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data quality
All articles tagged with "data quality".
Commerce operations insights and applications
Agentic AI Observability: Detecting When Your Autonomous Systems Are Operating on Bad Data
Agentic systems fail quietly. A pricing agent drifts 2% below target margin over eight weeks, processing 4,200 decisions before anyone notices. Margin leakage: £87,000. The agent didn't crash or throw errors—it just made slightly wrong decisions, consistently, for two months. Gartner predicts 40% of agentic AI projects will be cancelled by 2027, primarily due to silent degradation that compounds into operational disasters.
Operational workflow improvement
Why Fortune 500 Supply Chain AI Fails at Mid-Market: The Complexity Mismatch Problem
Fifty-seven percent of supply chain leaders cite data quality as the primary barrier to AI adoption. Not model accuracy. Not cost. Data quality. Most mid-market distributors have data spread across five to seven systems with no single source of truth. You cannot train an AI model on conflicting data. The unglamorous work of master data management, integration, and governance must come first.
Platform capabilities and technical insights
System Integration Challenges: When Legacy Meets Modern AI
A legacy ERP connects to a modern AI pricing platform. A field mapping breaks. Prices don't update. Nobody notices for 48 hours. By then, £15,000-£30,000 in margin has leaked. Silent failures are worse than loud ones because they compound. Real-time data observability makes these failures visible and resolvable before they cost money.
Operational workflow improvement
Agentic AI Data Architecture: Why Your Supply Chain Intelligence Fails Without the Right Foundation
A pricing agent at a South Yorkshire distributor adjusted 1,200 SKUs based on supplier cost data that was six weeks stale. The pipeline reported healthy. The data quality checks passed. The system lost £34,000 in margin over three months before anyone noticed. The agent hadn't malfunctioned - it had operated exactly as designed on information that was technically valid but operationally worthless. This is the observability gap that makes autonomous systems dangerous. Traditional monitoring tracks schema compliance and pipeline health. Agentic AI needs semantic monitoring - systems that understand whether data makes sense in the context of the decision being made, not just whether it arrives on time in the right format.
Platform capabilities and technical insights
LLM Observability: Why Custom AI Models Need Different Monitoring
Custom AI models degrade silently in production. Most mid-market distributors discover this weeks after the damage begins—when pricing errors accumulate, inventory misallocates, or search results decay. Model drift is silent cost leakage that compounds over time.
Operational workflow improvement
Budget Allocation Intelligence: How AI Prevents Mid-Market Distributors From Funding Dead Projects
Eighty percent of AI projects fail to deliver intended business value. The failures are predictable and preventable. Data governance gaps, scope creep, stakeholder misalignment, and unrealistic timelines appear weeks before deployment—but most distributors commit capital without spotting them. This article walks through the specific red flags that signal project failure and provides a framework for pre-deployment validation that takes 1-2 days, not months.
WithPraxis
Data Quality: The Foundation Every AI Project Needs
Eighty percent of AI initiatives fail before reaching production. The culprit isn't model complexity - it's bad data. This article examines what proper data quality assessment looks like, how migration transforms messy data into AI-ready systems, and what governance means for mid-market distributors.