The 3 AI Workflows I Run Every Day

Most people use AI the same way: open a chat window, type a request, copy whatever comes out. That's not a workflow — that's a slot machine. The difference between AI as a toy and AI as leverage is structure: structured input going in, a first draft coming out, and an operator edit before anything ships.

These are the three AI workflows I actually run, every day, across my own business and client builds. None of them are theoretical. Each one replaced a real line item — a copywriter, a researcher, a repurposing VA — and each one follows the same pattern.

Workflow 1 — Copywriting With the 3-Input Prompt

Lead capture used to be the most expensive part of every client build. Copywriter for the opt-in page. Copywriter for the thank-you page. Copywriter for the follow-up sequence. $800–$1,500 per funnel, 5–7 day turnaround.

Now Claude does the first pass — and the entire prompt is three inputs:

The objection is the unlock. Most AI copy fails because the prompt only describes the offer — so the output reads like a feature dump. Lead capture isn't an offer problem, it's a hesitation problem. Tell the model what the reader is afraid of, and the copy starts handling objections instead of describing features.

In one pass I get opt-in headline variations, page bullets, thank-you page copy, a 3-email follow-up sequence, and subject lines. Twenty minutes to draft, twenty minutes to edit. A $1,500 line item became ~$0 and 40 minutes — and this exact structure built every page and email in the $48k organic launch.

Workflow 2 — 100 Content Ideas Before Coffee

I used to start the day the way most creators do: scroll X for 30 minutes, Reddit for 20, check what competitors posted, open YouTube for trends. Ninety minutes for three half-decent ideas.

Now two Claude Code skills run automatically at 6:00 a.m. The first researches what's actually trending in my niche across nine platforms — Reddit, TikTok, Instagram, YouTube, Hacker News, Threads, and the open web — scored by what real people are upvoting, sharing, and arguing about. The second turns the top 20 topics into five content angles each: visual hook, platform pick, engagement prediction, with the top 10 starred and the top 3 scripted.

100 trend-backed ideas waiting on my desktop before I pour my first coffee. I scroll the list, pick the two or three that hit hardest, and start filming — knowing there's demand because the data proved it before I made the video.

Workflow 3 — One Asset In, a Week of Content Out

The third workflow attacks the repurposing tax. Every long-form asset — a podcast episode, a YouTube video, a detailed post — contains a week of short-form content. Most people either pay someone to extract it or let it die after one use.

The Simple Content Machine runs that extraction as a system: one piece of long-form content goes in, and AI drafts the social posts, the email, and the distribution variants — each one passed through the same operator edit before it ships. The repurposing happens inside the workflow, not in a chat window I have to remember to open.

The Common Thread

Notice what none of these workflows are: open-ended chatting. Each one has a defined input format, a defined output, and a defined human checkpoint. That's also why they compound — every week I feed back what performed, and the drafts get sharper.

Why Most AI Usage Fails

1. Vague Input

   "Write me a landing page" produces generic output because it is a generic request. Structure in, quality out — the 3-input pattern exists for exactly this reason.
2. Generate and Ship

   Skipping the edit pass is the most common mistake. AI gets you to 80%. The last 20% — cutting the AI tells, tightening the rhythm, adding the one phrase only you would say — is where the asset becomes yours.
3. No System Around It

   If the workflow lives in your memory — "I should ask AI to do this" — it isn't a workflow. The 6 a.m. content run works because it's scheduled, not remembered.

Start Here

All three workflows are documented as free, build-it-yourself resources: