The Emerging Technology Frontier

Future-proofing Business Success

Navigating Business Efficiency, Innovation, Data Security, Compliance, and Sustainability in the Age of Emerging Technologies The Emerging Technology Frontier: Future-proofing Business Success

Table of Contents

Introduction

Document digitization: Translating physical documents into digital records

Optimizing ML Operations on Edge: Real-Time Deep Learning Solutions on Edge Devices

Upscale business performance through Enterprise Security and Machine Learning Governance

Unlocking Responsible AI for ESG: Ways AI Can Drive Sustainability and Social Impact

In the era of Industry 4.0, businesses are heading towards a future defined by disruption and technological breakthroughs. Emerging technologies are steadily catalyzing and reshaping the business landscape, one use case at a time.

Amidst the ever-evolving market dynamics, as forward-thinking professionals strive to future-proof their businesses, they face a complex narrative of interconnect- ed factors shaping sustained business success in the present and, more importantly, in the future. On the one hand, deep learning and AI, especially Machine Learning Vision Solutions (MLVS), are driving Industry 4.0, showcasing the versatility of emerging technologies for various business applications. Conversely, AI's growing influence on environmental, social, and governance (ESG) performance underpins the significance of agile change management and seamless technological integration to bolster business sustainability. Moreover, as enterprises embark on the path to future-forward business success, ensuring enterprise data security and machine learning governance and tackling intricate tasks like document management becomes integral. Thus, enterprise perfor- mance optimization necessitates robust measures such as data encryption, access controls, and compliance gover- nance. Leveraging emerging technologies like machine vision and natural language processing for automated digitization of physical documents in enterprises becomes crucial for enhancing business efficiency. These intertwined insights offer a captivating glimpse into the business world's present-to-future trajectory primed for growth and transformation, urging businesses to assess their current standings and operations and reinvent for optimum integration of emerging technologies. Join us on this enlightening expedition into the frontiers of emerging technologies like AI, machine learning (ML), machine vision, and natural language processing (NLP) to unlock untapped potential for future-ready business success and sustain- ability practices across enterprises globally.

Document digitization is reinventing the landscape of business operations, but how is this transformation achieved, and what challenges lie ahead? In Document digitization: Translating physical documents into digital records , Fractal experts delve into the cutting-edge AI/ML technologies heralding this evolution, like machine vision and NLP. Discover the efficiency improvements achievable through digitization while navigating intricate obstacles such as processing handwritten and multilingual documents, addressing data inconsistencies, and ensuring compliance with personal information regulations. With insights into various stages of digitization and the importance of tailored strategies aligned with business objectives, this chapter offers a comprehensive view of document management's current and future landscape for business efficiency. In the era of Industry 4.0, data and advanced analytics have come to the fore for tapping into and boosting business success. Upscale business performance through Enterprise Security and Machine Learning Governance explores the emerging need for integrated, risk-free, and scalable systems, navigating the complexities of varying security standards across industries and the challenges of organizational silos. This chapter offers insights into the tightrope between robust security with time-optimized deployment and the essential role of automation and collaboration in governance. A grasp of these significant strategies to leverage the potential of machine learning operations will equip you for success in an ever-evolving business terrain, whether you are a business leader, tech enthusiast, or security professional.

Next, discover the future of edge ML technology and Fractal’s pioneering role in this technology revolution in Optimizing ML Operations on Edge: Real-Time Deep Learning Solutions on Edge Devices . This chapter delves into the transformative shift towards edge ML in machine vision solutions, offering faster data processing and addressing the privacy and latency concerns of cloud computing. While the journey of edge-based machine learning presents its unique set of challenges, ranging from data scarcity to security concerns, innovative solutions like federated learning and advanced encryption are emerging to tackle them head-on. This article shines the spotlight on Fractal's groundbreaking work in deploying advanced IVA models on edge devices, exemplified by practical applications such as drone surveillance for the Indian Army. Finally, Unlocking Responsible AI for ESG: Ways AI Can Drive Sustainability and Social Impact explores the synergy between present-to-future business success and corporate social responsibility (CSR), emphasizing the transformative role of artificial intelligence (AI) in bolstering environmental, social, and governance (ESG) performance. AI's applications span monitoring environmental impact, measuring employee well-being, harnessing data governance and analytics in ESG operations and enhancing governance transparency while grappling with challenges like measurement complexity, AI's environmental footprint, a need for more standardization, and balancing immediate financial gains against long-term goals. It advises organizations to adopt ESG strategies, highlights AI's potential for fostering sustainability and social impact, and urges collaboration among stakeholders, organizations, policymakers, painting a vivid picture of the challenges and triumphs toward a responsible, sustainable tomorrow.

Document digitization: Translating physical documents into digital records

Authors: Akash Gupta, Architect; Sarveshwaran Jayaraman, Senior Data Scientist; Ritesh Thakur, Director, Machine Vision, Conversational AI

What should be digitized — and when? Most document digitization solutions focus on situations where the data is being consumed and used in the present day. While these documents may not be critical for decision-making per se, they are important for operational efficiency and the simplification of processes. While document digitization falls within an organization’s overall digital transformation, digitization comes after digitalization (which provides the base for capturing structured data). Transformation journeys can be divided into four phases – 1. Structured data capture 2. Application development for structured data 3. Unstructured data capture 4. Application development for unstructured data Many companies have already exhausted the insights they can gain from their structured data and must focus on unlocking value from unstructured data. That is where document digitization comes into play. In other words, document digitization comes later in phases two & three. Where to start There is no “one-size-fits-all” approach. Digitizing documents can be complex, depending on the document type and the information. The best solutions will rely heavily upon these factors for successful completion. A successful digitization initiative requires careful deliberation of several key considerations. The first and most important is identifying opera- tions-heavy tasks that require large amounts of data entry

This article explores the power of document digitization in modern workflows and business processes, highlighting how digitization enhances efficiency.

Comprehensive data about customers, vendors, and entities must be collected and maintained for businesses to succeed in their operations, including financial and human resources information. However, transforming this data from traditional physical documents into structured data sets requires significant manual effort. Recent advancements in machine vision and natural language processing(NLP) have produced automated solutions that reduce manual efforts in digitization. Despite these advances, several challenges remain. From a business perspective, multiple touchpoints can result in data inconsistencies and processing delays. Managing diversity — different documents that have the same purpose, for example also poses a significant hurdle. From a systems perspective, technology should be deployed judiciously to ensure that it doesn’t negatively impact a process’s efficiency and effectiveness. Before translating physical documents into digital data, careful consideration needs to be given to the who, why, what, and when of digitization.

However, before digitizing physical documents, there are other factors to consider.

or translation. The next step is to determine the necessary tech infrastructure, including whether to use on-premises or cloud infrastructure, what IT norms need to be followed, and how much investment in the infrastructure is required to run the models (e.g., a base GPU). Each solution should also have the flexibility to alter or expand its algorithm logic to ensure extraction occurs in line with the business requirements. Any digitization components from other providers that an organization has already invested in will require a solution to be built around them, highlighting the need for solution providers to be cloud agnostic. Pillar 1 Sophisticated AI/ML technology, like machine vision and NLP, enhance the digitization process’s accuracy and efficiency. Pillar 2 A robust engineering architecture that can be scaled as needed to support operational optimization & handle large volumes of data.

Data storage, paper trails, compliance, and privacy

Information extracted from an unstructured source, such as a document scanned as a PDF, requires much less physical space to store than a hard copy. But while the storage of the digitized data is not an issue, the question of what happens to the original documents after extraction is important to address, as paper trails for compliance are critical. Generally, once digitized, documents are moved to a cost-effective storage option like a blob or cold storage, making them easily manageable and accessible when needed. Of course, data extraction and storage create a compliance challenge, particularly concerning personally identifiable information (PII). Data encryption cannot happen “on the fly,” as algorithms need to understand the context of the information to extract relevant data.However, data can be encrypted at the moment of extraction, ensuring that information is secure and cannot be accessed without authorization. Leveraging these cornerstones and considerations to build a document digitization framework, the subsequent step is an optimized solution for translating physical documents into digital assets.

Pillar 3 Digitization solutions that align with business processes and goals to generate strong ROI & adoption by users.

Document digitization solution framework

Five Steps to Digitization

Bringing it all together To bring it all together, an orchestrator acts as the framework for the system by linking the five independent, modular components together, ensuring that they communicate and provide optimal outputs.

Five main components are necessary for document digitization. These components make up the microservice architecture of Fractal’s Doc. Digit solution and interact with each other as needed to streamline end-to-end document digitization for different business processes. Module 1 : Consolidation The first component involves consolidating data from various sources — emails, chats, and shared locations — into a single source of truth, eliminating duplicate or repetitive documents. A centralized location for all documents ensures that the digitization process runs smoothly.

Today’s biggest challenge

In addition to common issues like data protection, computing, and power cost, document digitization has two significant challenges: handwritten and multilingual documents. The first challenge is the recognition of handwritten documents. Although OCR tech is being used to extract such information, it still needs to be made easier to recognize handwritten text accurately. To solve this problem, Fractal is investigating the application of the intelligent character recognition (ICR) framework, which uses convolutional neural network (CNN) models to determine the most probable characters or words in handwritten text. The second challenge is the digitization of multilingual documents. While it is relatively straightfor- ward to digitize templated documents such as invoices, documents that require an accurate interpretation of context, such as legal contracts, are proving to be much more challenging. We are assessing the potential of different approaches to decipher multilingual documents, ranging from transformer-based models to open-language frameworks that can be tailored for contextual under- standing. Although positive steps have been taken to develop solutions, they are still being tested in controlled environments and are not yet mature. Fractal & the future digitization Our solution for document management has been developed through the collaboration of technical and business teams, focusing on a solution that produces results closer to the business’s specific needs — even if the output is not 100% accurate. This has allowed us to find the sweet spot between accuracy levels from a technical perspective and business validation rules regarding specific information that needs to be extracted. This framework also goes beyond just digitizing and storing information. The envisioned solution is about organizing documents and data and using them to support business operations. In other words, the end goal is more than simply providing structured data– we want to help organizations with their functions, and the applications for this are exciting, vast, and wide-reaching.

Module 2 : IVA OCR (Fractal Image Processing Engine)

The next step is to translate scanned copies of the physical documents into unstructured text. The Fractal IVA platform’s customized optical character recognition (OCR) algorithms offer superior extraction rates, making the digitization process more efficient and accurate. The output is a set of unstructured text containing all content from the original document, including text representations of non-text elements such as nested tables and embedded JPG and PNG files. Module 3 : dCrypt (NLP engine) The third module, dCrypt, is an NLP suite and accelera- tor for post-OCR data preparation that extracts relevant information from the unstructured text corpus. This module is the Core component that allows a high level of customization to address different types of documents and business requirements. Each module in Doc. Digit draws upon the previous module for input but operates independently, providing flexibility in component usage. Modules come with pre-trained and configured components that can be retrained or tweaked based on specific client require- ments. Module 4 : Validation engine The next step is to pass the extracted information through the validation engine, which checks it against simple predetermined rules based on business processes and document standards, such as a character limit for the invoice number. All documents with issues are returned to the submitter for resolution.

Module 5 : Reporting / Consumption Finally, data is summarized and prepared for

consumption through dashboards, integrated into other applications, or even sent directly to customers (e.g., a notification that their ticket has been actioned).

Optimizing ML Operations on Edge Real-Time Deep Learning Solutions on Edge Devices

Author: Abhishek Chopde, Senior Data Scientist, AI@Scale, Machine Vision, Conversational AI

Far-reaching applications of Machine Vision In 2023, MLVS is poised to impact fields such as sensor technology, 3D imaging, event-based vision, and model optimization, with applications ranging from absence/presence detection, barcoding, and surveillance to crowd control, defect detection, and logistics automation. Part of the cause of the rapid development of MLVS is the increasing amount of image and video data available. With the abundance of data, there is a greater need for faster and more efficient processing times. You need heavy computational resources to train an intelligent MLVS model that automatically and correctly detects people or classifies objects. However, only some organizations will have the required infrastructure for this computational need. To solve this problem, many organizations rely mainly on cloud computing to remotely process their data with the help of many third-party data centers. While this solves the computing problem, the weaknesses of cloud computing come to the fore with specific applications. There are some privacy concerns with transferring data to servers you have no control over, and any application that requires real-time processing at scale will run into latency problems. The solution to this problem is edge machine learning (edge ML). Deploying edge ML for real-time MLVS Edge ML can process data locally at the point of collection. It addresses security issues by storing sensitive user data in the cloud. It also makes real-time data processing possible, essential for technologies like autonomous vehicles, shipment sorting facilities, and critical patient monitoring systems.

Deep learning is a technology that provides a machine with the ability to process visual images for the tasks that it is performing. For many years, it has aided in raising product quality, accelerating production, and optimizing manufacturing and logistics. This tried-and-true technology is now combining with artificial intelligence to drive the shift to Industry 4.0.

MLVS (Machine Learning Vision Solution) becomes more challenging when the collected data needs to be processed in real-time and operated in specialized environments. Are global institutions and enterprises equipped to implement MLVS effectively in real time?

Machine Learning operations

The success of any AI operation is the effective deployment and tracking of models in production through machine learning operations or MLOps. It is an operational development framework for ML applications that involve the digital architecture of the entire life cycle of developing, testing, optimizing, deploying, and monitoring ML models. MLOps for Machine Vision This is similar to how the human brain’s occipital lobe provides a control center for visual functions. The hippocampus provides one for learning, memory, spatial recognition, and navigation functions. MLOps for machine vision guide global businesses in training their operational machines for visual data recognition, data processing, and storage quickly and efficiently.

Many edge devices we come across are as small as a flash drive. They are adaptable to integrations and are available as boards and development kits. They can, therefore, easily be plugged in and installed into any system without any intrusion on the functioning of the main hardware. Challenges to using Edge devices While edge devices offer many benefits compared to current methods, there are some challenges with using this technology. Edge intelligence risk Edge intelligence refers to analyzing data close to where it is collected. While this provides advantages in computing performance, physical data poses a significant challenge, especially where edge technology is continuously mobile. For example, a drone monitor- ing territory of an enemy state close to its border could malfunction or be shot down, allowing it to be collected by the enemy military, resulting in a major strategic and surveillance failure. Security Protecting an edge device from advanced adversarial attacks and hacking by malicious users requires very sophisticated encryption of the edge device, the data, and the model, which can be costly and time-consuming to develop and implement. Data scarcity As with all ML applications, high-quality data is neces- sary for high-quality training. So, another challenge in edge ML is the scarcity of real-time training data for the model. The data is collected and processed in cloud-based services with a vast central database. But ML applications use real-time data for training/updating models on edge devices, which is usually self-collected by the device, thus having limited storage and process- ing capabilities compared to the large servers used in cloud computing. Federated learning Federated learning solves the problem of gaps in gathered data by the edge device. It enables the development of a single model trained on several different data sets from various sources without the parties ever needing to exchange their critical data. However, federated learning is only suitable for group training since there are still some concerns about the privacy and security of the data. Therefore, it is not

exceptionally suited for ML operations that are highly clandestine or top-secret.

Data consistency Data consistency is another challenge, and it occurs primarily due to the inefficiency of the sensors of the edge device – noise in the background or environment gets superimposed on “useful” collected data. To overcome this issue, companies will have to use data augmentation to effectively teach the model how to filter out the noise. The breakthrough: Fractal’s forward-looking IVA models Fractal has developed deep learning-based image and video analysis (IVA) models deployed on edge devices, allowing data processing at the source of data capturing. We have already implemented IVA models for some of our clients. A cut above: IdeaForge drone surveillance IdeaForge is one of India’s leading manufacturers and was ranked 7th among the top dual-use drone manufacturers in the world by Drone Industry Insight. They are a key supplier of UAV technology to the Indian Armed Forces, focused on surveillance and mapping solutions. Fractal is IdeaForge’s strategic partner in developing drone technolo- gy, especially for difficult terrains and border patrol areas, and is helping to realize the Indian Army’s “Year of Transformation” goal for 2023.

Sandalwood plantation intruder/theft detection through Edge ML Fractal also worked with a client to provide night surveil- lance for a sandalwood plantation with a history of intruder-related issues due to the resource’s lucrative price point in India. We deployed a thermal-imaging camera mounted on a UAV, which can detect and collect emissivity and thermal sensitivity data and return the exact coordi- nates of intrusion to the security team in real time due to the rapid processing enabled by an edge ML system.

focusing on the following characteristics:

Precision object detection and identification Lightweight and faster models for real-time insights through quantization and sparsification Patented advance encryption pipeline for data security and privacy Ground activity insights through image segmentation

Improved UX through customer action identification

To enhance Fractal’s capability in platform-agnostic Video Surveillance, we have developed a Surveillance platform, “IVAHWKI” equipped with optimized and calibrated deep learning-based models capable of integrating with any environment on-prem, cloud, or edge/embedded devices.

Fractal has stayed at the forefront of the edge ML technology through advancements in its IVA solutions,

Figure 1: Fractal’s edge-optimized deep learning image and video analysis model

Mapping a future of innovation in Edge ML in Machine Vision

An MLVS ecosystem traditionally collects data on location but processes it remotely on the cloud. The challenges associated with cloud, such as latency issues, present a problem for applications that need real-time data processing and instant decision-making. Our competitors in the machine vision space use traditional setups such as cloud networking to deploy their solutions. But Fractal has leveraged edge ML to enhance our customers’ operational insights, so they achieve their critical and strategic goals effectively and efficiently. MLOps on edge devices are attempting to operationalize the AI or ML life cycle, which includes various activities, from data preparation – through model training and experiments – to testing. Fractal will continue to innovate edge ML for vision systems to empower our customers as the global scope of this technology expands to hundreds of use cases.

Upscale business performance through Enterprise Security and Machine Learning Governance Author: Sourabh Kumar, Principal Architect, AI@Scale, Machine Vision and Conversational AI

Search engines are now part of our everyday lives. Research shows that Google handles an incredible 40,000 searches per second – a staggering 3.5 billion daily! Businesses are developing advanced enterprise-scalable analytics products to harness this vast amount of data and derive actionable insights.

closing their accounts. Establish a comprehensive retention plan to delete obsolete information regularly and efficiently. Breaking the barrier of enterprise silos for better business impact and data security governance However, teams might face several challenges while developing efficient enterprise security and governance. Every enterprise has different units, verticals, products, and geographies. Each works in silos – running its models using its own set of tools, which may lead to an operational bottleneck and add more complexity to governance and security implementations. For instance, a CTO (Chief Technology Officer) and a CIO (Chief Information Officer) team may have distinct enterprise vision and priorities. Both teams may develop models to optimize the enterprise’s performance but in seclusion. Such situations often see the repetition of work and difficulty in integrating the models. Both security and governance could be at risk in such a scenario. An advanced solution becomes necessary if enterprise performance is to be optimized. And there must be systems in place that are leveraged across departments to make governing and monitoring easier. With advances in big data and analytics, enterprises are creating sophisticated data science models and applica- tions. When an enterprise has a smaller number of models, governance can be manual and straightforward. When the stakes increase with a rising number of models, automa- tion becomes essential to verify that the applications and models are functioning correctly to ensure data gover- nance, security, and safety. This is a challenging task. Hence, enterprises must break away from siloed thinking towards an integrated end-to-end view to ensure success- ful automation across many data science models.

Enterprise security and machine learning governance are thus vital for ensuring optimal performance and enabling stringent security protocols for analytics projects.

This concept involves embedding governance into artificial intelligence and how it can be embedded using the right technology, process, and people. It ensures a risk-free, sustainable, and scalable system. No matter how robust the technology developed, projects can only be stabilized and succeed with the correct enterprise security and governance mechanism. To effectively adopt Enterprise Security and Governance, enterprises must: Implement robust measures to protect and secure data. For example, enterprise teams should ensure encryption when data is at rest and while being transported and access controls on internal users and external parties with heightened scrutiny for those seeking access logs, all supported by up-to-date software systems. Develop a mechanism enabling individual users to request the erasure of personal information after

Compliance Governance: the blueprint For industries that require fail-proof compliance, a robust governance system is needed to mitigate risk and integrate audit, balance, and control the life cycle of machine learning development. The governance system should include assessments as gatekeepers for stage upgrades at each model development step.

Stepping up security standards in enterprise solution architecture Depending on their purpose, security standards vary across industries and their corresponding models. For instance, banks would have a highly regulated system. Insurance companies may focus on developing a system to address their process challenges. In regulated sectors such as life sciences, robust security is paramount. In contrast, lower controls may be more suitable for those sectors that emphasize commercial excellence or sales effectiveness, i.e., helping commercial sales effectiveness. Enterprise security and Machine learning governance complexity varies according to industry and Use case. For example, in the BFSI industry, higher-level control must always be upheld regarding credit risk scoring systems while allowing quick deployment of secure solutions with minimal effort. If we look at the consumer-packaged goods industry, it relies heavily on machine learning models to generate successful results. Yet, these models could be severely compromised if effective security measures are not

implemented. Organizations often focus on embedding security measures at every step in their models, which might cause complexities and delays in deployment. While enterprise financial data may call for maximum security, it is equally vital for other verticals to ensure data governance in their models. Enterprises should also avoid excessive safeguards to ensure smooth deployment progress. It is recommended that the teams ensure that only minimum requirements are met when constructing effective yet secure models. Tip-off for the future: tighten the grip of security in enterprise governance models For enterprise security and machine learning governance to function optimally, teams must transition from siloed working to collaboration. As enterprises transition into Industry 4.0, they can maximize the potential of their machine-learning governance and enterprise security by embedding robust security at every step of the models.

Unlocking Responsible AI for ESG Ways AI Can Drive Sustainability and Social Impact

Author: Akbar Mohammed, Principal Architect, Strategic Center

Corporate social responsibility (CSR) is gaining significance in shaping enterprises’ sociocultural, environmental, political, and ethical structure and brand identity. Hence, businesses seek to integrate sustainable practices into their operations for long-term, well-rounded growth. One way this is being achieved is through artificial intelligence (AI). This powerful tool helps enterprises improve their environmental, social, and governance (ESG) performance, enabling them to monitor and manage their impact on the environment, society, and stakeholders more effectively. Enterprises worldwide are already taking internal measures to address their environmental impact, such as implementing separate bins for food and plastic and factoring in employee flight travel to calculate their carbon footprint. Some industry leaders have gone even further by committing to net zero carbon emissions. Other companies are taking positive steps toward aligning their ESG initiatives with the UN’s Sustainable Development Goals (SDGs).

also complex. Conflicting global policies, varying levels of integration, and the challenges of balancing short-term financial interests with long-term environmental and governance goals indicate that enterprises are still near the beginning of their journey toward achieving ESG compliance. Many existing AI applications can be reconfigured to identify, monitor, and produce solutions for these challenges. Yet, despite its potential, AI needs to be more leveraged and remains untapped in measuring the impact of sustainability efforts and developing new approaches for more environmentally sustainable practices. Environmental Impact The impact of AI on the environmental facet of ESG has significant potential. AI technology is already being used to track greenhouse gas emissions, resource consumption, and weather changes. This data is then analyzed to provide insights that can help conserve natural resources and assist organizations in adopting more environmentally friendly practices. In the agricultural industry, for example, AI is helping inform decisions regarding adopting new approaches, such as regenerative agricultural practices, to promote environmen- tal sustainability. This involves farmers using the land to a point before shifting to a more sustainable practice while regenerating the previously exploited land for future use. Social Impact AI also plays a significant role in measuring and improving employee welfare in the workplace. Fractal implements AI to measure employee well-being through an in-house AI conversational chatbot that keeps a tab on the psychological well-being of employees. Questions are formulated, and a qualified team of specialists interprets results to ensure the human element remains the driving force behind the conclusions. Automated surveys are then released to specific employees based on their work hours, allowing for a more comprehensive analysis.

The application of artificial intelligence in environmental, social, and corporate governance represents a formidable force for positive change.

While large enterprises have started incorporating data governance and structures to measure their impact, the challenges facing ESG are unfortunately not only many but

AI can also be harnessed to measure corruption through various practices, such as monitoring how companies work with vendors, countries, and communities. Companies can even assess their compliance with human rights and corporate governance standards. Both financial and non-financial measures can be tracked to determine the integrity of a company’s governance structure. Data and data governance AI can help synthesize data from multiple sources and provide decision-making tools to make it easier to manage sustainability problems such as transparency, assessing ESG compliance, optimizing operations, capturing sensor data, and performing scenario planning simulations. Collecting new and more consistent and reliable data can also be automated, which helps to simulate scenarios and automate standardization, creating more efficiency in ESG reporting and analysis.

In addition to monitoring an organization’s internal staff assets, AI can also be deployed to measure and uphold social sustainability practices, such as promoting a safe and fair working environment for employees. Think, for example, of a factory: are working conditions safe? Are female workers fairly compensated for their work compared to their male counterparts? Are the factory’s vendors complying with regulations? AI can be leveraged to answer these questions by conducting large-scale pattern recognition and comparing the results with predetermined metrics. Impact on Governance The impact of AI on governance is critical, as it can enhance transparency and accountability to stakeholders and the public.

There are three broad areas where data can be leveraged with AI.

Descriptive analytics

Predictive analytics

Image recognition

Summarizing existing data to understand where a company stands in terms of ESG.

Predicting future energy consumption, carbon emissions, and alternate sources that can be utilized.

Observing, classifying, and annotating images to track resource depletion and plan for future resource utilization.

But, with big data comes great responsibility, and data governance is just as critical in ESG operations as anywhere else. It must be reliable, consistent, high quality, and compliant with existing regulations. Therefore, robust data governance is essential for organizations to utilize ESG policies and make informed decisions efficiently.

Challenges in ESG AI At the intersection of AI, ESG, and technology lies a complex web of challenges that must be addressed to ensure responsible and sustainable decision-making. These include the following: The complexity of measuring and reporting sustain- ability and ethical practices. Every company has a unique set of operations, and there is no one standard to follow, making it difficult to prioritize and compare. A lack of standardization across different ESG metrics, as each global region has its own set of regulations and policies.

AI is just a tool – to harness its power, we must combine behavioral science, design, and engineering to create a diverse, responsible, and sustainable solution for clients.

Accountability, as it is not always clear who should be held responsible in the short term.

Moving toward a sustainable future

However, the biggest challenge facing AI in ESG is the environmental footprint that technology creates. For example, developing large language models consumes significant energy and emits substantial carbon emissions. While large tech companies have attempted to implement carbon-neutral policies, there are miles to go to achieve a balance. Implementing AI in your ESG initiative: Start here Regarding ESG practices and policies, every organization is at a different stage of development. The first step for those without a framework is forming an ESG strategy to guide decision-making. The next challenge is measuring the organization’s ESG framework and implementing programs that will make a meaningful impact. Finally, there is the execution phase — capturing data, building infrastructure, and focusing on analytics and personnel to support ESG initiatives. Specific solutions will vary depending on an organization’s ESG journey. But the goal remains the same: to create a sustainable and responsible decision-making approach that benefits the organization and the wider world.

AI technology in ESG has significant potential for growth and development, which can lead humanity to a place of sustainability and positive social impact. For instance:

Large language models can be used to understand public concerns and provide potential remedies.

Machine vision can be applied to analyze satellite imagery, photos, and videos to aid in environmental impact measurement and compliance. AI-powered smart building and energy management systems can help reduce energy consumption and minimize waste. Predictive analytics can be deployed to identify patterns and trends in historical data to help anticipate future ESG risks and opportunities. Since there is no standard guideline for ESG solutions, companies can explore different approaches to solve the problems we face. However, the journey toward sustain- ability is a global issue, and organizations need help to embark successfully on it. Policymakers and other stakeholders must actively engage in promoting positive change. While the impact of technology on ESG issues is yet to be seen, the positive momentum from organizations and governments’ response to the climate crisis and other ESG issues is promising and points towards a better, more sustainable future.

Enable better decisions with Fractal 500® companies. Fractal's vision is to power every human decision in the enterprise, and bring AI, engineering, and design to help the world's most admired companies. Fractal's businesses include Crux Intelligence (AI driven business intelligence), Eugenie.ai (AI for sustainability), Asper.ai (AI for revenue growth management) and Senseforth.ai (conversational AI for sales and customer service). Fractal incubated Qure.ai, a leading player in healthcare AI for detecting Tuberculosis and Lung cancer. Fractal currently has 4000+ employees across 16 global locations, including the United States, UK, Ukraine, India, Singapore, and Australia. Fractal has been recognized as 'Great Workplace' and 'India's Best Workplaces for Women' in the top 100 (large) category by The Great Place to Work® Institute; featured as a leader in Customer Analytics Service Providers Wave™ 2021, Computer Vision Consultancies Wave™ 2020 & Specialized Insights Service Providers Wave™ 2020 by Forrester Research Inc., a leader in Analytics & AI Services Specialists Peak Matrix 2022 by Everest Group and recognized as an 'Honorable Vendor' in 2022 Magic Quadrant™ for data & analytics by Gartner Inc. For more information, visit fractal.ai

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