How I found a problem to solve
I’m Novi, founder of AiPowerCoach, a company that helps professionals adopt AI systems they can actually rely on.
In November 2025, I was preparing a PowerPoint presentation to train professionals on essential AI skills. Halfway through, I stopped.
Something felt off.
In a world where AI can respond, adapt, and reason, a slide deck felt strangely passive. I was explaining how to work with AI using a tool that couldn’t interact, couldn’t check understanding, and couldn’t adapt to the person in front of it.
I kept thinking: this should be possible with AI itself.
Not as a demo. Not as a free-form chatbot conversation. But as a structured learning experience that could guide someone step by step.
That thought was the seed.
How I discovered a bigger problem
As I explored that idea, a small but persistent problem kept showing up.
Every AI learning experience depended heavily on the person using it. Some people progressed smoothly. Others got lost quickly.
The difference wasn’t intelligence or motivation. It was whether they knew how to work with AI.
People hesitated. They second-guessed their inputs. They weren’t sure whether they were doing things in the right order.
At that point, I didn’t have a theory. I just had a sense that the interaction itself was fragile.
From passive slides to active AI learning
AI, unlike slides, can listen.
It can ask follow-up questions. It can check whether someone understands. It can slow down or move forward.
That opened a new possibility: learning with AI instead of learning about AI.
But if this was going to replace passive teaching, it had to behave consistently. Especially if a trainer wanted to guide a group, or if people were learning on their own.
Without consistency, there is no shared experience. And without a shared experience, learning breaks down.
The missing implementation in AI interaction design
I started looking for existing work on AI interaction design.
What I found was mostly conceptual. Good ideas. Useful principles. Very little that actually ran.
There was no concrete way to make a chatbot follow a written scenario and guide a user toward a precise outcome, consistently.
No execution model you could hand to several people and expect the same result. No structure that could be reused without rewriting everything.
That gap became the problem worth solving.
What an S-File really is
The first real constraint I worked on was simple: multiple people should be able to go through the same AI-guided session and progress through it without drift.
To make that possible, the AI had to follow rules.
That led to the S-File.
An S-File is an execution contract: something the AI agrees to run, step by step, without improvising.
It’s closer to a small application than a prompt. It defines activities, transitions, validations, and boundaries.
Early versions were rough. The AI needed help distinguishing between what should be shown to the user and what should be executed in the background. Clear boundaries mattered more than I expected.
As those boundaries improved, the S-File evolved into a structured workflow the AI could reliably follow.
The trust gap in everyday AI use
While testing S-Files with different people, something became clear.
People weren’t mainly worried about what to say to the AI. They were worried about whether they were doing things correctly.
They asked questions like:
- Am I missing a step?
- Is this the right moment to move on?
- Can I trust the result I’m getting?
Two people could attempt the same task with AI and end up with very different outcomes, not because one was careless, but because the interaction itself was unstable.
The problem wasn’t AI capability. It was predictability.
That’s where the trust gap lives.
What people started doing with S-Files
Once S-Files were shared, a clear pattern emerged.
Because the behavior was predictable, people trusted the process. And once they trusted the process, they used it for real work.
One person used an S-File to build a Gmail-to-Slack automation, moving step by step and confirming understanding before continuing.
Another used an S-File to create a complete explainer e-book without knowing how to design a formatting system or write complex prompts.
Someone else told me an S-File designed to clarify a problem helped them rethink a business strategy they had been stuck on.
In most cases, people reached a clear outcome without ever writing a prompt themselves.
Nothing unexpected happened.
The S-Files behaved exactly as designed. What changed was how people related to AI.
Why an S-File is better than a prompt
Prompts assume a skill most people were never taught.
Writing a good prompt requires knowing what information to include, what constraints to add, and what questions to ask, often across several iterations.
An S-File removes that burden.
It packages a sequence of prompts, checks, and validations into a single, repeatable path. People don’t need to know how it works. They just follow it.
Normally, reaching a solid result with AI involves backtracking, rewriting, and guessing what to try next. With an S-File, the path is already there.
You don’t need the recipe. You don’t need the kitchen. You just follow the process and reach an outcome.
What I think the future holds for this format
Imagine working with AI without writing complex prompts.
Without rebuilding context every time.
Without worrying about hallucinations, missing steps, or asking the wrong questions.
That’s what this format makes possible.
S-Files reduce cognitive load. They lower the skill barrier. They replace experimentation with execution.
Information is submitted deliberately and validated as it enters the workflow. Error checking is built in, so results are challenged rather than accepted blindly.
For organizations, this means AI use can be structured, constrained, and trusted, without relying on individual judgment at every step.
Why this matters now
AI is moving out of experimentation and into everyday work.
As that happens, the cost of unreliable interactions increases. Inconsistent results don’t just slow people down, they undermine confidence.
Tools that only work for experts won’t scale. Systems that behave predictably can.
This is how AI becomes more reliable and dependable
AI doesn’t become useful by being more impressive. It becomes useful by being more reliable.
I didn’t set out to invent a new format. I was trying to solve a small, practical problem: how to make AI-guided work behave the same way every time.
The S-File is simply where that path led.
It’s one way to make AI more reliable and dependable, by design, not by chance.
