AI Logistics in East Africa: Lessons Ugandan SMEs Can Use

African businesses can pay up to four times the global average to move goods. That single fact explains why customers complain about “expensive delivery,” why supermarkets run out of stock at the worst times, and why many SMEs in Uganda struggle to grow beyond a few locations.

Now zoom out to the investment news: Google and Speedinvest backed Kenya’s Leta, a logistics SaaS company using AI to reduce delivery costs. The funding matters, but the bigger story is what it signals—serious money is betting that AI-powered logistics can fix the messy, expensive realities of moving products in our region.

This post is part of our “Enkola y’AI Egyetonda Eby’obusuubuzi n’Okukozesa Ensimbi ku Mobile mu Uganda” series—where we look at how AI is reshaping business operations and mobile money workflows in Uganda. Leta is a clean case study: it shows how AI can turn logistics from a constant headache into a measurable, optimizable system—often managed from a phone.

Why logistics costs are so high (and why AI targets the real problem)

High logistics costs in East Africa aren’t mainly caused by fuel prices; they’re caused by inefficiency you can’t see on a receipt. AI helps because it’s good at detecting patterns in messy operational data and making thousands of small decisions faster than humans can.

A typical SME delivery chain in Uganda has common cost multipliers:

• Under-loaded trips (a van leaves at 55% capacity because the schedule is manual)
• Poor routing (drivers choose familiar routes, not the cheapest or fastest)
• Late deliveries (missed delivery windows create re-deliveries and customer churn)
• Unpredictable demand (you buy too much stock “just in case” or run out unexpectedly)
• Weak visibility (you don’t know where a driver is until they call)

Here’s what works about the AI approach: it doesn’t need a perfect world. It performs well when the environment is complicated, roads vary, customers change plans, and demand spikes (December is a good example—holiday buying and travel put pressure on delivery schedules).

Snippet-worthy truth: Logistics gets expensive when decisions are made with guesswork. AI reduces cost by replacing guesswork with probabilities and optimization.

What Leta represents: AI as “operating system” for deliveries

Leta is described as a logistics SaaS provider that uses AI to make logistics cheaper. The important takeaway isn’t the brand name; it’s the model: software that sits above fleets, orders, and routes and continuously improves how deliveries happen.

What “AI logistics software” actually does

Most people hear “AI in logistics” and imagine robots. The reality is more practical:

1. Route optimization: Choosing delivery sequences that reduce time and fuel.
2. Load planning: Matching orders to vehicles so you send fewer trips.
3. Dispatch automation: Assigning deliveries to drivers based on capacity, distance, and service-level rules.
4. Demand forecasting: Predicting where stock will be needed so you don’t over-ship or under-ship.
5. Exception handling: Spotting when a delivery is likely to fail (traffic, delays, repeated customer rescheduling) and recommending a fix early.

If you run a distribution business in Kampala, Mukono, Wakiso, Mbarara, or Gulu, you can already feel the value: fewer wasted trips, fewer phone calls, fewer “we’ll try again tomorrow.”

Why investors care (and why Ugandan SMEs should too)

Google and Speedinvest don’t invest because something is “nice.” They invest when they see a scalable system. AI logistics scales because:

• Each delivery produces data (time, location, success/failure, costs)
• AI systems learn patterns across deliveries
• The product improves as usage grows

For Ugandan SMEs, the lesson is blunt: the winners will be the businesses that treat logistics as a data problem, not a hero-driver problem.

Uganda’s mobile-first advantage: where AI logistics meets mobile money

Uganda’s edge is not fancy infrastructure. It’s adoption of mobile workflows—especially mobile money—for payments, collections, and coordination. AI logistics becomes far more powerful when it connects to the payment reality on the ground.

Practical integration points (what to build or demand)

If you’re evaluating logistics tools—or building internal systems—prioritize features that tie delivery performance to cash flow:

• Cash-on-delivery (COD) verification: driver confirms delivery, payment status updates instantly
• Mobile money reconciliation: deliveries matched to transactions automatically (reduces “missing money” disputes)
• Proof of delivery: photo, signature, GPS stamp—stored and searchable
• Customer messaging: automated SMS/WhatsApp delivery windows and delay updates

This is where our topic series connects directly: AI isn’t only about “big tech.” It’s about making mobile-based business operations more predictable—and predictable operations are what banks, suppliers, and serious customers trust.

A December reality check: seasonality punishes manual logistics

December in Uganda is a stress test. Retail spikes, upcountry travel rises, and many teams operate with skeleton staff. Manual planning breaks under pressure.

AI-based planning handles this better because it can:

• forecast volume increases based on past weeks
• suggest temporary fleet needs earlier
• prioritize high-value or time-sensitive drops

You don’t need perfection; you need fewer failures during peak season.

Case-study thinking: how a Ugandan SME can copy the “Leta playbook”

You might not buy the same tool that a Nairobi startup sells. Still, you can adopt the same operating principles.

Step 1: Standardize your delivery data (start small)

AI can’t optimize what you don’t record. Begin with a simple, consistent structure:

• Order ID
• Customer location (even if it’s a pinned map point)
• Promised delivery window
• Vehicle/driver assigned
• Delivery outcome (delivered, rescheduled, failed)
• Reason codes (customer unavailable, no cash, wrong address, vehicle issue)

Most teams can capture this using a phone-based form or lightweight app. The win is not “digital transformation.” The win is that after 30 days, you can finally answer: What’s actually causing our delivery costs?

Step 2: Optimize one corridor, not the whole country

Most companies get this wrong: they try to fix everything at once. Pick a corridor you run often, for example:

• Kampala → Mukono
• Kampala → Entebbe/Wakiso
• Kampala → Jinja

Measure baseline metrics for 2–4 weeks:

• cost per drop
• drops per route per day
• on-time delivery rate
• failed delivery rate

Then apply simple rules (even before “full AI”): cluster deliveries by location, standardize dispatch times, set customer confirmation messages. When that’s stable, route optimization tools actually work.

Step 3: Tie delivery success to payment success

If your business depends on mobile money collections, treat delivery and payment as one event.

A strong process looks like this:

1. Customer receives delivery ETA
2. Driver confirms arrival
3. Payment collected (mobile money or POS)
4. Proof of delivery stored
5. Transaction automatically matches order

When you can report “delivery-to-cash time” (hours, not days), you become more resilient and more fundable.

Snippet-worthy truth: If you can’t reconcile deliveries to payments quickly, you don’t have a logistics problem—you have a cash flow problem.

What to look for in an AI logistics tool (so you don’t waste money)

Not every “AI” product is useful. For Ugandan SMEs, value comes from very specific capabilities.

Non-negotiables

• Works well on mobile: dispatch, driver updates, and proof of delivery must be phone-friendly
• Offline tolerance: poor network happens; the system should sync later
• Transparent pricing: per vehicle, per delivery, or per month—no hidden “implementation” traps
• Localizable address handling: supports landmarks, pinned locations, and incomplete addressing

AI features that actually move the numbers

• Route optimization with constraints (delivery windows, vehicle capacity, priority customers)
• Predictive alerts (flagging likely late deliveries early)
• Reason-code analytics (top failure reasons by area/driver/customer type)
• Demand forecasting (simple forecasts for replenishment planning)

Red flags

• “AI” that’s just a dashboard with no decision support
• tools that require perfect addresses to work
• systems that can’t export your data (vendor lock-in hurts SMEs)

People also ask: quick answers for Ugandan business owners

Can small businesses afford AI-powered logistics?

Yes—if you start with the cost you already have. If routing and failed deliveries waste even one vehicle-day per week, a lightweight system pays for itself quickly. The trick is buying for measurable outcomes (cost per drop, on-time rate), not for “features.”

Do I need my own fleet to benefit?

No. AI logistics also works for businesses using third-party riders and trucks. You still need standardized order data and performance tracking. The tool becomes your “control tower.”

Will AI replace dispatchers and drivers?

It replaces repetitive planning and guesswork, not the relationships. A good dispatcher becomes more valuable when they manage exceptions and service quality instead of drawing routes on paper.

What this means for Uganda’s next wave of growth

Google and Speedinvest backing Leta is a sign that AI logistics is becoming standard infrastructure in African commerce—like mobile money did. For Uganda, the opportunity is clear: businesses that connect AI-driven operations to mobile-based payments will deliver faster, waste less fuel, and collect money more predictably.

If you’re following this “Enkola y’AI…” series for practical steps, here’s the next move I’d take this week: pick one delivery corridor, standardize your delivery and payment data for 30 days, and calculate your true cost per successful delivery. That number becomes your baseline for automation.

The question worth sitting with is this: when your competitors start running AI-optimized delivery operations from their phones, will you be competing on product quality—or apologizing for delays and missing stock?