Most teams have the same complaint: the models are powerful, but the work feels scattered. You prompt in one tab, save assets in a folder, paste them into a doc, and try to line it all up in a video editor. Change a sentence and the whole stack wobbles—so you export again, upload again, and rename another file.
TL;DR
- An AI workflow is the path from idea to output—plan, prompt, generate, review, revise, ship.
- Today’s tools are fragmented: every step lives in a different app, context gets lost, and small edits turn into big chores.
- An infinite canvas fixes the flow by keeping text, images, audio, and video together, connected by relationships—not copy/paste.
- Result: faster iteration, fewer mistakes, clearer source of truth.
What is an AI workflow? (Plain English)
An AI workflow is simply how work moves from a spark of an idea to something you can publish. It’s the sequence of steps you repeat—often daily—plus the glue between those steps. In practice, that means:
- You outline the goal and constraints.
- You write a prompt (and sometimes add a reference image or script).
- A model produces a draft.
- You review, tweak the input, and generate again.
- You pick a result, format it, and export.
That’s the AI process. It’s not magic—it’s steps. The trouble isn’t the steps themselves; it’s that each step usually happens in a different tool.
How AI works
- Plan — Define the outcome, audience, and constraints.
- Collect context — Gather references: text notes, links, images, brand rules.
- Prompt — Write a clear request with examples and constraints.
- Generate — Run the model with the right parameters; create between 1 and 10 variations.
- Review — Compare results against the goal and constraints.
- Revise — Adjust prompts, swap references, try a different model.
- Publish — Export in the right format (PNG, MP4, WAV, PDF) and share.
Each loop makes the work better. The pain starts when those loops cross app boundaries.
Why your tools don’t work together (yet)
- Single purpose silos — Prompt here, edit there, export elsewhere. Nothing shares state.
- Brittle handoffs — Copy/paste and file exports lose parameters, seeds, and context.
- Version drift — “final_v7_REAL_final.mp4” exists because your source and outputs are separated.
- Hidden costs — You don’t see how many credits a change will take until after you click.
- Slow feedback — One small edit forces a chain of manual reexports and uploads.
It’s not you—it’s the architecture. We’ve been using linear tools for nonlinear work.
The missing piece: an infinite canvas workflow
An infinite canvas treats your project as a connected system rather than a pile of files.
- One surface — Documents, images, audio, and video live together.
- Connections, not copies — A script connects to an image generator and a voice model; change the script and downstream nodes update.
- Context travels — Prompts, parameters, reference images, and seeds stay attached to the work.
- Fast iteration — Generate between 1 and 10 variations and pick visually. No tab ping pong.
- Export from source — MP4/PNG/WAV/SRT directly from the canvas—no reassembly.
- Clear ownership — The document is the single source of truth; outputs are children, not orphans.
This is the gap an infinite canvas fills—it makes the flow feel human.
A five minute example
Let’s say you’re making a launch post with a hero image and a short teaser clip.
- Write a 60 to 90 word post on the canvas.
- Connect the post to an image node; pick a style and generate 3 variations.
- Connect the same post to an audio node for a calm VO.
- Send the selected image + VO to a video node; export a 15 to 20 second clip.
- Edit one sentence in the post. Regenerate image/VO/video with a click—no reuploads.
Same models you already use—less friction, more control.
When linear tools are fine—and when they aren’t
- Fine — One off graphics, a quick paragraph, a single export.
- Not fine — Anything that needs iteration, versions, or multiple formats.
If you expect to change your mind—as you should—opt into a workflow you won’t outgrow.
Common pitfalls (and how the canvas avoids them)
- Vague prompts → Keep examples and constraints next to the prompt.
- Losing reference images → Attach them directly to the node; no broken links.
- Parameter amnesia → Store seeds, steps, and settings with the output.
- Layout thrash → Generate variations in a grid; compare at a glance.
- Publish scramble → Export the right format from the same place you created it.
Key takeaways
- An AI workflow is the repeatable path from idea to output.
- Fragmentation—not the models—is what slows teams down.
- An infinite canvas unifies steps, preserves context, and speeds iteration.
- The payoff is simple: fewer tabs, fewer exports, better work.
Try it
Build your next piece on a canvas—see how fast the second edit feels.
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