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21/08/2026 4:51 pm
TL;DR: AI stakeholder trust comes from unifying eval scores, trace views, and production observability so design, leadership, and go-to-market can see the same evidence, according to Braintrust. That matters because AI quality is judged across latency, cost, regressions, and user outcomes, not in isolated dashboards, and governance gets harder when those signals stay fragmented.
NHIMG editorial — based on content published by Braintrust: How to earn stakeholder trust with evals and observability
Questions worth separating out
Q: How should teams make AI evals and observability useful for leadership reviews?
A: Use a small set of shared metrics that answer whether the feature works, what it costs, and whether quality is changing.
Q: Why does fragmented AI visibility create governance problems?
A: Fragmented visibility creates governance problems because teams cannot reliably determine whether a discovered tool is authorised, risky, or tied to sensitive data.
Q: How can security teams govern natural-language access to production data?
A: Treat it like any other privileged data access path.
Practitioner guidance
- Create a single evidence layer for AI reviews Unify eval scores, trace data, and production metrics so leadership, product, and engineering use the same source of truth for AI quality decisions.
- Design trace views for non-technical oversight Build simplified trace interfaces that show the decision path, outcome, and key metrics in language reviewers can understand without reading raw spans.
- Constrain self-service production queries Limit who can query production logs, require audited access, and define which datasets ad hoc tools may reach before they become part of normal review.
What's in the full article
Braintrust's full blog post covers the operational detail this post intentionally leaves for the source:
- How to configure dashboard charts for time series, top lists, and big-number views across evals, latency, cost, and token usage.
- How to use custom trace views to transform raw spans into stakeholder-friendly artefacts for product, leadership, and engineering reviews.
- How Loop translates natural-language questions into SQL over production data and promotes one-off answers into reusable charts.
- How to build segmentation by user segment, task type, and deploy version so regressions are visible in review meetings.
👉 Read Braintrust's post on earning stakeholder trust with evals and observability →
AI evals and observability: what does stakeholder trust actually require?
Explore further
21/08/2026 5:09 pm
Shared evidence is now a governance control, not a reporting convenience. AI programmes fail stakeholder alignment when evals, traces, and operational metrics remain separate artefacts. That fragmentation creates versioned truth, where product, engineering, and leadership each see a different reality. In practice, the control failure is not missing telemetry, but missing synthesis. Teams should treat the evidence layer as part of AI governance and review it alongside decision rights.
A question worth separating out:
Q: What should organisations do when AI traces are too complex for non-engineers to review?
A: Create simplified trace views that mirror the product flow and expose the decision, the outcome, and the quality signal in plain language. If stakeholders cannot understand one run, they cannot challenge systemic issues, so the review interface itself becomes part of governance.
👉 Read our full editorial: AI evals and observability need shared evidence to build trust
