Despite these challenges, the article suggests that the rise of enterprise intelligence continues, with data fabrics emerging as a strategy to support enterprise AI requirements. It emphasizes the importance of common data platforms, cloud data management applications, and data engineering skills in creating a robust data infrastructure for AI. The article concludes by stating that without these elements, high project failure rates are likely, and the rise of enterprise intelligence requires new infrastructure solutions that deliver AI safety and security.
Key takeaways:
- Generative AI (GenAI) has the potential to significantly improve productivity and job performance in the enterprise, with applications such as code generation and retrieval-augmented generation.
- Despite the potential benefits, many companies have struggled to implement GenAI solutions due to challenges such as data security, compliance, and the need for a robust data infrastructure.
- Data fabrics and common data platforms are emerging strategies to support the requirements of enterprise AI, providing essential services for data collection, metadata management, data governance and data discovery.
- Establishing an enterprise AI program office and developing data engineering skills are critical steps towards implementing GenAI in the enterprise, along with the use of third-generation data platforms and powerful data pipelines.