Most companies have been told AI will change everything. Very few have been shown how, in a real business, with real money on the line. We can show you, because we did it to our own.
With AI, almost anything is possible. Not everything is worth doing.
We're Shashank and Krupank BK. We're brothers, and for more than five years we've run a profitable sports management software company with no outside investors. Over the last stretch we rebuilt that company with AI: it went from ten people to three, and each of us now gets more than three times as much done.
This is how we did it, what we learned, and why we started One Three Nine to help other companies do the same.
The company we built
Our software runs registrations, payments, scheduling and communication for sports clubs. We never raised money, so every cost came out of our own margin, and every decision had to pay for itself.
By last year we had ten people and a familiar problem. We were moving slower than we wanted and spending more than the business should. At the same time, new AI-native companies in our space were shipping faster than we could. We weren't built that way. We had to keep up, or fall behind.
We didn't start with the tools
The temptation with AI is to start with the tools: buy licences, run pilots, see what sticks. We started with a harder question. Of everything this company does, what is actually worth changing, and in what order?
Every idea went through the same four questions.
Return. What would it give back, in time, money or customers?
Timing. Is the business ready for it now, or does something else need to happen first?
Effort. How hard would it be to do well, not just to demo?
Fit. Does it serve the goals we already have, or is it a distraction?
Plenty of ideas didn't survive. The ones that did gave us an order.
What we changed, one function at a time
Sales research came first, because the return was clearest and the risk was low. Researching a prospect used to take 15 to 20 minutes. Now an engine finds every club in a region, reads its website, identifies the software it uses and writes one line on why it should switch, in seconds. (Read how the research engine works.)
Marketing came next, with a content engine that drafts search-targeted articles for a person to approve. Then engineering: AI coding agents, held to the same written standards as our own engineers, built a full memberships product in about a week, against a three-month estimate. (Read the build in detail.)
Support and the back office followed. An agent trained on our help centre, documentation and past conversations now answers about 70% of questions without a person, and reconciliation runs automatically.
What we learned
Not everything is worth doing. With AI, almost anything is possible. The skill is choosing what to do first, and what to skip.
Phases beat big bangs. Each change had to work, measured, before the next one started. That kept the risk small and gave every step a result to point to.
The team has to own it. Every system needs an owner, written standards and a review step. If it only works while the person who built it is watching, it doesn't work.
What worked a year ago is often out of date. The tools change month to month. We review our own setup regularly and replace what no longer earns its place.
What we see in other companies
When we talk to leadership teams and private equity operating partners, we hear the same things. AI has been mandated almost everywhere. Pilots have been run and licences bought. But few companies have changed how the work actually gets done, and most leadership teams have never seen it done in a business like theirs.
The gap isn't access to tools. It's knowing what's worth doing, in what order, and how to make it last after the consultants leave.
Why One Three Nine
The name is how the change compounds. Prove one: start with the function where the return is clearest. Fund three: the time and money it frees up pay for the next three. Scale to nine: by then your team runs the method itself, and it spreads across the business. Each phase pays for the next. That's how our own company got here, and it's how we'll work with yours.
We started One Three Nine to help other companies make that journey, and we hold ourselves to four commitments.
Everything is tested on ourselves first. If a method hasn't worked in our own business, it isn't in our advice.
The founders do the work. No hand-offs to junior consultants. You work with the two of us.
We'll tell you what isn't worth doing. Even when it means a smaller engagement.
You stay in control. Your team builds and owns the result. We set up, coach and review, so nothing depends on us.
Who we are
Shashank held several Head of AI roles at IBM, across technical sales, product management, competitive strategy and enablement, each with global responsibility, and built and scaled IBM's Data Science Platform. He holds an MBA and a Master of Management Analytics from the Smith School of Business at Queen's University. Krupank is a product leader who has worked at a range of tech companies and studied at the University of Waterloo; he leads product and design in our company. (More about us.)
Who we work with, and how to start
We work with leadership teams at established companies, and with the private equity funds and family offices that own them, across Canada and the US. There are four ways to work with us: a one-day Leadership Workshop, an AI Priority Roadmap, a Guided Build and Ongoing Advisory.
Most start with the workshop. Some start with a single 45-minute call. If you've been told AI will change everything and you'd like to see what that looks like in a real business, we'd be glad to show you.
See it in practice
The stories behind the numbers in this article, from our own company.