span diverse fields, including product design, marketing campaigns, and scientific discovery, where innovation and exploration are paramount.

Applications in Sustainability

Use Cases Across Key Sectors

In the pursuit of sustainable business practices, organizations across various sectors are increasingly turning to GenAI solutions. Lets explore some diverse case studies across various industries showcasing the transformative impact of generative AI on sustainability initiatives.

Beverage and Brewing Sector

In the beverage and brewing sector, a growing trend among companies involves the strategic implementation of Generative AI. The focus is on cultivating innovation and scalability within internal processes and overall business operations. ChatGPT-based chatbots are being deployed to streamline workflows, marking a significant step towards enhancing efficiency and contributing to positive social impact by supporting their workforce through better and faster access to information.

Fintech Sector

In the fintech sector, companies are actively reshaping the landscape of financial crime prevention e.g. through the integration of generative AI technologies. The introduction of AI-powered copilots is proving instrumental in enhancing productivity within financial crime investigations. Notably, industry players are actively participating in initiatives which are dedicated to advancing anti-money laundering detection mechanisms. This collaborative endeavor emphasizes the pivotal role of generative AI in fostering sustainability within the fintech sector, demonstrating a collective commitment to upholding financial integrity and security, while also contributing to efficient governance by enhancing regulatory compliance and strengthening cybersecurity measures for greater transparency and trust in financial systems.

Healthcare Sector

Within the healthcare sector, companies are leveraging the potential of generative AI, to revolutionize patient care. A primary focus is on providing millions of patient appointments across diverse markets, utilizing generative AI for personalized care options, automated administrative tasks, and increased clinician productivity. The transparency and trust embedded in this approach allow clinicians to review and edit AI-generated content, showcasing the transformative power of generative AI in advancing sustainability within the healthcare industry. The use of generative AI in healthcare enhances patient care and operational efficiency. Besides the positive social impact through advanced medical care it is contributing to environmental sustainability through reduced resource consumption and demonstrating good governance by emphasizing transparency and clinician oversight.

Aerospace Industry

In the aerospace industry, companies are transforming the manufacturing of aircraft components through innovative methods. These companies leverage generative AI to expedite the manufacturing process by swiftly evaluating numerous design possibilities. Parameters such as weight, size, material strength, and aerodynamic performance are systematically assessed, resulting in optimal design concepts that prioritize structural integrity and passenger load support. The application of generative design extends beyond individual components to various aspects within these companies. Instances include the development of a lighter partition wall, the redesign of a vertical tail plane for a specific aircraft model, and the strategic planning of adaptable assembly facilities for wing components. Generative AI in the aerospace industry can help reduce environmental impact via lighter components, prioritizes passenger safety, and ensures.

Logistics Industry

Global logistics leaders have embraced GenAI for route optimization and resource allocation, dynamically adjusting delivery routes based on real-time data like traffic conditions and weather. This strategic use of generative AI not only reduces transit times but also minimizes fuel consumption, showcasing sustainability efforts in the logistics industry. In the broader context of AI-driven computer vision in logistics, generative AI finds application in various areas. It can generate realistic images for training computer vision models to identify and track objects in logistics environments, augment data sets, and visualize data creatively, contributing to improved efficiency, safety, and sustainability in logistics operations. The technology’s potential applications continue to expand as generative AI evolves, promising innovative solutions in the field. Generative AI in logistics reduces fuel consumption for environmental sustainability, enhances safety and efficiency for societal well-being, and demonstrates responsible technology use through strategic resource allocation, contributing to sustainable and socially impactful logistics operations.

Fashion Industry

In the fashion sector, companies are leveraging generative AI alongside robotics and 3D weaving technology to create sustainable and custom made jeans. Through innovative AI software, companies achieve automated, localized, and custom-fit manufacturing,meanwhile significantly reducing global human carbon emissions. The use of generative AI plays a crucial role in the digital automation of garment creation, optimizing production processes and contributing to a future where nothing becomes trash. In fashion, the combination of generative AI, robotics, and 3D weaving produces sustainable, custom clothing, reduces emissions, ensures local and waste-reduced production.

The integration of Generative AI offers tangible solutions across diverse sectors, revolutionizing agriculture, energy, manufacturing, transportation, retail, healthcare, education, finance, and media & entertainment. From optimizing crop yields and energy placements to reducing waste in manufacturing and developing sustainable business models, Generative AI proves to be a key enabler of sustainable practices.


Potential Applications of GenAI in Environmental, Social, and Governance Dimensions (E, S and G)

Having explored these insightful case studies of Generative AI’s real-world impact across diverse sectors, let’s have a look at the potential applications of Generative AI within the ESG dimensions.

Illustration: GenAI Applications for Sustainability - PwC

Engaging in sustainable business practices not only represents an offering but also functions as a distinct competitive advantage, setting forward-thinking businesses apart from their competitors. We at PwC guide you towards operational excellence for your individualized sustainability solutions based on your needs. Generative AI has an impact, beyond being a buzzword. It plays a pivotal role in optimizing operational efficiency, requiring minimal executive involvement.

Harmonizing Sustainability and Profitability

We stand on the verge of witnessing carbon footprints evolving into a commonplace metric, much like how we perceive billing data. Sustainability reporting (e.g. Corporate Sustainability Disclosure Directive, short CSRD) is at the same level as financial reporting. This shift marks a pivotal moment in our journey towards sustainability, where we recognize it not as a mere moral obligation but as a gateway to prosperity – sustainability is the new profitable.

Stay tuned for more insights! Together with Microsoft and the University of Oxford we are working on a paper on Data & AI for Sustainability. In this study, we explore the transformative potential of AI within the realm of sustainability and corporate profitability. Some sustainability pioneers have recognised the financial potential of Data & AI for Sustainability and are successfully realizing huge scale cost-saving potential and unlocking new revenue streams. This integration redefines the competitive landscape for visionary business leaders. Data & AI for Sustainability represent a revolutionary paradigm shift, fostering sustainability as a driver of corporate prosperity, with implications for global-scale collaboration and continuous progress toward a more sustainable and profitable future.

Data & AI for Sustainability

Please sign up here to be among the first to receive the paper.

From Blueprint to Reality: Accelerating Sustainability with GenAI

Generative AI is increasingly becoming a powerful force in driving the progress of sustainability initiatives. Its ability to automate various tasks, identify new possibilities, and improve decision-making processes has proven to be instrumental in accelerating the realization of these projects. But how can companies realize this potential? This chapter will shed light on the implementation approach.

Technical Understanding of Generative Models

Selecting the appropriate Generative Model and Type for a particular sustainability application is crucial since it can have a considerable influence on the accuracy, efficiency, and overall effectiveness of the model. The decision regarding the type of model to be used should be based on various factors, including the characteristics of the data, the desired results, and the computational capabilities at hand.

Types of Generative AI

GenAI spans various domains, encompassing text-based models like LLMs, image generators like DALL-E, VAEs and GANs, music and speech generators like tacotron, art and faces generators like styleGANs, and even code generation models like Codex. The diversity in Generative AI models, including GANs (Generative Adversarial Networks), VAEs (Variational Autoencoders), auto-regressive models, and flow-based models, opens avenues for multifaceted applications. Text generation involves crafting reports and content for sustainability reports and policies, while image generation extends to visualizations where graphics can be created for sustainable design ideas and promotional content around sustainability such as arts and videos that convey powerful messages about environmental conservation, raising awareness through visually striking and emotionally resonant pieces, sustainable product concepts, and impact images. In a similar vein, music and audio, video, 3D modeling, speech, faces, and code generation offer unique capabilities for innovation across sustainability. Where StyleGANs can be used to generate diverse and inclusive visual content, promoting representation and awareness of sustainability across different communities and cultures and code generation can generate code snippets that calculate and report sustainability metrics within software applications.

Illustration: Types of Generative AI - PwC

Foundation Models and beyond

The foundation models, be it for text, time series, image, or other domains, serve as the basis for Generative AI. These models are trained on extensive amounts of data and use techniques such as next-token prediction and masked language modeling to generate outputs that are contextually relevant and coherent. The architecture includes embeddings, attention mechanisms, and encoding methods that empower GenAI’s contextual understanding.

  • Embeddings refer to the numerical representations of words or other inputs, typically learned from large amounts of data.
  • Attention mechanisms, on the other hand, are a type of neural network that allows models to focus on the most relevant parts of the input data.
  • Encoding, meanwhile, serves as a means to convert the input data into a format that the model can efficiently handle.

The computational constraints of large models are tackled by model compression techniques such as quantization, pruning, and distillation.

  • Quantization is the process of converting the weights of the model from higher precision data types to lower-precision ones. It can be applied to weights as well as activations. Weight quantization focuses on decreasing the precision of the weights, while activation quantization deals with reducing the precision of the network’s activations in order to reduce the memory footprint of a neural network.
  • Pruning is a technique employed to eliminate unnecessary or insignificant connections within a neural network,to reduce the size of a neural network.
  • Distillation, on the other hand, is a method used to train a smaller and less complex neural network to mimic the behavior of a larger and more complex neural network.

To further improve their performance, optimized architectures and training methods like KV cache, group query attention, and benchmarking are employed.

  • A KV cache or key-value cache, functions as a data storage system that holds key-value pairs. The purpose of utilizing KV caches is often to enhance the performance of neural networks,as they can store frequently used data and avoid having to re-compute it each time it is needed.
  • Group query attention is a technique that allows attention to focus on a group of words by acquiring a sentence representation that encapsulates the interconnections among the words.It shares single key and value heads for each group of query heads. Benchmarking refers to the assessment and analysis of a model’s performance.
  • Benchmarking is important for ensuring that models are performing as expected and for identifying areas where they can be improved.

Foundation models, model compression, optimized architectures, and benchmarking form the cornerstone of Generative AI, enabling its wide-ranging capabilities.

Roadmap of Integrating GenAI into Sustainability Initiatives

Illustration: Roadmap of Integrating GenAI into Sustainability Initiatives - PwC

Identifying specific challenges that could be addressed using Generative AI models. Setting up metrics of success and evaluation of results derived from the integration of Generative AI, ensuring clear objectives are outlined.

Laying the data foundation is a very important step in the whole process. Excellent data quality is a must. It should be free from biased and private information. In addition to that the size of the data set also matters – the larger the dataset, the better the performance of the model.

Only for cases where the task to be performed is extremely dissimilar from the original model’s training data, there is a need for fine tuning. Fine-tuning a Large Language Model (LLM) involves adjusting its parameters to improve its performance on specific tasks on domains. In this process we train an additional layer to the frozen model with a labeled dataset in a specific domain, for sustainability, we can fine-tune the model with information about regulations like LKSG, SFDR, and EU Taxonomy in certain regions like the EU, leveraging the model’s faster learning curve as a strategic asset. It is advisable to use and reuse the pre-built models for sustainability reasons, only in specific cases when requested should the model be built from scratch.

There are different predefined metrics such as perplexity, assessing predictability; BLEU (Bilingual Evaluation Understudy), quantifying machine translation quality through n-gram overlap; ROUGE (Recall-Oriented Understudy for Gisting Evaluation), measuring n-gram recall for translation and summarization; METEOR (Metric for Evaluation of Translation with Explicit Ordering), evaluating translation with explicit ordering. Word Mover’s Distance gauges semantic similarity via vector space, and Cosine Similarity measures the alignment of word meanings in vector spaces. These metrics are used to evaluate LLMs based on specific applications.

As the next step of integrating GenAI into initiatives e.g. sustainability, rigorous testing becomes the priority. This step is crucial to minimize operational, security, and ethical risks, as well as to prevent adversarial attacks. The testing process includes both automated procedures that run in regular intervals, and a human-in-the-loop approach. Adversarial testing is implemented, actively seeking to challenge the model by supplying it with data likely to reveal potential issues. The combination of automated test cases and a human touch enhances coverage, ensuring a thorough evaluation. Reporting and mitigating any issues identified in the above process ensures the reliability and robustness of the Gen AI solution in addressing sustainability challenges.

These trained and evaluated Generative AI models can now be packed and deployed on premise of cloud. This process involves model packaging in a suitable format, infrastructure selection ensuring factors like scalability, performance, and cost effectiveness and other infrastructure requirements such as servers, storage and networking resources and finally implementing authentication and authorization mechanisms to control access to the model.

This step ensures vigilant monitoring of model performance, promptly addressing identified issues and guarding against bias infusion to maintain reliability, safety, and effectiveness. Model monitoring can detect data drift, negative feedback loops, and inaccuracies, preserving the integrity of Generative AI models.

How PwC can help

PwC offers a wide range of services from consulting specific to your sustainability needs to building an end-to-end Generative AI solution to solve your sustainability challenges. PwC is uniquely positioned to advise clients and stakeholders on this transformational technology to build trust in their business and drive sustained outcomes. PwC possesses a wealth of internal and external expertise, along with cutting-edge data resources, which can be effectively utilized to harness the vast capabilities of Generative AI. We offer a unique approach to AI that includes a standards-based approach to enterprise GenAI architecture, a factory model for scale, and a repeatable approach to use cases to deliver increased value for clients. Please feel free to reach out to our expert team for more information.

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Franz Steuer

Franz Steuer

Partner, PwC Germany

Andreas Hufenstuhl

Andreas Hufenstuhl

Partner, PwC Germany

Christine Flath

Christine Flath

Partner, PwC Germany

Tel: +49 171 5666490

Andreas Feiner

Andreas Feiner

Partner, Sustainability Services, PwC Germany

Tel: +49 171 8404662

Saskia Becke

Saskia Becke

Manager, PwC Germany

Tel: +49 175 5653403

Nimanshi Jha

Nimanshi Jha

Associate, PwC Germany

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