AIErudit Blog
Practical AI insights, career strategies, and learning resources for Business Analysts, Product Managers, and technical leaders.
Latest articles
Why Business Analysts Need AI Skills in 2026
The role of the Business Analyst is evolving faster than ever. In 2026, AI literacy is no longer optional -- it is the foundation of competitive advantage in every industry.
Claude 101: What You'll Learn in Our Flagship Course
A detailed look inside Claude 101, our flagship course designed for Business Analysts who want practical, hands-on AI skills they can use from day one.
From Requirements Docs to AI-Powered Workflows
Traditional requirements documentation is slow, inconsistent, and often outdated before it ships. Here is how AI-powered workflows transform the entire requirements lifecycle.
The PM's Guide to AI Product Strategy
Product Managers face unique challenges when integrating AI into their product strategy. This guide covers the frameworks, metrics, and decision points that separate successful AI products from expensive experiments.
How CTOs Should Evaluate AI Tools for Their Teams
With hundreds of AI tools competing for your engineering budget, CTOs need a systematic evaluation framework. Here is how to separate signal from hype and make decisions that scale.
Codex or Claude Code? Choose the Workflow
A practical way to choose between Codex, Claude Code, and agentic coding workflows: start from the work system, not the logo on the tool.
Prompting Is Now Context Engineering: How to Design AI Agent Context
Prompting still matters, but professional AI work now depends on context engineering: sources, tools, constraints, output contracts, and verification.
Why Agentic AI Pilots Fail Before Production
Agent demos are easy to love. Production agent workflows need ownership, data boundaries, evaluations, governance, and a release loop.
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