Most AI hires learn the business on the job. I learned it running four companies, then built the agents.
I start from the P&L, not the model. 10+ years as founder, CEO and CMO: $10M+ in own-company revenue from $0 with no outside funding, and 8 figures in verified client revenue ($100M+ estimated). In 2025–26 I replaced a 24-person fulfilment and ops team with 5 people and 25 AI agents doing the same work, and cut monthly payroll, software and AI cost by 85%. I know models, APIs and data well enough to build with them, and I lead engineers for deep builds.
Two ways to fill an AI implementation seat. Most candidates bring the code and learn the business. I bring the business, and I have already shipped the agents.
AI now writes a large share of the code. What it can't do is sit with the business stakeholders, find the workflow that costs the most, rank the use cases by ROI, decide what a machine should own and what a person must keep, and drive adoption until the team trusts the result.
That is business acumen, and it takes a decade to earn. I have it from building four companies, and I have the technical fluency to build and ship the agents myself. For deep engineering, I lead developers rather than pretend to be one.
Live screenshots, October 2026. Click any image.
15–20 min walkthrough of the outcome I want
Flat-rate Claude Max plan; reads the transcript and every frame, delegates
Acquisition, ops, fulfilment: plan and review
Sonnet builds; Hermes on low-cost models handles volume
Projects that used to take a 10-person team a week now ship in 1–2 days with AI agents. Humans approve money and client-facing steps.
The same loop I ran on my own company, and the one I would run inside yours.
Sit with the business stakeholders. Map the most expensive workflows in hours and money.
Rank the AI use cases by ROI. Decide what AI owns, what a human keeps, where the approval gates go.
Agents, APIs, voice and data, wired into the tools the team already uses.
Pilot to production: QA on every release, a human on every money or customer step, adoption with the team.
KPIs: cost per task, hours saved, P&L impact. Then pick the next wave.
The cost lesson. We burned $50K+ of AWS credits in about four months because nothing was routed by cost. Since then: frontier models only where judgment matters, flat-rate subscriptions over per-token billing, low-cost models for volume, daily spend caps per provider. Deploying AI is easy. Deploying it profitably is the job.
First code at 11 (Visual Basic, simple HTML sites). Then I chose business as the higher-leverage skill.
Chosen by cost and job, not by hype.
I hired, ran and paid for these jobs long before agents could do them. That is where the judgment comes from.
A one-man Meta ads shop grown into an agency for 300+ clients. Built the media buying team, ran offer strategy, and advised founders scaling from six to seven and seven to eight figures.
A fully automated evergreen webinar funnel that became a $2M/year business at peak, run by six people. My first company built on automation instead of headcount.
I didn't finish school. I chose business instead, and I've been building ever since: four companies, teams of 3 to 50, millions in ad spend. Then I spent two years putting AI agents into the work my teams used to do, and learned what pays back and what doesn't.
Based in Dubai (UAE Golden Visa, no sponsorship needed). Looking for an AI Implementation Lead, AI Deployment Strategist or Head of AI Transformation seat, in the US or the GCC, where the business problem matters as much as the code.
Verified figures only. Ad platforms keep about 3 years of data and I no longer have access to some older client accounts, so I quote what I can show.
Hiring for AI deployment, or want agents running inside your business? Send me the workflow that costs you the most. My inbox is open.
