Viktoriia Grygorenko
CEO at The Playa
Profiled #8
AI use case: Build vs. buy
Sooner or later every operator reaches the moment when AI personalization lands on the roadmap and someone says "we could build this ourselves." In this episode of Profiled, Gali Hartuv and Viktoriia Grygorenko take one concrete use case — lobby personalization — and work through the build vs. buy decision in practice, including a live walkthrough of the cost model you can run on your own numbers.
You will learn
- The three questions worth answering before anyone opens a spreadsheet
- What building actually requires beyond engineering: domain expertise, research, product ownership and infrastructure
- A full walkthrough of the calculation — team, hiring time, cloud and catalogue costs, ramp-up, confidence factor and exit cost
- Where a hybrid path fits: start with a vendor, buy out the technology once the uplift is proven
Speakers
Time is the variable that decides it
Buy starts capturing uplift around month three; build around month nine. Across a three-year case, that head start moves more money than the difference between the cost lines — which is why buy so often wins on the numbers.
Build is a strategic decision, not a financial one
Building makes sense when the capability must stay under your control in five years, has a named owner, and survives a bad quarter on the roadmap. If it doesn't survive that test, you're not treating it as strategic.
In-house is a cost centre, not a project
Five months is a launch date, not an end date. New providers, new games, model drift and constant iteration keep the team on the payroll — and if the person who designed it walks out, you lose time you can't buy back.
Technology is an enabler, not a solution
A model only performs when someone with real domain expertise defines the target, the constraints and the signals it learns from. Vibe coding skips exactly that part — and cheap becomes expensive when the shortcut costs you a year.
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