With over a decade of experience at the intersection of product management, strategy consulting, and technology, Olayinka Omolere has helped build and scale products across industries, continents, and company sizes—from startups to global tech giants.

A UC Berkeley MBA and former strategy consultant who has worked with organisations ranging from billion-dollar pension funds to Gates Foundation initiatives, Olayinka brings a unique systems-oriented perspective to product development. His work spans financial inclusion and AI-first product frameworks adopted across multiple organisations, mentorship at leading accelerators like Techstars, and product management on platforms serving tens of millions of users.

In this interview, Olayinka shares insights into how product building changes across company sizes and offers principles and approaches that help product teams design resilient, scalable solutions in various contexts.

You’ve worked at both startups and global tech companies. What’s the biggest shift in product development between the two?
One of the most striking differences is how intuition and decision-making are treated. At a startup—especially those with fewer than 50 people—product managers often need to move fast with limited data. Intuition, grounded in user understanding, becomes a valuable tool. In fact, you often don’t have time to wait for complete data before making a call.

By contrast, at larger companies—especially those serving millions of users—you are expected to build consensus through data and documentation. Even seemingly simple ideas require validation, stakeholder buy-in, and often weeks of analysis. I recall an instance where gathering the data to justify a small yet meaningful change took longer than the engineering effort to build the feature itself.

Startups foster natural camaraderie—shared ownership, celebrating small wins, and people wearing multiple hats. In larger organisations, you have to work harder to maintain that spirit of creativity while navigating complex stakeholder ecosystems. The key insight is that product velocity isn’t just about speed. It is about optimising the entire system of decision-making, resource allocation, and risk management for your specific organisational context. Whenever you succeed in doing that in a large platform environment, you not only improve team morale, you set an example that can resonate across regions and business units.

AI is everywhere now. It’s the defining technology of our time. But honestly, many companies are just slapping chatbot wrappers on everything without extracting real value. Some say Africa is behind in this space. You’re working at the forefront in Silicon Valley. What innovative approaches do you have for building truly AI-native products that create the future?
You are absolutely right. Most AI implementations are surface-level. The real opportunity isn’t building another ChatGPT wrapper, but embedding intelligence into the core product architecture.

Here is how I think about it: instead of asking, “How do we add AI to this product?”, you start with, “If we already knew exactly what users needed, how would we design this?” Then I work backwards to what solves a real user problem and is technically feasible. My computer science background helps me think about this systematically rather than just following trends.

One area I think has huge potential is building systems that predict user intent multiple interactions ahead and preload the optimal experience. This goes beyond traditional machine learning recommendation engines. It is about building context‑aware systems that adapt the product itself in real time based on predictions of user intent.

The approach I’ve seen succeed has three layers: AI models that understand user context, systems that modify product behaviour based on that understanding, and feedback loops that improve predictions over time. Implementations of this can reduce user friction and increase task completion rates.

The opportunity for African and global teams is massive. You can build AI-native from the ground up without legacy constraints. While established companies struggle to retrofit AI into existing systems, African teams can architect products where intelligence is fundamental. That’s not being behind. That’s leapfrogging to the next generation of product design.

You have developed some innovative approaches working across scales—from mentoring startups at Techstars to building products at leading social media companies. For product managers here in Nigeria and across Africa, what’s one final key insight from your work that might not be obvious to them?

The biggest insight I’d share is to design for constraints as a feature, not a limitation. Most product managers think constraints—like limited connectivity, diverse device capabilities, or irregular income patterns—are problems to solve around.

I have learned that designing directly for these constraints often leads to more elegant, resilient solutions that work better for everyone. This approach not only serves users in challenging environments but also makes the products faster and more reliable for all users globally.

The framework includes three core principles I developed: graceful degradation (ensuring basic functionality works everywhere), contextual optimisation (adapting features based on local conditions), and inclusive defaults (designing the baseline experience for the most constrained users).

I have seen companies implement these principles and achieve better user retention in emerging markets while simultaneously improving their core product metrics. This constraint-first thinking is a competitive advantage. When you design for the most challenging conditions from the start, you build products that are inherently more robust, accessible, and scalable. Nigerian product managers have an attractive opportunity to lead this approach rather than just adopting solutions built elsewhere.