AI Evaluation Should Start From Decisions, Not Test Cases
Enterprise AI evaluation should test whether a decision can responsibly rely on an output, not only whether a model passes generic test cases.
Topic
Enterprise AI Systems
How enterprise AI systems are designed, evaluated and governed for reliable use in real workflows.
Building Enterprise AI Systems: Beyond the Model
Enterprise AI becomes reliable when context, evaluation, permissions, human judgement, workflow and learning are designed as one system around the model.
Human Review Is Not a Control Unless Accountability Is Designed
Human review becomes a real AI control only when the reviewer’s judgement, evidence, override authority and accountability are explicitly designed.
When AI Can Act, Permission Design Becomes Architecture
As enterprise AI moves from answering to acting, permission design becomes part of system architecture, defining what agents may observe, recommend, execute, escalate and stop.
Agentic Workflows Need Escalation Logic
Enterprise-grade agentic workflows need explicit conditions to recommend, execute, escalate or stop.
Diagnosing RAG Failures
RAG failures require diagnosis across source governance, ingestion, retrieval, generation, evaluation and escalation.
Why Evaluation Matters More Than Model Selection
Enterprise AI evaluation must test whether a system is reliable enough for its specific decision, evidence standard and failure modes.
AI Makes Weak Decision Architecture More Visible
AI often exposes weak decision architecture by making unclear ownership, evidence and workflow boundaries harder to ignore.
When Human Overrides Become Organisational Memory
Capturing the reasoning behind human overrides can turn exceptions into reusable organisational learning for people and AI.
The Hidden Cost of Poor Context in Enterprise AI
Enterprise AI underperforms when incomplete context and weak workflow design make plausible outputs unusable for real decisions.