Improving Warehouse Efficiency with Unified Data and AI-Driven Visibility - with Dan Keto of Easy Metrics

2026-02-19 · Source: The AI in Business Podcast · Field: Manufacturing & Industrial — Supply Chain & Logistics, Manufacturing Operations & Management, Artificial Intelligence & Machine Learning · Depth: Intermediate, long

Summary

Dan Keto, President and Co-founder at Easy Metrics, discusses the critical need for unified data foundations in warehouse and distribution networks before implementing AI. He highlights that fragmented transactional data across various systems, including robotics, automation, and WMS, leads to poor visibility, delayed decision-making, and constant crisis management in operations. Easy Metrics' cloud-based platform unifies operational, labor, and financial data to provide real-time visibility, enabling faster diagnosis of variances and more defensible decisions. Keto emphasizes that aligning stakeholders on a common data language and conditioning the data are crucial steps, as attempting "AI-first" approaches with unoptimized data can lead to "hallucinations" and exorbitant costs, particularly when applying LLMs to mathematical operational data.

Key takeaway

For warehouse operations, supply chain, and finance leaders aiming to improve productivity and defend margins, you must prioritize establishing a unified data foundation. Fragmented data leads to costly delays and poor decisions, and attempting AI without conditioned data will result in inaccurate outputs and dramatically higher expenses. Focus on aligning your stakeholders around a shared data language to enable repeatable investigations and proactive alerts that expose true cost drivers and guide operational fixes.

Key insights

Unified data foundations are essential for effective warehouse operations and cost-efficient AI implementation.

Principles

• Data unification precedes AI adoption.
• Align stakeholders on common data language.
• Optimize data structures to minimize AI costs.

Method

Unify fragmented transactional data into a single pane of glass, align stakeholders on a common data language and KPIs, then build investigations and apply AI for proactive alerts and insights.

In practice

• Prioritize data unification over early AI deployment.
• Construct investigations to replace manual data analysis.
• Use AI for proactive alerts on cost drivers like overtime.

Topics

• Warehouse Data Foundation
• AI in Warehousing
• Operational Performance Management
• AI Implementation Strategy
• Cost Optimization

Best for: CTO, Executive, AI Product Manager, Director of AI/ML, VP of Engineering/Data, Operations Professional

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Editorial summary, takeaway, and curation by AIssential. Original article published by The AI in Business Podcast.