ERP Data Strategy for the AI Era
AI needs clean data, but ERP data is usually a mess. Most companies can't fix data strategy fast enough to power real AI features. Here's what to do.
Thoughts & Perspectives
Nine years of writing about AI, building custom ERP systems, and shipping mobile products. By Abhi Asok.
AI needs clean data, but ERP data is usually a mess. Most companies can't fix data strategy fast enough to power real AI features. Here's what to do.
Single agents are useful, but multiple agents coordinating across complex workflows and making decisions together is where enterprise AI is truly headed.
The New Architecture (Fabric, JSI) is finally complete and React Native is mature now. But is it still the right choice for your next mobile project?
The promise: fully autonomous ERP workflows with no human touch. The reality: partially autonomous, heavily guardrailed, where constraints matter more than AI.
Three years after ChatGPT launched, enterprise AI adoption is real but much narrower and messier than anyone predicted. What deployed, what didn't, why.
Chatbots are table stakes now. The real frontier is AI agents that autonomously take sequences of actions on behalf of users. Here's how to build it.
Every ERP vendor launched an 'AI copilot' in 2024. I've tested most of them. Here's what separates genuine workflow assistance from rebranded autocomplete.
2024 was the year AI agents shifted from research projects to true production reality. Here's what this fundamental shift means for the entire industry.
iOS 18 shipped in November. SwiftUI is finally, genuinely production-ready for complex enterprise apps. That fundamentally changes everything for iOS teams.
ERP integrations break constantly. SAP changes an API, your systems are down for days. Here's the solid architectural pattern that prevents these failures.
Phi-3, Gemma, Llama 3.1. In September, the market shifted from massive models to small, efficient ones. It's not a compromise—it's legitimately better.
Logistics ERP is uniquely complex. Six different optimization problems running simultaneously. This is what we learned rebuilding one from the ground up.