How Good Does AI Need to Be Before We Put It in Charge?

Self-driving cars are the clearest proof that people demand far more than parity before they hand over control. Waymo already clears the safety bar researchers say the public requires, yet every incident still makes headlines, because we forgive human mistakes more easily than machine ones. That same pattern is playing out in AI more broadly, and it explains why low stakes tasks like drafting and summarizing get automated first while high stakes ones like payments and medical decisions come last. The piece lays out a simple framework for figuring out where your own AI use cases should sit: what the error rate looks like today, what a mistake costs, and whether it can be undone.

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How Leading Teams Are Building AI-Native Product & Dev Workflows

A simple guide to how top AI companies use tools like Claude Cowork, Claude Code, and Claude Design in their daily product and engineering work. It explains the basic building blocks that keep AI output consistent: SKILL.md files for tasks you do over and over, and CLAUDE.md files that store project context right next to the code. Then it lays out a clear plan for setting up an end-to-end flow that goes from idea to research to PRD to build to testing. Use it to see where AI workflows are heading and which pieces to set up first, without having to adopt everything at once.

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How to Take AI from 0 to 1 at Your Company

A 10-step playbook to help a company use AI in a practical way and get real results. It starts with the basics like giving everyone access to a safe chatbot, naming one AI leader, and setting clear rules for tools and data. It then explains how to pick the right problems, ship a small project into production, measure the impact, and repeat to build momentum over time.

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AI Project Intake Form

A copy/paste template to collect AI project requests in a consistent format. It clarifies the business problem, data needed, timeline, and success metrics so you can prioritize the best ideas and ship projects faster.

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Model Change Playbook

A simple plan for changing the AI models used in your systems and workflows without breaking results. This helps you upgrade to the latest models on your timeline, handle model deprecations without panic, and keep outputs consistent with basic testing and a clear rollback plan so you reduce rework and prevent customer facing mistakes.

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AI Terms in Plain English

A quick reference guide that explains AI words in everyday language. It helps leaders follow vendor pitches and internal updates, ask better questions, and make decisions without getting stuck in jargon.

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AI Vendor Questionnaire

A copy/paste template to evaluate AI vendors consistently. It helps you compare tools faster, surface risks early, and speed up procurement with fewer back-and-forths.

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AI Project ROI in 10 Minutes: Value, Cost, Impact

This doc is a fast way to estimate AI project ROI using Impact = Value − Cost. Its value is that it turns vague “AI benefits” into conservative, measurable numbers so you can quickly decide whether to move forward, revise the use case, or stop.

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