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Analysis of AI, automation, and software: what is changing, what holds up, and what it means for practical decisions.
155 articles · Page 13 of 13
Managing Stakeholder Expectations in AI Projects
Learn how to bridge the gap between AI demos and production systems. Set realistic expectations and maintain stakeholder trust throughout your AI project.
Integration Patterns That Don't Break at Scale
Webhooks, polling, message queues, or event-driven architecture? How to choose the right integration pattern and avoid the point-to-point trap.
API Design Principles That Stand the Test of Time
APIs outlive the code that calls them. A practical guide to designing HTTP APIs that stay stable, intuitive, and maintainable as your product scales.
Build vs Buy: An AI Solution Framework
When should you build custom AI solutions vs. leverage existing tools? A practical framework for making this critical decision.
Build vs. Buy: Workflow Automation Guide
iPaaS, RPA, or custom code? A practical framework for choosing the right workflow automation approach for your business.
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.