ML.NET for business predictions: prove value before automating decisions
Evaluate ML.NET predictions with clear business decisions, honest data preparation, useful baselines and ongoing monitoring of real outcomes.
WORKTECHLABS / INSIGHTS
Ideas for better
business software.
Practical perspectives on technology, business and the decisions that connect them. From the team building it.
Find your next read
A fresh perspective starts here.THE JOURNAL
Your reading list
Showing 1–6 of 6 articles
AI agents in your ERP: useful actions with explicit approvals
Connect AI agents to ERP tasks through narrow tools, reviewable proposals, explicit approvals and reliable handling of uncertain business outcomes.
Build or buy an AI feature? Compare the whole workflow
Compare an AI product, a managed service and a custom integration using the same real task. Include review effort, data control, operating cost and future dependencies.
Adding AI to a legacy application without rewriting it
Introduce document extraction or a staff assistant through a controlled integration boundary. Keep permissions, business rules and final decisions in the systems that already own them.
The future of AI in business software: prepare for change, not hype
The useful question is which decisions and workflows AI can improve. A grounded view of assistants, bounded automation, evaluation and the capabilities worth building now.
Evaluating document extraction before you deploy it
Building the labelled sample that tells you whether the model is good enough for production.