2025人工智能创新中的可观测性研究报告：应用趋势、关键需求与最佳实践_34页_1mb

Observability for AI Innovation
Adoption Trends, Requirements and Best Practices

This report highlights the growing importance of observability in AI innovation, emphasizing the need for transparent, trustworthy data inputs and outputs. Key findings include:

1. Adoption Trends

   • Over 68% of organizations have formalized or optimized observability programs for data quality, pipelines, and AI/ML models, reflecting a shift toward structured governance.
   • North American companies show higher maturity in AI adoption and observability practices compared to European counterparts, with 88% (vs. 47%) implementing formal programs. They prioritize regulatory compliance, model accuracy, and privacy, driven by stricter EU regulations like the AI Act.
   • Structured tables remain the primary focus for observability, but attention is expanding to semi-structured (e.g., JSON, log files) and unstructured data (text, images, videos). Nearly one-third of companies now observe unstructured data in production, fueled by GenAI’s demand for diverse inputs.

2. Challenges

   • Primary obstacles include training/skills gaps (35%), manual processes (25%), and organizational confusion (25%). Governance and compliance remain critical, yet many rely on ad hoc methods, which risks transparency and accountability.
   • Cross-functional collaboration is insufficient, with only 18% of companies formalizing consistent collaboration policies. Data scientists dominate observability roles but are not always aligned with business stakeholders.

3. New Requirements

   • GenAI adoption introduces demands for observing unstructured data (40% of organizations), vector databases (33%), and metadata management (33%).
   • Trust in AI outputs remains low (59% of respondents), with concerns over model drift, bias, and data integrity. Transparency across input, processing, and output is essential for credible AI outcomes.

4. Best Practices

   • Organizations should formalize observability programs, integrate governance policies, and define clear success metrics (e.g., KPIs for data quality and model accuracy).
   • Enhancing cross-team collaboration and investing in modern tools (e.g., dedicated observability platforms) can address gaps in transparency and automation.
   • Expanding capabilities for unstructured data, including metadata labeling and real-time monitoring, is critical for GenAI success.

5. Regional Differences

   • North America leads in AI maturity, with stronger focus on streaming data, real-time analytics, and advanced observability techniques.
   • European companies lag in adopting GenAI (52% adoption rate vs. 73% in North America) and face challenges in balancing compliance with innovation.

6. Recommendations

   • Formalize observability programs with structured governance frameworks.
   • Strengthen technical and business collaboration to ensure cross-functional oversight.
   • Invest in training to close skills gaps and leverage external expertise for AI-managed services.
   • Modernize infrastructure to support scalable, automated observability tools for complex environments.
   • Prioritize transparency and accountability in AI pipelines to build stakeholder trust.

The report underlines that while data fundamentals are critical, achieving observability requires addressing people, processes, and technology together. North America’s proactive approach contrasts with Europe’s reliance on traditional methods, urging stronger governance and innovation alignment. As AI evolves, observability will remain a cornerstone for responsible and effective deployment.