Product manager first. AI is how I execute faster, not what I am.

My path into product management didn't start with a PM title — it started with running a business. Since 2021 I've built sales operations and B2B commercial infrastructure from the ground up at TSA World Technologies, and separately founded a community relief NGO, Rarsfound. Both taught me the same lesson from different angles: you make product and resourcing decisions with real constraints and no safety net, and you own the outcome either way.

The formal PM roles came alongside that, not after it. At Mi-Pal Technologies I ran end-to-end discovery and delivery for a B2B property platform and a hospitality app, and at Edutech Global I owned scope and cross-functional delivery for a student/admin portal. Both gave me structured, quantified outcomes — a 28% sprint-velocity lift, a 35% reduction in onboarding drop-off — inside a genuinely disciplined Agile process.

A Professional Diploma in Software Engineering (NIIT, 2023) sits underneath the product work, and it changes how I operate as a PM: I can read a technical trade-off conversation instead of translating it, scope an API integration (Paystack payment-splits at Edutech) with a realistic sense of the engineering cost, and — most directly — direct AI coding agents to actually ship working software (PadiPay) because I understand enough of what they're producing to review it critically rather than rubber-stamp it.

The engineering background shows up as a working habit, not a talking point: I authored full PRDs (ArcOunt, Quik) with the underlying data models and technical constraints already reasoned through, scoped RICE-prioritized backlogs with explicit, reasoned framework overrides, and directed AI coding agents through three build iterations of a working marketplace app (PadiPay). In every case, modern AI-assisted tooling accelerated the drafting and the build — the framing, the trade-offs, and the decision to ship or hold stayed mine to make.

I also run a self-hosted agent gateway (OpenClaw + Ollama) with 62 live commands on Telegram — operated and debugged continuously since 2025, not a one-off tutorial. That's given me first-hand, operational familiarity with how these systems actually behave in production (uptime, provider routing, failure modes), which is what makes me a useful partner in a technical trade-off conversation rather than someone who needs the conversation translated.

Certifications in Product Strategy & Discovery (Product School), Advanced Product Development (Utiva Product School), and Agile Project Management (Udemy) gave me the frameworks; the self-directed work in Agentic AI Systems is where I've spent the most recent, most intensive learning effort, because it's the part of the field moving fastest.

Founding and running an NGO alongside a commercial operations role and formal product management work forced a specific leadership habit: be honest about what's actually true this week, not what the plan said should be true. That shows up in how I write product strategy too — the Quik documentation names explicitly where a framework's mechanical answer conflicts with the real goal, and says so instead of quietly picking one.

Problem first, framework second, AI third. Every project in my portfolio starts with a written problem statement and a persona before any solutioning — the AI accelerates drafting and iteration once the framing is right, but it doesn't originate the framing. That ordering is deliberate, and it's the difference between 'I used AI to build a product' and 'I used AI to build a product I'd already decided was worth building.'