BARC Research Study: Observability for AI Innovation

Adoption Trends, Requirements and Best Practices

As AI raises the risks and rewards of analytics, data teams are solidifying their observability programs to strengthen data governance. Engineers, managers and executives now contribute to formal programs for data, pipeline and model observability. While much work remains, cross-functional teams seek to improve privacy, trust, transparency, regulatory compliance and model accuracy.

This research study examines three distinct observability disciplines: data quality, data pipeline and AI/ML model. In each case observability refers to the measurement, monitoring and optimization of these elements.  Most organizations now have formalized programs for data, pipeline and model observability. Organizations prioritize
privacy, auditability, and compliance in their effort to foster Responsible AI. Challenges do persist, of course, due to shortages of skills, collaboration and process automation that hinder full adoption of observability.

Highlights in the research report include:

• AI maturity: a matter of observability and governance
• Observability means more than structured data
• North America leads Europe
• Lessons from GenAI adoption

Download the full BARC research study today.