Research library
Artificial intelligence
Evaluate where AI is useful, understand the systems behind it, and make informed choices about models, data, and deployment.
213 articles · Page 18 of 18
When AI Makes Sense (And When It Doesn't)
A practical framework for evaluating whether AI is the right solution for your business problem, or if simpler approaches would serve you better.
Kimi K2.5: Trillion-Parameter Open AI Model
Moonshot AI releases Kimi K2.5 with 1 trillion parameters, open weights, and the ability to spawn 100 autonomous sub-agents.
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.
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.
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.
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.