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Guide for data leaders on scaling gen AI technology. #AIleadership

A data leader’s technical guide to scaling gen AI

Data and AI leaders have been exploring generative AI (gen AI) use cases for over a year, uncovering both the significant value and challenges in scaling these solutions. Managing data remains a major hurdle, with issues ranging from data quality to governance and training data. To overcome these challenges, organizations can take three key actions. Firstly, they can enhance data quality and readiness for gen AI use cases. Secondly, they can leverage gen AI to build better data products with modernized data platforms. Lastly, they can focus on key data management considerations to enable reuse and accelerate the development of data solutions.

Improving data quality is crucial, as feeding poor data into gen AI models can lead to costly outcomes and breaches of trust. Organizations can obtain better and more accurate source data from complex data types by utilizing tools like knowledge graphs to capture relationships between entities. They can also use multimodal models to interpret complex document formats accurately.

Using gen AI to accelerate the building of reusable data products can help companies develop data products faster and more efficiently. By shifting to an end-to-end approach in building data pipelines and enhancing consistency with better orchestration and data management, organizations can achieve time savings and scalability for specific use cases.

Scaling gen AI with security and coding standards is essential to mitigate risks and enforce standards. Protecting data at each step and integrating coding best practices into gen AI outputs can help ensure data security and quality. By addressing these technical challenges and focusing on orchestration capabilities, automation, and usability, data and AI leaders can help their organizations transition from gen AI pilots to scalable solutions that deliver real value.

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