Building AI-Native Commerce for Africa: Beyond the Hype
When you walk through Balogun Market in Lagos or any major market across African cities, you see real businesses. Merchants who've invested capital in inventory, rented shop space, hired staff. They're skilled entrepreneurs who understand their customers, their products, and their local markets intimately.
Yet when these same merchants try to reach customers online, they face a fundamental problem: they must pay foreign platforms — Facebook, Instagram, Google — for the privilege of advertising to their own local customers. That money leaves the continent permanently. It builds no African infrastructure, creates no African jobs, generates no insights that benefit African businesses.
I explored this economic inefficiency in depth during a conversation with SouqNews Television and Just Africa about AI-native commerce and Africa's digital economy. The discussion covered what "AI-native" actually means in practice, the economic case for local platforms, and what the next five years might look like.
Here, I want to expand on those themes, and get more specific about how AI-native commerce works, why it forces better infrastructure, the investment mindset shift it requires, and the honest challenges we face in building it.
What "AI-Native" Actually Means (And Why It Matters)
The term "AI-native" gets thrown around carelessly, but it describes something fundamentally different from what most people think of as e-commerce.
Traditional digital commerce is straightforward: you have a catalog, customers browse it, they make purchases, you record those transactions. Any intelligence — recommendations, analytics, customer insights, gets added later as features. It's essentially a digital version of a physical store catalog.
AI-native commerce works differently because AI systems are data hungry. They need comprehensive information to function, which forces you to build infrastructure properly from the beginning. You can't bolt AI onto poorly designed systems and expect it to work. If you don't build it right, it simply won't function.
What does this look like in practice? At Paylo, we're building what I call "a direct layering of intelligence over traditional sales."
Merchants still sell products. Customers still buy them. But the intelligence embedded in the platform transforms how these transactions happen.
Consider three fundamental differences:
1. Conversational access
You don't need to visit a website to browse anymore. You're thinking about what to buy for someone's birthday, you just ask, and the system gives you recommendations based on what it already knows about you, your budget, the recipient's preferences. The catalog becomes accessible through natural conversation rather than manual navigation.
2. Visual interfaces
We're implementing image search capabilities where you can photograph an item and immediately find merchants who stock it. Show a picture of shoes you like, and the platform finds local sellers who have similar products. This removes language barriers, terminology confusion, and the assumption of digital literacy that traditional text-based search requires.
3. Predictive intelligence
Rather than showing merchants what happened (trailing indicators), the system tells them what they should do next. "You're running low on your best-selling item" becomes actionable intelligence, not just data observation. The AI doesn't just report; it advises.
This matters because any form of data you can think of, it's going to be there. Business owners get comprehensive insights about their own operations plus anonymized, generalized data about their market area. They know what's selling well locally, what the average prices are, where demand is strongest — information that was previously only available to large enterprises with dedicated analytics teams.
The reality is, most merchants in Nigeria's informal markets are over 30. Many finished secondary school, some went to university, but they didn't study analytics or data science. They have basic digital literacy, enough to use WhatsApp, Facebook, maybe Instagram. That's about it.
You can't realistically expect millions of these people to go back to school and learn business intelligence just to run their shops better. It's not happening.
AI solves this by making the complexity invisible. The system takes years of sales information and business intelligence. It takes the kind of insights that would normally require expensive analysts, proper documentation culture, and compresses it into simple guidance.
The merchant doesn't need to understand 'inventory turnover rates' or 'customer lifetime value.' They just need answers to questions like 'Should I restock this?' or 'Why aren't people buying this product?'.
Effectively, they get an educated personal assistant who helps them participate in a more functionally optimal market without needing to learn anything new.
The Economic Case: Arbitrage, Proximity, and Keeping Value Local
Right now, the merchant who wins isn't necessarily the one with the best products or prices — it's whoever has the most money to spend on Facebook and Instagram ads.
You're selling shoes in Ikeja. Someone ten states away is also selling shoes. They're spending heavily on advertising, so their products show up first for customers searching in Lagos. Even though you're local, even though you could deliver faster and cheaper, even though your prices might be better because you're not factoring in massive ad spend — you lose visibility.
AI-native commerce changes this equation through intelligent arbitrage.
Here's how it works: The platform already knows your location. When someone nearby is looking for shoes, your products appear because proximity matters. Delivery will be faster. Shipping will be cheaper. And critically, since you're not spending money on advertisements, those costs aren't factored into your pricing.
But it gets more interesting.
Let's say you're a trusted seller — the platform has verified your product quality through past transactions and customer feedback. Through AI-driven matching, your products can be presented to buyers who value quality and are willing to pay a premium for it. To that buyer, your price is still cheaper than what they'd usually pay for equivalent quality elsewhere.
You make better margins. They get better value. Both sides win. This is arbitrage in its purest form: creating value by matching supply and demand more intelligently.
The economic implications extend beyond individual transactions. When advertising spend stays within the local digital economy instead of flowing to Silicon Valley, that capital can build African infrastructure, employ African engineers and generate insights about African markets that benefit local businesses while the multiplier effects compound over time.
Every major digital economy eventually builds local infrastructure because local platforms can profitably serve segments that foreign companies find too small, too complex, or too different from their core markets.
China has Alibaba and WeChat. Southeast Asia has Shopee and Grab. Latin America has MercadoLibre, now larger than Amazon in key markets.
The question isn't whether we build local commerce infrastructure, but who builds it, and when?
Intent-Based Discovery vs. Surveillance Advertising
One of the most frequent concerns I hear is about privacy. People have experienced the unsettling feeling of discussing something near their phone and then seeing ads for it everywhere. It's intrusive, and it's trained people to be suspicious of anything that seems to know too much about them.
AI-native commerce works fundamentally differently.
Traditional advertising is push-based: companies try to insert their messages into your attention, hoping something sticks. They track your behavior across platforms, build profiles, and target you whether you're interested or not.
What we're building is pull-based: ads here are based on intent. You only see recommendations for products when you're actually looking for something. Your own AI assistant (the one already on your phone — Siri, Google Assistant, ChatGPT) understands what you want. It searches our catalog to find matches. The platform helps connect your expressed intent with available supply.
This is a crucial distinction: buyers are looking for you; our platform just helps them find you. We are building a platform that offers discoverability and convenience, not surveillance. You wanted to find local merchants selling quality shoes at fair prices. The platform made that connection efficiently. Both parties benefited from the match.
This model respects users while serving merchants better than traditional advertising ever could. Merchants aren't paying to interrupt people who aren't interested. They're being discovered by people actively seeking what they sell.
- Conversion rates are higher.
- Customer satisfaction is better.
- Marketing spend becomes more efficient.
The infrastructure requirements are different too. Instead of building systems to track users across the internet, we're building systems to understand product catalogs deeply and match them against clearly expressed intent. It's a fundamentally more ethical model that also happens to work better economically.
The Constraint That Rewards Excellence
There's a counterintuitive advantage to building AI-native systems in Africa: the technology itself forces you to do things right.
AI systems are data hungry. They need comprehensive, clean, well-structured data to function. You can't fake it. You can't cut corners. You can't build a beautiful interface on top of broken infrastructure and expect the AI to magically work. If the foundation isn't solid, the whole system fails.
This prevents the kind of technical debt that plagues many platforms. When you're building traditional e-commerce, you can get away with suboptimal architecture. Manual processes can paper over data gaps. Humans can compensate for poor system design. It works… poorly, but it works.
With AI-native systems, those workarounds don't fly.
The AI needs reliable data about inventory levels, transaction history, delivery performance, customer feedback, pricing across markets. If any of those data streams is inconsistent or poorly structured, the AI's recommendations become unreliable, and the whole value proposition collapses.
So you're forced to build proper data collection systems. You're forced to structure information correctly. You're forced to integrate different parts of your platform seamlessly. You're forced to implement quality controls that actually work.
This constraint breeds innovation. But more than that, it rewards something our tech has desperately needed: taste and a spirit of excellence.
For too long, we've tolerated systems that barely work. Clunky interfaces. Poor user experience. Products built without genuine care for craft. You could get away with that in traditional software when you slap a nice UI on broken infrastructure and hope nobody notices.
That's not allowed anymore. You can't hide poor architecture when the AI needs clean, well-structured data to function. You can't fake quality when the system's intelligence depends on how well everything connects.
That commitment to building things properly, with attention to every detail. Suddenly it isn't just aesthetically pleasing anymore; it's operationally required.
This might be the constraint that determines which companies succeed in the coming years.
Democratizing Business Intelligence Through AI
For decades, sophisticated business intelligence was available only to companies that could afford analysts, data scientists, and enterprise software. Small merchants operated on intuition, informal knowledge, and whatever patterns they could discern from memory and experience.
AI fundamentally changes this equation because intelligence is no longer expensive. The computational power and algorithmic sophistication that once required enterprise budgets are now accessible through API calls costing fractions of a cent.
But raw capability isn't enough. The challenge is making that intelligence accessible, removing the barriers that traditionally existed between sophisticated analytics and merchants who haven't been trained to interpret them.
Consider a typical scenario: A merchant using traditional platforms might see dashboards showing conversion rates, customer lifetime value, inventory turnover, seasonal patterns. But understanding what those metrics mean and what to do about them requires analytical literacy most small business owners don't have.
The data is there, but it's not actionable.
With an AI business assistant, the interaction becomes different:
Merchant: "Should I restock this product?"
AI: "Yes. You've sold 15 units in the past week, which is 40% above your usual rate. You have 8 units remaining. Based on current demand, you'll run out in 4 days. I suggest ordering 25 units to cover the next two weeks plus buffer stock."
That's the same analytical sophistication of demand forecasting, inventory optimisation, pattern recognition, but delivered conversationally in language the merchant speaks: Hausa, Yoruba, Igbo, Swahili, Amharic, Zulu, French, Arabic.
The intelligence works regardless of which of Africa's hundreds of languages the merchant uses. Language is no longer a barrier to accessing world-class business intelligence.
They don't need to understand what "inventory turnover rate" means. They just need to know whether to reorder, why and how much.
The intelligence layer extends beyond individual business operations. Because AI systems are data hungry, they're collecting comprehensive market data. With proper privacy protections and anonymization, this creates valuable insights:
- "This product is the top seller in your area this month."
- "The average price for this item locally is ₦8,500; you're pricing at ₦9,200."
- "Weekend sales in your category are 40% higher than weekdays."
This is market intelligence that small merchants have never had access to. They're no longer operating blind, competing only on price because they don't know what else to optimize. They understand their competitive position, their market dynamics, their opportunities for differentiation.
The impact compounds over time. As merchants use the AI assistant, they learn to ask better questions. As they make data-informed decisions, they see better outcomes. As their businesses grow, the AI's recommendations become more sophisticated. Intelligence that was previously gatekept behind expensive tools and specialized knowledge becomes accessible to everyone building something.
At the end of the day, merchants just need more sales. They don't care about the underlying technology — AI, machine learning, algorithms. What they care about is whether this helps them sell more, operate more efficiently, and compete with larger players who've historically had all the advantages.
That's what AI-native commerce delivers: sophisticated capabilities at accessible costs while creating a level playing field where small businesses can compete on quality, service, and local advantage rather than just marketing budget.
The Deep Tech Investment Challenge
Building AI-native commerce infrastructure is fundamentally different from launching typical consumer apps, and that requires investors to think differently about metrics, timelines, and success criteria.
The old investment playbook goes something like this: launch quickly, find product-market fit, demonstrate traction, show hockey-stick growth, raise the next round. Move fast and break things. Prioritize user acquisition over infrastructure quality.
That playbook doesn't work for deep tech.
You cannot build AI-native systems with shallow foundations. You cannot demonstrate "traction" before the fundamental infrastructure exists. You cannot show product-market fit when the product requires months of careful engineering before it's ready for market.
Consider the development requirements:
- Multi-tenant architecture that can scale across thousands of merchants
- AI systems that need comprehensive data to provide value
- Integration with fragmented payment systems
- Offline-first design for inconsistent connectivity
- Quality control mechanisms that prevent fraud
- Visual search capabilities using computer vision
- Conversational interfaces that understand local contexts and languages
None of this happens in a weekend hackathon. You have to build it properly, which takes time, capital, and patience.
ChatGPT's 'product-market fit' came after years of research and billions in compute costs, not before. The traction followed the deep technical work.
This is why African AI infrastructure needs patient capital with different evaluation criteria. Instead of 'How many users do you have?' — ask 'How solid is your technical foundation?' Instead of 'What's your month-over-month growth?' — ask 'Are you solving the right fundamental problems?' Instead of 'When will you be profitable?' — ask 'What's your path to building defensible infrastructure?'
Not 'patient' in the Silicon Valley sense of waiting 18 months instead of 12.
Patient in the real sense of understanding that proper infrastructure might take 3–5 years before it scales explosively, but once it does, the defensibility and value creation are substantially higher than quick-flip consumer apps.
When you're building infrastructure rather than burning money to buy market share and maintain brand recognition, different metrics matter. Deep tech requires deep commitment. But the financial and social returns undeniably justify that patience.
Ecosystem Dependencies: Everything Must Grow Together
I'd love to paint a picture where AI-native commerce alone transforms Africa's digital economy. But the honest reality is more complex: you cannot just have a booming e-commerce sector and then every other thing is lagging behind. Everything needs to grow at once.
Our success depends on sectors we don't control:
Logistics and transportation: We can build the smartest AI for matching buyers and sellers, but if delivery takes two weeks because road infrastructure is poor, the experience suffers. Same-day delivery expectations are normal globally — we need the physical infrastructure to make that possible.
Fintech and payment systems: The rise of open banking in Nigeria is promising. Better access to credit facilities means more purchasing power, which drives e-commerce growth. But payment fragmentation remains a challenge — mobile money, bank transfers, cards, cash all coexist. Our systems have to handle all of it intelligently.
Power and connectivity: You can't run AI infrastructure without reliable electricity and internet. We design for offline-first operation and intermittent connectivity, but fundamental improvements in these areas would unlock much more.
Regulatory frameworks: We need governance that protects consumers and ensures accountability without stifling innovation. This is part of why I published the Nigerian Ethical AI Framework (NEAIF) to contribute to these policy discussions early, ensuring regulations enable, rather than hinder development.
I was asked during the interview whether Africa could adopt some of the advanced infrastructure we see globally — warehouses run entirely by robots, for instance.
"Automated warehouses? That's still a distant dream for Nigeria. The technology works. That part is solved. But the reality is you could wake up one day and find the whole warehouse empty. Innovation has never been in short supply, but the supporting ecosystem of security, power, and maintenance infrastructure isn't there yet."
We can't leap directly to the most advanced implementations. We have to build foundations first, ensure they work reliably, then layer on more sophisticated capabilities.
On the bright side, five years is more than enough time to make substantial progress if multiple sectors advance together. E-commerce can't boom in isolation, but if fintech, logistics, connectivity, and governance all improve incrementally, the compound effects are powerful.
The ecosystem approach also creates opportunities. When we solve hard problems of reliable payments with inconsistent infrastructure, delivery despite poor roads, quality control without formal systems, we build capabilities that become valuable elsewhere.
Solutions that work in Nigeria can work anywhere. That's exportable expertise.
What the Next Five Years Look Like
Predicting technology is difficult, but economic patterns are more reliable. Based on how other emerging markets developed digital infrastructure, here's what I expect for Africa:
Local platforms will dominate local commerce segments. Not entirely displacing foreign platforms, but commanding significant market share in areas they serve well. Merchants will prefer African-built tools because they're genuinely superior products for these use cases.
AI agents will become primary interfaces. Instead of apps with buttons and menus, conversational AI handling transactions. "Find me a red dress under ₦15,000" and it's done. Voice commerce in local languages. This eliminates literacy barriers and makes digital commerce accessible to populations currently excluded.
Platform consolidation will occur. Early experimentation, then winners emerging, followed by mergers and acquisitions. That's a healthy signal of market maturation. Competition will remain, but the field will narrow to platforms that demonstrate genuine product-market fit.
Government integration will advance. Digital identity, tax compliance, regulatory reporting all built into commerce platforms. This formalizes significant portions of the informal economy, which in Nigeria alone represents roughly 50% of GDP.
African platforms will serve global markets. Particularly African diaspora communities wanting authentic African products. The platforms we build for hard markets will prove superior for serving distributed, diverse communities — another case where constraint drives innovation with broader application.
But this vision has prerequisites:
Infrastructure investment must continue: Broadband access, payment systems, data centers, reliable power. These aren't nice-to-haves; they're foundational requirements.
Policy frameworks must enable without over-regulating: Collaborative governance that protects consumers while encouraging innovation. Policymakers need to engage with builders early rather than regulating after the fact.
Capital must be patient: As discussed earlier, building infrastructure takes time. Investors who understand that local platforms will eventually dominate because they serve local needs better. Those are the partners we need.
Builders must commit: This isn't about creating an app and hoping for acquisition. It's about building foundational infrastructure that compounds value over decades. That requires different thinking, different timelines, different measures of success.
The opportunity is real. The timing is right. AI has made sophisticated technology accessible enough that small teams can build world-class platforms. Africa's markets are large enough to support multiple successful platforms. The economic inefficiencies of the current model create clear openings for better solutions.
Five years from now, we should see thriving local commerce infrastructure, increasingly sophisticated AI capabilities, and a generation of entrepreneurs who built better products.
Building, Not Waiting
AI-native commerce isn't a distant possibility — it's being built right now, today, by teams across the continent who understand that our markets deserve infrastructure designed for our realities, but robust enough to evolve with the latest advancements.
At Paylo, we're working on one piece of this puzzle: giving merchants unified digital presence (website, marketplace storefront, AI business assistant) through a single platform. We're currently working with an initial cohort of merchants in Lagos and Abuja, learning from their experiences, refining features, and building the foundation for something that can eventually scale across the continent.
But this is bigger than any single platform. This is about whether Africa participates in building the next generation of commerce infrastructure or remains a consumer of systems designed elsewhere, paying perpetually for the privilege of using tools that don't quite fit our needs.
If you're a developer: Learn modern AI infrastructure. Not just how to call APIs, but how to architect intelligent systems. Understand multi-agent patterns, offline-first design, progressive enhancement. These skills are valuable and increasingly essential. Build for real problems in real markets, not theoretical scenarios.
If you're an entrepreneur: Start now. Perfect conditions are a myth. Focus on one problem, solve it exceptionally well, then expand. Think regionally from day one. Build relationships with customers who'll give honest feedback. Be patient with the technical complexity, but impatient with learning and iteration.
If you're an investor: Understand that different contexts require different metrics. The playbook that works for consumer social apps doesn't work for deep infrastructure. Look for technical soundness, founder expertise in the problem domain, and realistic timelines that account for the actual complexity of what's being built. Patient capital wins in infrastructure plays.
If you're a policymaker: Engage with builders early. Understand the technology before regulating it. Create frameworks that protect consumers and ensure accountability without stifling innovation. Invest in digital public infrastructure — identity systems, payment rails, broadband access. These are public goods that enable private innovation.
The conversation with SouqNews and Just Africa was valuable because it forced me to articulate these ideas clearly, respond to real questions about implementation, and think through the practical challenges we face.
Watch the full discussion here: SouqNews Television Interview
What we're building: Paylo provides merchants with unified digital infrastructure — branded websites, marketplace presence, and AI-powered business intelligence from a single platform. We're currently accepting merchant applications in Lagos and Abuja, expanding based on feedback and feature development.
Learn more: usepaylo.com | hello@usepaylo.com
I'm always open to discussing these ideas with other builders, investors, policymakers, and anyone thinking seriously about Africa's digital future. The work is hard, the timeline is long, the dependencies are real. But it's a massive opportunity that simply cannot wait for perfect conditions.