Precisely has published the results of new global research. The Observability for AI Innovation study (1), created by Business Application Research Centre (BARC) and sponsored by Precisely, surveyed a diverse set of over 250 data and AI stakeholders worldwide, uncovering critical insights into how organisations leverage observability to drive trusted AI and analytics outcomes.
As organisations scale their use of AI, observability has become a foundational requirement for ensuring transparency, accountability, and performance across data ecosystems. The research reveals that while many companies have taken significant steps to formalise observability programmes, progress varies across disciplines. Differences in programme maturity, measurement practices, and regional adoption trends all point to areas that still require attention.
AI observability is gaining traction, but gaps remain
76 per cent of organisations have formalised, implemented, or optimised programmes for both data quality and data pipeline observability (2), demonstrating a strong commitment to building trusted AI foundations. While AI/ML model observability is close behind at 70 per cent, the responses indicate a broader range of maturity levels, with many organisations still operating with inconsistent or underdeveloped programmes.
When it comes to measuring success, 68 per cent of respondents use qualitative and/or quantitative metrics to assess their observability efforts, however, the remaining organisations rely on ad-hoc or no measurement at all, posing a significant risk. Without clearly defined metrics and alignment with enterprise-wide governance frameworks, organisations risk falling short of their AI objectives.
Unstructured data is emerging as a key focus
Organisations are extending their observability programmes beyond structured tables to include semi-structured data (such as JSON or log files) and unstructured data (such as text, images, video, and sound). According to the study, 62 per cent of organisations are exploring the use of semi-structured data, with 28 per cent already actively using it, while 60 per cent are currently evaluating unstructured documents. These strong adoption trends signal growing recognition of the importance of diverse data types – particularly as advanced use cases like predictive machine learning and Generative AI depend on them. Observing this data requires different observability techniques than tables, including the careful appending and tracking of object metadata.
North America leads in AI and observability maturity
Compared to Europe, North American firms report significantly higher AI adoption rates and observability maturity. An average of 88 per cent of North American organisations have formalised observability programmes across disciplines, compared to just 47 per cent in Europe. North American firms also place greater emphasis on regulatory compliance and data privacy, despite the absence of federal AI regulations comparable to the EU’s AI Act. In addition, North Americans place a higher priority than Europeans on model accuracy and twice as many of them have formal observability measurements in place.
“As AI and the emergence of agentic use cases raise the risks and rewards of analytics, data teams are solidifying their observability programmes to strengthen data governance and quality,” said Cameron Ogden, senior vice president – product management at Precisely. “The research reinforces that observability is not simply a nice-to-have but is a foundational capability for ensuring the integrity of enterprise data – particularly when it comes to fuelling AI models for trustworthy and scalable outcomes.”
