Workslop has a price, and the reader pays it
AI-made work that looks finished but is not costs the person who receives it close to two hours. Where that cost lands and what cuts it.
What we learn putting sales follow-up, back office systems and AI to work, first in our own businesses and then in our clients'.
AI-made work that looks finished but is not costs the person who receives it close to two hours. Where that cost lands and what cuts it.
Everyone quotes MIT's 95% figure. Few quote the reason the report gives for it, and that reason is the useful part for a business owner.
Three times this summer our own AI steps fell back to generic output without an error anyone could see. A timeline, and what a model change exposed.
You do not need a knowledge base to give your AI tools and your team a shared memory. You need six short pages, written in the right order.
What a new hire needs in week one is almost exactly what an AI tool needs before every task. One document can do both jobs.
Teams and AI tools both repeat failed experiments when lessons live in memory instead of on paper. How a short learnings log changes that.
Connecting an AI assistant to all your documents feels like giving it company knowledge. It mostly gives it your archive. What a memory adds on top.
Most firms keep their AI context inside one vendor's product without deciding to. Why that matters, and how to keep your company memory portable.
Most AI damage happens at the moment something leaves the building. Where to put approval gates, and why written rules alone are not enough.
The memory features in ChatGPT, Claude and Copilot remember a person. A firm needs its AI to remember the firm. Here is the difference in practice.
A drafting step in our own back office was quietly returning templates instead of real drafts. No error, no alert. How we found it and what changed.
Every new chat starts from zero, so teams spend the first ten minutes re-explaining. Questions and answers on why it happens and what helps.