Ideas on enterprise technology
Practical perspectives on supply chain, retail, data, AI, and quality engineering from the team delivering enterprise programs every day.
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Why Your Enterprise Data Isn't Delivering, and What to Do About It
Most enterprises do not have a data shortage. They have a data assembly problem: hundreds of applications holding partial views, and no governed layer that turns those views into one number anyone will act on.
How the Best Supply Chain Leaders Set Every Transformation Up to Win
The supply chain transformations that hold up under pressure share one thing, and it shows up long before a platform is selected: the operating model question was settled before any vendor RFP went out.
7 Signs It's Time to Move Off a Legacy WMS, and What Manhattan Active® Actually Changes
Accurate counts in the DC that nothing downstream agrees with, a peak season carried by people, and a platform only two people fully understand. Seven symptoms that a legacy WMS has stopped being infrastructure and started being the constraint.
6 Ways Retail and Supply Chain Teams Are Preparing for Agentic AI
The teams getting agentic AI right are not adopting it faster than everyone else. They are settling ownership, decision rights, exception handling and validation before an agent is allowed to change anything in a live system.
Why Do POS Implementations Fail? The Real Challenges Start Beyond the Register
Checkout failures rarely start at the register. They start in the pricing rules, integrations, payment flows, and store workflows the register depends on every day.
Everyone Is Talking About AI Agents. But How Do You Actually Put One to Work?
Moving from curiosity to a working agent inside a Manhattan Active® environment takes more than a good demo. It takes the right workflow, process foundation, data, and implementation approach.
Why AI Gets Business Questions Wrong More Often Than You Think
The greatest risk in enterprise AI isn't that it produces answers that are obviously wrong. It's that it produces answers that look completely reasonable while missing the business context that gives them meaning.