Search
Found 3 articles
A Framework for Choosing AI Tools: Five Questions That Eliminate 90% of Options
Facing a dizzying list of AI tools, trialing them one by one is the most expensive way to choose. This article gives you five screening questions — data sensitivity, error tolerance, integration needs, usage scale, and cost of failure — each mapped to which category of tool to pick, and ends with a decision-flow table that walks you through the whole judgment.
Build or Buy: The Decision Tree for an Enterprise Agent Platform
Should we build our own Agent platform or buy one off the shelf? This article builds a decision tree from six decision dimensions — requirement uniqueness, data sensitivity, engineering capacity, maintenance cost, vendor lock-in, and compliance requirements — and gives judgment tables for the build, buy, and hybrid routes plus a total-cost-of-ownership comparison framework.
When Not to Use AI: Six Scenarios That Will Crash and Burn
The most expensive mistake in AI adoption is deploying it where it never belonged. This article covers six high-probability failure scenarios — from accuracy requirements with no human review, to rule-certain repetitive work, sensitive data, legal liability, ill-defined tasks, and volumes too small to justify a pipeline — explaining for each why it fails, what to use instead, and under what conditions to revisit the decision.