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Standard data labeling expertise is not enough

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AI solutions combined with technological and human ingenuity lead to faster, more accurate outcomes

The rate at which organizations are developing AI solutions is growing exponentially. So is the need for high-quality training data. Gartner research has found that there’s an urgency of leveraging AI for business transformation, but 50% of IT leaders struggle to move their AI projects past proof of concept – one reason being a lack of data necessary to train AI solutions.

This paper discusses the challenges of data labeling, and how organizations need to pursue the best – rather than just the fastest – data-labeling service that takes into consideration quality, flexibility, scalability, and cost of data labeling.

About authors

Marek A. Sowa Head of Generative Technologies CoE Capgemini's Business Services

Marek Sowa

Head of Generative Technologies CoE, Capgemini’s Business Services
Marek Sowa leads the Generative Technologies Center of Excellence for Capgemini’s Business Services. He advises clients on intelligent automation strategy and digital transformation, backed by deep expertise in GBS, AI, and innovation – helping him deliver tangible business outcomes to them.
Vijay Bansal Director - Global Head - Data Labeling Services, Capgemini Business Services

Vijay Bansal

Director – Global Head – Data Labeling Services, Capgemini Business Services
Vijay has extensive experience working in map production, geo-spatial data production, management, data labeling and annotation, and validation roles. In these positions, he aids machine learning and technical support initiatives for Sales teams, coordinates between clients, and leads project teams in a back-office capacity.

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