Most training is a slideshow and a snack. You sit, you nod, you forget. This week is the opposite. We teach by handing your team the real tools and letting them ship something live by the end of the day. This first session is the map. We are going to wire AI and automation into your go-to-market work, step by step, so you can run it yourselves after we leave. No mystery, no hype, just the moves.

Step one is the workspace. We open Claude Code in the terminal on your own laptop. It is a coding agent that holds your whole project in its head, so nobody is jumping between twelve browser tabs. Your team types what they want in plain English, and the model does the clicking. The first lab is just this: get it installed and ask it to read your folder of marketing files out loud.

Step two is giving the agent hands. We connect one tool through an MCP server, which is a small adapter that lets the model talk to outside software. We pick Google Sheets first because everyone has a messy sheet. Now your team can say, read my campaign tracker, and the agent pulls the rows directly. They watch the connection light up. That is the moment it stops being a chatbot and starts being a worker.

Step three is the prompt. We teach your team to write it like a cooking recipe, not a wish. Name the input, name the steps, name the output. Bad: make this better. Good: take these three subject lines, rewrite each in plain language under nine words, keep the offer. The lab here is each person rewrites their own vague ask into a clear one, then runs both and sees the difference.

Step four is offloading busywork. We point the agent at a real chore your team hates. Tagging two hundred leads by industry. We show them how to give the model the list, the rules, and one example, then let it tag the rest. They check ten by hand to trust it. That habit, spot-check before you ship, is the whole game. The model is fast, you are the editor.

Step five is data. We connect the agent to your analytics through another MCP server and have your team ask it questions in words. Which posts brought traffic last month. Which email got opened. No more waiting on a report. They pull their own answer, see the query the model wrote, and learn to read it. Now the data lives where the decision happens, not in someone else's inbox.

Step six joins the last two. Once the agent can read your numbers, we have it write from them. It looks at which topics actually pulled people in, then drafts the next three posts about those topics. Your team is not staring at a blank page anymore. They are editing a draft that already knows what worked. The lab is one post, start to finish, built off real traffic.

Step seven is making it stick. A good prompt should not die when the person closes the laptop. We teach your team to save each working recipe as a small file in the project, so anyone can run it again next week. This is how a workshop becomes a system. One person figures out the lead-tagging move once, and the whole team inherits it forever.

Here is our proof, with no invented numbers. The Big T-M runs its own studio on exactly these moves. Our whole content calendar, every video and post, is built by an AI agent from one source file. We are not selling you a method we read about. We are handing you the one we use to keep our own lights on, and you can watch it run in public.

So that is the week ahead. Each day we go deeper into one part: growth, content, data, then the automation that ties them together. The takeaway today is simple. AI is not a robot that replaces your team. It is a fast hand your team learns to direct. Go see the whole thing running at office dot temerarii dot xyz, then come back tomorrow and we build.