The global spend on AI is growing as enterprises across various sectors increasingly leverage its transformative potential. With the emergence of generative AI / large language models, enterprises worldwide are adopting AI at an accelerating pace to unlock new revenue streams, streamline operational costs, enhance user experiences, and establish significant differentiation within their respective industries. However, the foundation of AI development hinges on large training datasets, often tailored to the unique requirements of each business. Data Annotation and Labeling (DAL) services play a vital role in curating these high-quality training datasets essential for AI development. However, maintaining in-house DAL capabilities proves to be costly, time-consuming, and labor-intensive for enterprises. As a result, businesses are turning to external DAL service providers to implement DAL solutions. In their pursuit, enterprises seek partners capable of expediting their time-to-market by executing annotation projects at scale and speed without compromising data quality. They prioritize providers that prioritize building strong relationships, cost-effectiveness, agility, and a steadfast commitment to delivering tangible business impact and RoI at every stage of their transformation journey. Providers with a trained workforce and advanced annotation platform capabilities can efficiently guide these enterprises through the DAL landscape. This compendium provides detailed snapshots of 20 DAL solutions providers featured on Everest Group’s DAL solutions for AI/ML PEAK Matrix® Assessment 2024. Each profile offers a comprehensive overview of the provider’s operational overview, delivery locations, solutions offered, investments, and market success. Scope All industries and geographies The assessment is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with DAL service providers, client reference checks, and an ongoing analysis of the DAL services market Contents In this report, we examine: DAL service providers landscape DAL PEAK Matrix® characteristics Providers’ leadership, presence across geographies and industries, global FTE and revenue estimates, buyer size, and overall practice structures DAL delivery locations and intellectual property overview, along with an analysis of flagship IP and key partnerships across the DAL value chain Provider investments in DAL across talent, infrastructure (centers of excellence / labs), acquisitions, research, academic partnerships, and solutions Recent case studies and projects won, with a detailed description of solutions provided Providers’ key strengths and limitations Enterprise sourcing considerations Membership(s) Artificial Intelligence (AI) Outsourcing Excellence

The global spend on AI is soaring as enterprises across diverse sectors increasingly tap into its transformative potential. With the emergence of generative AI / Large Language Models (LLMs), enterprises worldwide are swiftly adopting AI to unlock new revenue streams, reduce operating costs, enhance user experiences, and gain significant industry differentiation. However, at the core of AI development lies the need for large training datasets, a challenge met by Data Annotation and Labeling (DAL) services. However, managing DAL capabilities in-house appears to be costly, time-intensive, and resource-draining for enterprises. As a result, businesses are outsourcing DAL solutions to external providers. In their quest, enterprises seek partners capable of accelerating their time-to-market with annotation projects delivered at scale and speed without compromising data quality. They prioritize providers that emphasize relationship-building, cost-effectiveness, agility, and a steadfast commitment to deliver tangible business impact and RoI throughout their transformation journey. Equipped with trained workers and robust annotation platforms, these providers efficiently guide enterprises through the DAL landscape. In this report, we assess 19 providers featured in the Data Annotation and Labeling (DAL) solutions for AI/ML PEAK Matrix® Assessment 2024. Each profile offers a comprehensive overview of the provider’s strengths and limitations, enabling enterprises to make informed decisions as they navigate the evolving DAL landscape. Scope All industries and geographies The assessment is based on Everest Group’s annual RFI process for the calendar year 2024, interactions with DAL solutions providers, client reference checks, and an ongoing analysis of the DAL services market Contents In this report, we examine: DAL solutions providers’ landscape DAL PEAK Matrix® characteristics Enterprise sourcing considerations Membership(s) Artificial Intelligence (AI) Sourcing and Vendor Management

Data is universally accepted as a key business asset across enterprises, irrespective of size, scale, industry, and geography. Today, organizations are recognizing the value that data investments can offer and are, therefore, more open to sourcing/buying data from external data providers. As enterprises increasingly operate in networks and ecosystems, the need to look beyond internal data will only rise. In fact, industries are already examining different ways to use external data. External data maximizes business value by improving insights and enables enterprise stakeholders to make better data-driven decisions related to evolving market dynamics, especially in instances where historical data has limited use. To capture the opportunities related to external data adoption, the supply ecosystem – both data providers and tech enablers – has expanded in recent times. In this report, we examine the current state of external data adoption, the booming supply-side ecosystem, and enterprises’ need to reimagine data sourcing and consumption. Scope: All industries and geographies Contents: This report examines: The role of external data in enterprise analytics and penetration across industries Key drivers of external data adoption The booming supply side and the rich ecosystem of external data providers Challenges of external data adoption Enterprise considerations to maximize value from external data investments Membership(s) Data & Analytics Sourcing and Vendor Management

The advent of Robotic Process Automation (RPA) helped enterprises automate documents with structured data sources; however, content-centric documents – accounting for almost 80-85% of enterprise document load – cannot be automated using conventional rules-based solutions. Consequently, manual processing of these documents often leads to issues such as long turn around time, high cost of operations, high incidence of errors, and difficulty in automating heterogenous data. Driven by these factors, enterprises, today, are looking for solutions that incorporate elements of Artificial Intelligence (AI), so as to process documents with unstructured data sources. The AI-based solutions, often called Intelligent Automation (IA) solutions, possess capabilities such as computer vision, machine learning, and NLP that can be integrated with RPA and BPM workflow to provide an end-to-end automation experience. Technological advancements, such as transfer learning, are further easing the barriers or inhibitions that enterprises have while adopting any intelligent automation solution. This viewpoint covers the entire document processing conundrum in detail, with details on applicability of AI to provide an end-to-end automation experience. The sections covered in the viewpoint include: Limitations of RPA and OCR / template-based solutions in processing content-centric data The role of AI in document processing How AI augments RPA and BPM to provide an end-to-end process automation experience Key technologies powering AI capabilities, including transfer learning A case study explaining how an enterprise utilized an intelligent automation solution to automate document processing Membership(s) Service Optimization Technologies (SOT)