How East African Commerce Platforms Are Using AI Beyond Chatbots

 

When people think about artificial intelligence in retail, they often picture customer service chatbots answering questions about deliveries or recommending products. Those tools are certainly becoming more common, but they're only scratching the surface of how AI is changing retail across East Africa.

Sub-Saharan Africa loses more food between harvest and retail than any other region in the world, with roughly 23% of production never reaching a shelf, according to the UN's Food and Agriculture Organization. That single statistic captures why AI's real value in East African commerce has little to do with chatbots: the biggest wins come from fixing what happens between the farm and the store.

The region's most ambitious commerce companies are deploying AI much earlier in the value chain. They're using it to forecast demand, optimise delivery routes, improve inventory planning, reduce food waste, assess merchant creditworthiness, and make supply chains more efficient. These are the systems customers rarely see, yet they're often the ones creating the biggest business impact.

That shift matters because retail in East Africa faces a unique set of operational challenges. Supply chains remain fragmented, demand can fluctuate dramatically, logistics costs are high, and retailers frequently operate with thin margins. For businesses navigating those realities, AI isn't just another technology trend. It's becoming a practical tool for making better decisions at scale.

Companies like Twiga Foods, Wasoko, and Copia illustrate how AI and data-driven technologies are quietly reshaping East Africa's retail ecosystem, while Chpter (born out of the now-shuttered MarketForce) shows how that same instinct is being repurposed for social commerce. While each company approaches the challenge differently, they all point to the same conclusion: the future of retail isn't being built around smarter chatbots. It's being built around smarter operations.

Why AI Is Becoming Essential in Retail

Retail has always been a data business.

Every sale, delivery, customer preference, and stock movement generates information. Until recently, however, most retailers lacked the tools to turn that information into meaningful business decisions.

That's changing quickly.

Cloud computing has become more accessible. Mobile money has digitised millions of transactions. Businesses are collecting far more operational data than ever before, while advances in machine learning allow that data to be analysed almost instantly.

For East African retailers, the benefits are tangible.

Instead of relying solely on historical trends or instinct, businesses can now predict which products will sell faster, identify inventory shortages before they happen, optimise delivery routes to reduce fuel costs, and allocate stock more efficiently across multiple locations.

This isn't simply about automation. It's about improving decision-making. In an industry where profitability often depends on keeping the right products on the right shelves at the right time, better decisions can translate directly into higher margins.

Twiga Foods: Using AI to Build Smarter Food Supply Chains

Few companies demonstrate this better than Twiga Foods.

Founded in Kenya in 2014, Twiga set out to modernise one of East Africa's most fragmented industries: fresh produce distribution. Traditionally, food moves through multiple intermediaries before reaching retailers, creating inefficiencies, higher prices, and significant post-harvest losses.

Twiga's digital platform connects farmers directly with vendors while using technology to coordinate procurement, inventory management, warehousing, and last-mile distribution. Behind that platform is an increasing reliance on predictive analytics: rather than simply reacting to orders, Twiga analyses purchasing patterns, seasonal trends, historical sales, and supply availability to anticipate demand before it materialises, which helps the company plan procurement more accurately, minimise food waste, and improve product availability for retailers. AI also supports route optimisation across Twiga's logistics network, an effort the company says has helped cut delivery costs.

More recently, Twiga has pivoted from being purely a fresh produce supplier toward a broader FMCG distribution platform, acquiring several local distribution companies in 2025 to widen its footprint, after a period of layoffs and leadership changes tied to a tougher fundraising environment.

The broader trend is clear. In food retail, AI isn't replacing warehouse workers or delivery drivers. It's helping businesses move highly perishable products more intelligently, reducing waste while improving profitability throughout the supply chain.

As we've explored in our article on the East African HealthTech companies building the future of healthcare, this behind-the-scenes application of AI mirrors what's happening in other industries. The greatest impact often comes from strengthening infrastructure rather than replacing human interaction.

Wasoko: Turning Retail Data Into Better Business Decisions

If Twiga shows how AI can transform agricultural supply chains, Wasoko demonstrates its value in business-to-business commerce, though its story has taken a turn since its early growth days.

Originally launched as Sokowatch in Kenya in 2013, Wasoko built its platform around helping informal retailers order inventory digitally and receive deliveries directly to their shops, using transaction data to understand purchasing behaviour, identify demand trends, and optimise inventory allocation. AI and advanced analytics also underpin one of Wasoko's most valuable services: embedded finance, where the company analyses transaction records, purchasing consistency, repayment behaviour, and inventory turnover to assess merchant credit risk using alternative data rather than conventional collateral.

In 2024, Wasoko merged with Egypt's MaxAB in what's considered one of the largest tech mergers on the continent, forming a combined group serving more than 450,000 merchants. That consolidation came alongside real strain: Wasoko exited Uganda, Zambia, and Senegal as part of the process, and founder Daniel Yu stepped down as co-CEO in 2025. The combined MaxAB-Wasoko Group now operates across five markets, Egypt, Morocco, Kenya, Tanzania, and Rwanda, with the business increasingly leaning on its fintech and embedded credit products to improve margins.

This reflects a broader shift happening across East Africa's technology ecosystem. As we discussed in why East Africa's enterprise SaaS market is finally taking off, businesses are increasingly using software not just to digitise operations, but to generate better data. That data, in turn, creates entirely new business opportunities, from smarter inventory management to more accessible credit, even as individual companies face a difficult road to profitability.

MarketForce and Chpter: From Digitising Informal Retail to Automating Social Commerce

MarketForce's story shows how quickly the region's retail-tech landscape can shift.

Founded in Kenya in 2018 by Tesh Mbaabu and Mesongo Sibuti, MarketForce built RejaReja, a B2B platform that let informal retailers order inventory digitally, access working capital, and accept digital payments, reaching more than 270,000 small retailers across Kenya, Nigeria, Uganda, Tanzania, and Rwanda at its peak. Every digital order, payment, and inventory update generated valuable operational data, allowing merchants to understand buying patterns and make more informed purchasing decisions than handwritten records or intuition alone allowed.

But in April 2024, facing a difficult fundraising climate, MarketForce shut RejaReja down entirely. Its founders instead spun out a new venture, Chpter, which applies the same underlying instinct, using data to bring structure to informal commerce, to a different channel: AI-powered chat automation for businesses selling through WhatsApp and Instagram. (Mbaabu and Sibuti have since moved on again, launching a digital banking platform called Cloud9.)

The broader lesson extends beyond any one company's fate. The digitisation of informal retail creates the foundation on which AI can eventually deliver greater value. Businesses first need reliable digital data before machine learning models can forecast demand, optimise inventory, or automate recommendations. In other words, digitisation comes first. AI follows, sometimes in a completely different business than the one that generated the original data.

Even Traditional Retailers Are Embracing AI

The AI transformation isn't limited to startups.

Large retailers operating in East Africa are increasingly embedding artificial intelligence into everyday operations. Carrefour's Kenyan stores are franchised and operated by Majid Al Futtaim, which has invested group-wide in AI and data platforms, including a Microsoft-built intelligent data system that supports real-time sales forecasting and reporting across its Carrefour markets in the Middle East and Africa. Rather than relying solely on historical sales reports, these systems help predict demand, improve stock availability, and reduce waste across stores.

It's another reminder that AI's greatest commercial value often lies behind the scenes. Customers may never notice that a supermarket stocked the products they needed because an algorithm predicted demand days earlier. But retailers notice through lower operating costs, fewer stockouts, and stronger margins.

Four Ways AI Is Reshaping East African Retail

AI is improving demand forecasting.

 Retailers no longer have to depend entirely on historical sales or instinct. By analysing purchasing behaviour, seasonality, promotions, weather patterns, and inventory levels, AI helps businesses anticipate demand more accurately, reducing both shortages and excess stock.

AI is reducing supply chain waste. 

Food waste and inefficient logistics remain major challenges across East Africa. Companies like Twiga are using predictive analytics and route optimisation to improve procurement and distribution, helping products reach retailers faster while reducing unnecessary losses.

AI is making smarter financial decisions possible. 

Transaction data has become a valuable business asset. Commerce platforms can increasingly use merchant purchasing behaviour and repayment history to support credit assessments, giving small businesses access to financing that traditional banking systems may not provide.

The most valuable AI is often invisible. 

The biggest AI success stories aren't flashy customer-facing tools. They're the systems quietly helping businesses forecast demand, manage inventory, optimise logistics, and improve operational efficiency. Customers may never realise AI played a role, but they experience its benefits through better product availability, faster deliveries, and more reliable service.

The Obstacles

AI's potential is significant, but widespread adoption still faces meaningful obstacles, and the recent struggles of Wasoko, Copia, and MarketForce are a reminder that smart technology alone doesn't guarantee a sustainable business.

Data quality remains one of the biggest challenges. Machine learning models are only as effective as the information they're trained on, and many businesses are still transitioning from fragmented manual processes to fully digital operations.

Infrastructure also varies across the region. Internet connectivity, cloud adoption, and digital maturity differ considerably between markets, making it difficult to deploy advanced AI solutions consistently.

Cost is another consideration. While enterprise retailers can invest in sophisticated analytics platforms, many small businesses continue to operate with limited technology budgets.

Perhaps the biggest challenge, however, is organisational rather than technical. Retail managers still need to trust AI-generated recommendations before acting on them. Building that confidence takes time, particularly in businesses where experience and intuition have guided decision-making for decades.

As we've discussed in the cybersecurity companies building East Africa's digital trust infrastructure, stronger digital systems also bring greater responsibility. As retailers collect more customer and operational data, protecting that information becomes just as important as analysing it.

The Future of AI in Retail

The conversation around AI in retail is gradually changing. Instead of asking whether retailers should adopt AI, the more important question is where it creates the greatest business value.

Across East Africa, the answer increasingly lies behind the customer interface. From Twiga Foods' smarter supply chains and Wasoko's data-driven (if turbulent) commerce platform, to the lessons of Copia's collapse and MarketForce's pivot into Chpter, the region's retail sector is becoming more intelligent one operational decision at a time, even as individual companies rise, merge, or fold along the way.

The companies leading this transformation aren't simply building better shopping experiences. They're creating the digital infrastructure that makes retail more efficient, more resilient, and more scalable. As East Africa's retail economy continues to grow, businesses that combine strong operational data with AI-driven decision-making are likely to gain an increasingly durable competitive advantage. And as that demand grows, so too will the need for cloud infrastructure, enterprise software, and the skilled professionals behind it, many of whom work in the tech jobs AI is least likely to replace in East Africa.

Chatbots may be what customers notice, but they won't be what decides who wins. That will come down to the systems running quietly in the background, and to which companies last long enough to keep refining them.