Enterprise AI platforms are at the forefront of digital transformation, enabling businesses to innovate, compete, and operate efficiently. These platforms empower organizations to streamline processes, enhance decision-making, and accelerate their AI adoption journeys by unifying data governance, model training, deployment, and monitoring in a single ecosystem. Gen AI further amplifies this by enabling dynamic automation, content generation, and personalized customer interactions. In this report, we analyze enterprise AI platforms, including their architecture, core components, and technology advances. The report highlights key trends shaping these platforms’ adoption, including the rise of Small Language Models (SLMs) tailored to specific business needs, the growing importance of ethical AI governance, and the integration of real-time decision-making capabilities. It also examines enterprise challenges in scaling AI adoption. This research offers valuable insights for businesses harnessing AI platforms for sustainable growth and competitive differentiation. Scope Industry: cross-industry Geography: global Contents In this report, we examine: Introduction to enterprise AI platforms: overview of architecture, capabilities, and definitions Technology advances: insights into gen AI, agentic AI, and governance tools shaping platform capabilities Market trends and drivers: the role of SLMs, real-time analytics, and scalable AI solutions in adoption Ecosystem evaluation: leading players across big tech, data management heritage, native AI, and specialized AI providers Challenges and strategic solutions: addressing integration, data complexity, and cost management Membership(s) Artificial Intelligence (AI) Sourcing and Vendor Management

This report underscores synthetic data’s vital role in addressing data challenges during AI initiatives. It highlights the significance of leveraging synthetic data to train and optimize AI models effectively. Additionally, it explores best practices for using synthetic data and its effectiveness in mitigating privacy concerns and biases commonly associated with using real-world data The report also explores key industry trends, use cases and adoption patterns related to synthetic data. It examines different buyers offering synthetic data solutions, providing insights into the diverse provider landscape to assist technology buyers in making informed decisions. Furthermore, the report will educate professionals developing AI/ML solutions on the benefits of synthetic data. It offers a comprehensive understanding of synthetic data’s working principles, highlights key nuances, and equips professionals with the knowledge necessary to leverage synthetic data effectively, thereby enhancing their AI/ML projects. Contents: In this report, we examine: An overview of synthetic data Synthetic data’s role in the AI development cycle Key applications and adoption of synthetic data across different industries The synthetic data supplier ecosystem Scope: Industry: cross-industry Geography: global Membership(s) Artificial Intelligence (AI) Outsourcing Excellence

Driven by the exponential growth of data and the impact of the pandemic, enterprises have rapidly adopted Artificial Intelligence (AI) as a strategic tool to gain a competitive edge and enhance their business models. Recognizing its potential, they seek to leverage AI to reduce dependency on human workforce and unlock new revenue streams while cutting costs. As a result, enterprises are striving to develop improved AI tools and technologies. However, implementing AI has its own set of challenges such as availability of high-quality curated data and responsible AI implementation. In this report, we discuss the importance of high-quality curated data in the success of enterprises’ AI initiatives. We explore different aspects of preparing high-quality data such as data annotation, using synthetic data when real data is insufficient, incorporating a human-in-the-loop approach, and ensuring data inclusivity and mitigation of biases. Additionally, the report examines emerging trends in AI data services that enterprise should consider before making implementation decisions. Scope  All industries and geographies Contents In this report, we examine: AI / Machine Learning (ML) life cycle Features and benefits of data annotation and labeling Importance of AI-assisted data annotation and synthetic data Human workforces’ role in the AI life cycle Data annotation and labeling services ecosystem Emerging trends in AI data services Membership(s) Artificial Intelligence (AI) Sourcing and Vendor Management

Today, enterprises and providers are struggling to acquire skilled talent for their Data, Analytics, and AI (DAAI) needs. The acute talent shortage has led to a huge demand-supply gap, causing significant changes to enterprises’ recruitment and sourcing strategies along with a substantial increase in talent costs. In this report, we assess the current talent scenario in the DAAI space, how it fares geographically, and what is expected of it in the near future. We also analyze the skills associated with different value chain segments in the DAAI services market, explore some of the key talent themes and measures adopted by enterprises to tackle talent-related challenges, and describe what an ideal DAAI operating model looks like. Scope All industries and geographies Contents In this report, we: Assess the DAAI talent demand-supply gap Examine the key emerging talent themes Decode the ideal DAAI operating model Membership(s) Data & Analytics Locations Insider™ Sourcing and Vendor Management