Starburst: Chewing through data access is key to AI adoption

""It may seem that nothing can, but there is one substantial problem that is worth discussing. This problem is central to AI adoption as we know it, impacting all forms of AI, regardless of model. Without solving it, AI technology cannot increase productivity. It cannot drive organisational change. It cannot create a virtuous cycle. The key ingredient in the AI revolution comes in the form of context, specifically contextual data. This might sound obvious, but the nuances of AI's need for context have far-reaching implications,""

"At a time when world governments are currently offering free AI training in a direct attempt to drive AI adoption in the workplace (we've seen the reports, UK), discussion surrounding how, when and where AI services are being brought to bear inside modern workflows is everywhere. Evan Smith, technical content manager and instructional designer at data query and analytics company Starburst, thinks that the AI adoption race is certainly here, but to succeed, it needs to overcome one key bottleneck - data access."

Widespread efforts to accelerate workplace AI adoption meet a central bottleneck: access to contextual data. Contextual data provides the situational, current, and governed information that AI models require to produce useful, reliable outputs. Large language models are trained on broad, generic datasets and therefore lack organisation-specific context, which limits their ability to drive productivity and organisational change. Solving the bottleneck requires secure, governed access to internal data, integrated observability, metadata and latency management, and privacy-preserving practices. Human-in-the-loop processes and data engineering investments are necessary to create the virtuous feedback loop between technology and organisational practices.

Read at Techzine Global