Research library
Insights
Analysis of AI, automation, and software: what is changing, what holds up, and what it means for practical decisions.
198 articles · Page 17 of 17
Data Quality: The Make or Break Factor in AI
Why data quality matters more than model choice for AI success. Learn practical steps to assess, clean, and improve your data before any AI initiative.
The Case for Boring Technology
Proven tools beat shiny frameworks. How boring technology choices compound into faster delivery, fewer outages, and real competitive advantage.
AI-Assisted Development: Beyond the Hype
An honest look at AI coding assistants like GitHub Copilot and Claude. Learn where they excel, where they fail, and how to use them effectively.
The Hidden Costs of AI Projects
The hidden costs of AI projects that budgets miss: data prep, integration, talent, and maintenance. A realistic budgeting framework.
Building Your First AI Proof of Concept
How to build an AI proof of concept that delivers real insights. A practical framework for POCs that validate AI for your problem.
The Enterprise AI Adoption Gap
Why most enterprises fail to move AI from pilot to production, and practical strategies to overcome the real adoption obstacles.