I'm Andy Jordan, applying for Director of AI, Operations.
I build AI agents and tools that do real operational work. Agents run my company's daily operations, and a document application I built runs at a client every day.
I made this instead of sending a cover letter.
iFIT already changed how the world trains. Operations is next.
On the product side, iFIT has entered the AI era — an AI Coach live for members in 19 countries, on a platform built over almost fifty years through NordicTrack, ProForm, and Freemotion. Millions of members train on it every day.
The mission is bigger than equipment: serve the athlete, improve people's lives, and lead the world in digital health. Operations is how that mission gets delivered. This role brings AI into S&OP, Member Care, Quality, Procurement, Logistics, and Customs — and raises the AI skills of the whole Ops organization along the way. The goals are the ones every operations leader is chasing right now: more efficiency, accurate and timely data, lower costs.
That's the work I've already been doing — at a smaller scale, with my own money on the line. The rest of this page shows what it looks like at iFIT.
I don't talk about AI operations. I run one.
I've spent the last decade running restaurants — up to 10 locations and 200-plus employees. P&L, supply chain, staffing, quality. Earlier this year I went all-in on AI: I rebuilt my own operations on AI agents, founded an implementation firm, and started doing the same for other businesses — while still overseeing the restaurant group part-time — the systems I built carry that load now.
Today the company's daily work runs on a team of AI agents I designed and direct:
- Eight specialized AI agents run as departments — sales, finance, operations, HR support, research, and content — each with a defined role, a schedule, and accountability to a chief-of-staff agent that coordinates the whole floor
- A daily executive briefing: every department distilled into one ranked morning board of decisions I approve line by line
- Every signal in one place: email, messages, live financials, calendars, and market data flow into a shared knowledge base, so priorities come from evidence, not memory
- A revenue pipeline that never drops a follow-up: every prospect touched on schedule, every conversation logged, performance tracked on a scoreboard
- Autonomous cost and cash operations on live accounting data: transactions categorized, books reconciled, spend, receivables, and payables flagged before they become problems
- Overnight intelligence: market research, competitive analysis, and investment briefs on my desk before the workday starts
- Approval gates on everything external or financial: full autonomy where it's safe, human control where it matters
- I build production software through AI-assisted development: the dealership platform, its test-gated releases, and the live dashboards are systems I built and ship, not tools I configured
- I adopted the leading AI tools early and use them in production daily: Anthropic's Claude and Claude Code, OpenAI's Codex, and the MCP standard that connects AI to email, finance, and business systems. I don't evaluate these tools from articles — I run a company on them, and I teach them in plain language
And for clients: production software a dealership's team runs their paperwork on every single day, gated by 600-plus automated tests per release. Executive dashboards on live financial data. AI visibility assessments that drive real decisions.
Projects you can poke at.
A selection of systems designed, built, and operated under my direction. Each is real and in use; client identities are withheld in this public format, and every item can be demonstrated live in an interview.
Autonomous Business Operations Platform
A whole company's daily operations run by eight specialized AI agents structured as departments — sales, finance, operations, HR support, research, and content — coordinated by a chief-of-staff agent, with scheduled autonomy and human approval controls on all external and financial actions.
LocalRedact — Privacy Layer for Enterprise AI
Every company has the same security rule: "use AI, but never paste in company or customer data." LocalRedact makes that rule workable. LocalRedact removes sensitive values on the local machine before anything reaches a cloud AI model, then restores them locally afterward — teams get full AI capability while confidential data never leaves the building.
MOPS — Restaurant Operations Platform
An operations management system for multi-location restaurants: kiosk-based daily workflows with individual PIN accountability, every checklist and signoff attributable to a person, store, and time — replacing scattered forms, spreadsheets, and memory with an auditable operating record.
Document Automation Platform
A document-processing and compliance application built for a retail automotive client, in daily production use by their team. Every deployment is gated by a 600-plus scenario automated test suite.
Real-Time Financial Dashboards
Executive dashboards built on live accounting data — cash position, cost trends, receivables, and performance rendered as instant-read views for decision-makers, refreshed automatically from the books of record.
Search & AI Visibility Assessments
Analysis tooling that measures how a business appears across Google and AI answer engines (ChatGPT, AI Overviews), producing executive-readable reports with ranked, actionable fixes — used in live client engagements.
Every company hits the same wall with AI.
Leadership says "use AI." IT stands up the accounts and writes the security rules. And then… not much happens. People get a login and no playbook. A few motivated folks in a few departments build clever things on their own that never connect. The reports still get pulled by hand.
It's not a technology problem. It's an operations problem. Adoption is a system you build and run, like food safety or demand planning: clear workflows, real training in plain language, quick wins that earn trust, and someone accountable for making it compound.
My operating philosophy is simple: rebuild the organization around AI that can sense, decide, and adapt — with people setting the purpose and the judgment. That's not theory to me. My own company is a small working version of it, running live every day.
That's the job this role describes.
What I'd do first at iFIT.
Weeks 1–3Listen and map
Sit with S&OP, Member Care, Quality, Procurement, Logistics, and Customs. Find where the hours go: the manual pulls, the repeated questions, the reports someone rebuilds by hand every Monday. Rank by hours saved and risk. Map the ERP and CRM systems Operations already runs — anything I build integrates with them, not around them — and arrive with a clear view of what iFIT should build, buy, or avoid. Partner with IT and Data Engineering from day one: governance is theirs, and I build inside it.
Weeks 4–8Ship two quick wins
Two live agent deployments in real workflows, picked for visible payoff and low risk. Measured before and after, in hours and errors, not vibes. Nothing in production without the same discipline I use for client production systems: tests, approval gates, rollback.
Weeks 6–12Make it spread
An AI champion in each Ops function, trained in plain language on their own work, not generic demos. A simple intake so any team can request an automation. A monthly cadence with Ops leadership: what shipped, what it saved, what's next.
Day 90A roadmap with numbers on it
A prioritized, measured plan the COO can defend in any room — and the start of the business case for a permanent AI capability inside Operations.
Fifteen years of operations, one throughline.
Build the system, teach the team in plain language, measure it, and make it stick. First in retail, then across a multi-brand restaurant group, now with AI.
Founder — Bootstrap AI
AI workflow implementation for owner-operated businesses. Multi-agent operations system running the company's own daily work; production client software behind automated test gates; live financial dashboards; AI visibility assessments.
Founder & CEO — Clarity Leadership Team
Directed operations for up to 10 restaurant locations and 200+ employees across multiple brands. P&L, workforce planning, procurement, vendor contracts, customer experience. Led new-location launches end to end and introduced the group's first operations AI tooling. Now overseen part-time — the operating systems and AI tooling carry the day-to-day.
Chief Operating Officer — Morty's Cafe, Logan's Heroes, The Crepery
Scaled Morty's Cafe from one location to five and expanded The Crepery. Built the repeatable labor, ordering, inventory, and forecasting systems that made multi-unit growth work.
General Manager — Morty's Cafe · Store Leader — Expercom (Apple Premier Partner)
Ground-floor operations: labor budgets, inventory control, hiring and training, service standards, financial performance.
What I am, and what I'm not.
I'm not a career enterprise IT executive. If you want someone who's spent 20 years administering big-company systems, that's not me, and I'd tell you so.
Here's what I am: startup DNA all the way down. An operator who's run the messy real thing, who learned to build because nobody would build for businesses like mine, and who ships working systems in days — then hardens them with tests, approval gates, and rollback so they hold up. The posting calls it the Fixer standard: see the real problem, own the fix, make sure it still works tomorrow. That's been my job for fifteen years, and it's how I'd run the dotted-line network of AI SMEs — as their mentor and escalation point, not their ticket queue. The posting says outcomes over years of experience. This whole page is my answer to that line.
And so there's no question about focus: this role would be my full-time job, full stop. I built my companies to run without me — that was the whole point — and iFIT would get all of me.
And yes, the fitness part is real. I'm a runner. I work out every day. Building for a company that gets people moving would be the first time my work and my mornings point the same direction.
