Viveka 
What’s the difference between using AI and building with it?
Using AI is reactive: you ask a tool for something, it hands you something, you take it or tweak it, and you move on. It’s genuinely useful for small things, and it’s what most accomplished women do with AI — which is also exactly why most of them feel like AI isn’t really for their business. Building with AI is different. It’s not about asking for an output; it’s about knowing exactly where friction lives in your specific workflow — the part of your business that slows you down or feels disconnected from everything else — and using AI to remove that friction. You’re not handing your thinking to a machine; you’re getting the friction out of the way so the thinking you’re already doing can actually reach people.
The reason “using” feels generic is that the output wasn’t built for you — it was generated by a template. You default to it because it’s the easiest door in: you already know how to ask questions, you can paste a prompt, you can get something back. Done. But done is not the same as moved, and that gap is the whole problem.
What does building with AI actually look like?
Here’s a real one from my own business. Every client call I run has intellectual property in it — a specific framework, a moment where I named something a client had never heard named before, an example of what’s working and what isn’t. For three years I did what most people do with that, which is nothing, or I’d try to transcribe the good parts by hand and abandon it a week later.
Then I changed the workflow. The call is already recorded, so I send the recording to Claude with a specific ask: pull the three biggest insights, the frameworks I mentioned, the examples that landed, and the exact moment the client shifted. What comes back isn’t “advice about positioning” — it’s the actual positioning conversation I already had, cleaned up. From there I can turn one hour of client call into a week of assets in about thirty minutes: a Substack article, three LinkedIn posts, an email, a community teaching, all in my voice, because it started with my real work instead of a template. The AI is doing the unglamorous part, clearing away friction, while the thinking stays entirely yours — it’s the connective tissue between one hour of good thinking and a week of visibility. (That’s the same instinct behind turning the asset you already own into something usable, which I wrote about in Your IP Is Already an App.)
Why do most people get the AI piece wrong?
Because they try to solve it first, before the two things underneath it are in place. In the Visibility Infrastructure OS I call the AI piece Module 3, the AI layer, and it only works once Modules 1 and 2 are done. Module 1 is inner clarity — positioning, knowing exactly who you serve and why you’re the only one who does what you do. Module 2 is outer infrastructure — the systems that make visibility happen consistently, like your LinkedIn structure, Substack as the home for your IP, an organized list, a community that actually functions. Module 3 is where AI removes the friction between your expertise and your visibility.
Most people start at Module 3, thinking that if they just get the AI part right, everything else follows — so they learn prompting, test tools, try every framework, and nothing lands. If your positioning isn’t clear, AI can’t position you; it can only generate more confused content. If you don’t have the infrastructure, AI can’t build it for you; it can only hand you more content to push into the void. That’s why the two hours with Claude felt wasted: you were trying to solve a positioning problem with a tool built for workflow friction.
Where does AI actually move the needle?
Three places, once your positioning and infrastructure are steady. First, recording to distributing: you’re already talking — client calls, talks, notes you dictate — so you have expertise in audio form all the time, and AI can pull the substance out, clean it up, and make it shareable. This is the one that saved me the most time and produced my most recognizable content. Second, batch content from a single source: instead of writing one post at a time, you start from one piece of wisdom and let AI help you see the angles — one positioning insight becomes three LinkedIn posts, a Substack article, and a community teaching. Third, and this is the sneaky one, testing positioning angles at scale: you prototype a few versions of who you serve and why they need you, in different formats, and watch which one your audience actually responds to. You end up testing instead of guessing, and you can do it inside of a week.
How do you start building with AI instead of just using it?
Four steps, and it takes about two weeks to get the rhythm — not months. Map your workflow first: where are you wasting time, where does expertise sit and not move, where do you keep saying “I should be sharing this” but the friction is too high? Then pick one place AI removes friction — not everything at once, just the single thing that would actually change the tempo of your business (for me it was call recordings; for you it might be something else entirely). Test it on your own business before you build a system around it, and run one of your own calls or documents through it to see if it produces what you wanted. Then document what works and kill what doesn’t, because you’ll iterate a few times and that’s normal. After that, it’s just part of how you work.
Do you need to understand how AI works to do this?
No. You don’t need to be good at prompting, and you don’t need a course on Claude or ChatGPT or any of it. What you actually need is clarity on what you want to build and where the friction lives, and that clarity doesn’t come from the tool — it comes from your positioning, from understanding your work clearly enough to know what thinking is worth distributing and where the bottleneck actually is. That’s what Module 1 of VIOS is for: a short process that gets you clear enough to see where the friction is and what’s worth building toward. Everything else follows from that.