Heard an AI term in a meeting?
Here’s what it means for you.
AI words get used in board meetings, vendor demos and LinkedIn posts as if everyone knows what they mean. We put AI to work inside businesses like yours, so we’ve unpacked the ones we hear most: what each one is, why it matters to your team, and what to do next.
- What it is
- One page that turns your three biggest pains into changes, each with a number, an owner and a fix. The fix isn’t always AI.
- What it changes
- Six months on, anyone can check whether it worked. “AI everywhere” can’t be checked, so nobody can tell.
- What it is
- Model Context Protocol: a standard way for an AI to plug into the software your team already uses.
- What it changes
- Instead of pasting things into a chat window, your AI looks them up in the tools themselves. You still choose which tools it can reach.
- What it is
- An assistant that only sees one person’s files, inbox and calendar. Handy for your own work, stuck the moment a job passes to a colleague.
- What it changes
- Multiplayer AI works from one record the whole team shares, so a handoff stops ending in “ask so-and-so”.
- What it is
- Mapping how a process really runs, and cutting the steps that shouldn’t be there, before any AI goes in.
- What it changes
- AI on a messy process just makes the mess faster. Fix the process first, then spend the month after launch helping people use it.
- What it is
- AI doesn’t know your business. It looks facts up, and most facts live in several places that don’t agree.
- What it changes
- Connect the tools you already use and give each fact one home. Answers come out right, and only the right people see them.
- What it is
- Software you buy covers what every company does. The steps that make your business yours are the part nobody sells.
- What it changes
- Keep the systems you bought and build a thin layer on top for those steps. With AI writing much of the code, that’s no longer a big-company project.
Six terms,
unpacked.
Each page takes one term, says what it is in plain words, and shows what it changes for a team like yours. They’re grouped in the order most companies run into them, but each stands on its own, so start with whichever you heard most recently.
Deciding where AI goes
Before anything is bought or built- AI strategyHow to write an AI strategy you can measureSix steps from “what hurts” to a one-page plan anyone can check, with a free workbook to do it in.
- Workflow firstWhy AI rollouts stall a month after launchWhy mapping the process comes before the AI, how forty steps became eighteen, and what the weeks after launch need.
Choosing what to buy, build or connect
When a vendor is pitching- Buy vs buildWhen building your own software beats buying itWhat changed the cost of building, and how to keep the systems you already pay for while adding the part that’s yours.
- MCPWhat MCP means, and why vendors keep mentioning itWhat it connects, what it’s good for, and when switching it on is worth the effort.
Getting AI to read your business
Once the answers don’t line up- AI and your dataWhy AI gets your numbers wrongHow the same fact living in several places trips AI up, and how connecting your tools fixes it.
- Multiplayer AIHow to make AI work for a team, not just one personWhy personal assistants stall at every handoff, and what changes when everyone works from one shared record.
Stop guessing where AI belongs.
We’ll show you.
Every page ends in the same place: one job, one team, and the one step where AI would make the biggest difference. If you’d like help finding yours, that’s what an AI Jumpstart is: we work through it with your team and prove it on your own records, in two to four weeks. And if there’s a term you keep hearing that isn’t here, ask us. We get these questions all the time and we’re happy to explain.