What Production AI Teams Can Learn From Site Reliability Engineering
Service objectives, error budgets, runbooks, staged rollouts, and blameless reviews offer AI teams a disciplined way to manage uncertain model behavior in production.
Artificial Intelligence
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Why AI Summaries Lose Crucial Details—and How to Test for It
Summaries often preserve the theme while dropping exceptions, quantities, and obligations; a targeted evaluation method can reveal omissions before users rely on them.
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The AI Adoption Curve Is Splitting: What Power Users Do Differently
New usage patterns suggest a widening gap between casual users and employees who redesign entire workflows, raising urgent questions about training, incentives, and inequality.
PN
AI for Manufacturing Quality: Lessons From the Factory Floor
A factory deployment shows how vision models, sensor data, operator feedback, and careful thresholds can catch defects while avoiding costly false alarms and work stoppages.
AO
The Practical Guide to Versioning Prompts, Models, and Evaluations
Treat prompts, model settings, tools, and test sets as linked production artifacts so every release is reproducible, reviewable, and easy to roll back when quality shifts.
LB
The Shadow AI Audit: Finding Unsanctioned Tools Before Data Escapes
Discover how to map unofficial AI use through surveys, network signals, expense data, and interviews—then replace blanket bans with safer, approved alternatives.
DM
How to Use AI for Root-Cause Analysis Without Chasing False Leads
Combine logs, metrics, traces, and change records with constrained AI analysis to generate testable hypotheses while keeping engineers in control of incident diagnosis.
AO
AI Chips Beyond GPUs: What the New Hardware Race Means for Buyers
As custom accelerators challenge GPU dominance, enterprise buyers must weigh workload fit, software support, availability, energy use, and switching costs before committing.
LB
Why Your AI Roadmap Needs Decommissioning Plans From Day One
Models, vendors, and workflows age quickly; defining retirement triggers, migration paths, data disposal, and user communication early prevents obsolete AI from lingering.
DM
The AI Pilot Graveyard: Why Promising Experiments Never Reach Production
A forensic look at the technical, organizational, and financial gaps that strand successful AI pilots before they become dependable production systems.
AO
AI agents: Automation blueprint for Modern Teams
AI agents: Automation blueprint for Modern Teams explains the practical decisions, risks, metrics and rollout steps operators need to move from experiment to dependable production value.
AO
RAG Is Not a Database: The Architecture Mistakes Teams Keep Making
Learn why retrieval-augmented generation fails when teams ignore indexing, permissions, freshness, and query design—and how to rebuild the stack correctly.
LB