I run more than fourteen brands, and at some point last year I wanted the thing everyone's hiring for — the AI executive who sits across the whole portfolio and makes it all cleverer. I couldn't make the hire make sense, so I built the job into the operating system instead, because I had to, and I found out the hard way which bits of it actually mattered.
The way I found out: a broad-match test quietly cost me money on the SFT account before I'd built the guardrail that now catches that exact thing. Which is the part of all this that doesn't fit on a LinkedIn headshot. The market's busy inventing a six-figure title for it — Chief AI Officer, Head of AI, AI Transformation Lead, serious headshots, a quarterly steering committee — and I get the pull, everyone can feel AI is going to change how work gets done and nobody wants to be the founder who waited. But I didn't need a title. I needed the job done, and I needed it to behave when nobody was watching.
Here's the thing I'd say to most ecommerce brands before they reach for that hire. It's not a vision gap — it's a plumbing gap, and I say that with feeling because mine was plumbing too: data scattered across five tools, half the decisions buried somewhere in Slack, a product catalogue that fell over the moment you looked at it sideways. A senior hire with a remit to "explore use cases" tends to make the plumbing gap more expensive, not smaller.
So I built the job into the operating system instead
Inside strivefortone, the AI role isn't a person in a meeting. It's a set of systems that read the accounts overnight, hold onto past decisions, draft the work, check the claims and surface the one thing that actually needs deciding in the morning.
It runs across more than fourteen brands now, and the shape of it is roughly this: every night the data comes in and each brand gets scored, anything that looks off gets flagged, and the decisions we've already made stay attached to the work so the thing isn't starting from a blank page at six in the morning the way a new hire would. Small, bounded jobs go to agents — write 4,134 product descriptions, watch the server-side tracking, keep a parts-finder running live on a storefront so a customer can actually find the part that fits their car. Anything that needs taste, real risk judgement or client context comes back to a human. That's the part most "AI strategy" skips, and it's the part that matters.
Continuity is the first real win, not speed
Speed is nice, and I like speed — it's what lets you ship a landing page or an email or a catalogue pass or a tracking fix while the idea is still warm, instead of three weeks later when you've half forgotten why you wanted it.
But the deeper win is continuity, because a business gets quietly expensive when every decision has to be rediscovered from scratch. Why did we pause that campaign in March? Which claim did we actually check against source data, and which one did we just hope was true? An AI operating layer is supposed to carry that forward so you're not re-litigating last quarter every Monday. Otherwise what you've built is a clever intern with no memory and a lot of confidence — fine for a first draft, genuinely bad at running anything with money attached.
What this means for a brand
For a founder or a marketing lead, the practical version looks like this:
- a 06:00 briefing that tells you what changed overnight and what needs a decision from you today
- guardrails sitting on the boring places money leaks — broken tracking, a catalogue gap, a campaign quietly drifting
- content and campaign drafts built from a proper brief, not a loose prompt typed at midnight
- a memory layer that knows the brand, the past decisions, the mistakes you don't want to repeat and the constraints you're working inside
- a human review point wherever taste, risk or commercial judgement actually matters
If this is your problem
This usually isn't a $150k hire problem at the start. It's a contained implementation problem. If you're not sure where to begin, a $2,500 roadmap sprint is the low-commitment way in — we look at your data and your workflows and tell you what's worth building first. From there, a bespoke build scoped to your actual data starts at $5,000, solving one real workflow end to end. And if the value is there, the ongoing piece — the 06:00 briefing, the guardrails, the memory and the review — sits beside the business as an Intelligence Retainer at $3,000/mo. Full pricing is on the AI implementation page. Build the system before you build the department.
This is why I keep talking about implementation rather than consulting. The value was never in agreeing that AI could help — of course it could, everyone agrees on that at the start of every meeting. The value is in picking the right part of the business, wiring the data into it, setting the rules, giving the system an actual job, and then making sure it behaves when nobody's watching it for the first five minutes. That last bit is where most AI work quietly falls over. It demos well. Then it meets an actual business, with actual money attached.
The point of all this isn't to look AI-forward. It's to make the business less dependent on heroic manual effort — on someone clever staying late to hold it all together in their head.
Could you hire someone to lead that? Sure, and sometimes you genuinely should. But before you add a salary, you can build the spine it would sit on: the memory, the workflows, the guardrails, the jobs that run whether or not anyone remembers to run them. I built it for my own portfolio first, because I had to. The pieces that turned out to be useful became the builds other brands can use.