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
Generative ArtificialIntelligence (GAI) technology has existed for the past five decades. However, recent developments in AI models, faster computing power, and the availability of high-quality training data are currently redefining the technology…
