From port congestion and customs delays to power outages and rising fuel prices, South Africa’s logistics operators face growing pressure on margins and performance. These challenges are compounded by global competition, infrastructure bottlenecks, a shortage of skilled drivers, and widening technology gaps.
But a shift is underway. New, affordable AI tools – from predictive systems to agentic automation – are giving local freight businesses, importers and logistics agents the power to streamline operations, reduce friction and rethink how they scale.
“What’s powerful about AI in logistics is that it closes the capability gap,” says Dylan Govender, head of supply chain at Investec Business Banking. “Smaller operators can now make the same data-driven decisions as global players, without needing the same infrastructure.”
How AI Is rewiring global logistics
Around the world, AI is already transforming logistics workflows by solving longstanding operational inefficiencies. Generative AI tools are now forecasting weather patterns, customs backlogs and port activity, giving logistics firms early warnings about potential delays or rerouting needs.
Real-time tracking systems are detecting shipment anomalies and reoptimising delivery paths mid-transit, improving reliability while reducing fuel and time waste.
At the admin layer, automation tools are streamlining customs processing by extracting and verifying data from waybills and cargo documents, cutting paperwork delays and improving compliance.
PepsiCo built a digital twin of its distribution centres using the NVIDIA AI to improve throughput and reduce both downtime and energy consumption.
Alaska Airlines are also using NIVIDA AI capabilities to cut emissions and fuel use through optimised logistics and delivery paths, while DHL has invested in AI for last-mile delivery optimisation across routing, fuel efficiency and vision picking technologies.
An Eastern European SPAR retailer used AI-powered forecasting and automated ordering to cut inventory by 20% and boost on-shelf availability from 86% to 94%. The system halved ordering time and improved decision-making across 19,000 items.
What is a digital twin?
A digital twin is a real-time virtual replica of a warehouse, reflecting layout, stock flow, equipment movement, and environmental conditions.
Using AI and simulation tools, companies can test different workflows, robot routes or layout changes in the digital environment, before making expensive real-world changes.
NVIDIA’s Omniverse platform is leading this innovation, helping companies like KION Group simulate warehouse scenarios at scale.
At GTC 2024, NVIDIA demonstrated how digital twins can train autonomous robots, optimise warehouse throughput and reduce energy consumption – making logistics operations faster, cleaner and more resilient.
AI in African logistics and importing: Real-world gains
AI adoption isn’t just happening in global supply chains, it’s gaining real traction across African logistics and trade environments, where fragmented systems, high friction and thin margins make efficiency a top priority.
Take TradeDepot, which operates across Nigeria, Ghana and South Africa. The company uses AI to analyse purchasing patterns and market data across multiple regions, helping to reduce inefficiencies and cut its carbon footprint.
In Zimbabwe, grain storage operators have implemented AI-enabled Internet of Things (IoT) systems to monitor silo environments and optimise storage in real time, improving food security and reducing spoilage.
Closer to home, logistics firms and importers in South Africa are beginning to apply AI to their most pressing challenges:
- Predicting customs delays: Durban’s port has begun piloting AI models for predictive maintenance and logistics optimisation. These systems analyse real-time customs and container data to flag congestion risks early and reduce dwell times.
- Automating paperwork: Freight operators are using AI-powered Optical Character Recognition (OCR) and language models to extract key details from waybills, bills of lading and declarations, cutting down manual processing time and reducing cross-border clearance delays.
- Dynamic route optimisation: In regions with unreliable secondary road infrastructure, AI is helping logistics providers recalculate routes on the fly, reducing late arrivals and fuel waste.
- Inventory forecasting for importers: Local importers are using predictive AI to anticipate shifts in demand and better time inbound shipments, reducing costly overstocking and underutilised warehouse space.
“In South Africa, importers face a tough operating climate with currency volatility, shipping delays and supplier disruptions,” says Govender. “AI offers them a way to get ahead of those issues by predicting problems earlier and reacting with better precision.”
Agentic AI tools are also helping importers automate admin-heavy tasks like supplier onboarding, invoice verification, and customs compliance. This frees up internal teams to focus on higher-value activities like client service and supplier relationship management.
Many of these tools are mobile-first, subscription-based, and designed to work within existing operations, removing the need for full-scale tech overhauls and allowing smaller players to start where the ROI is clearest.
What is agentic AI?
Agentic AI refers to systems that can act independently to complete tasks, make decisions, or trigger actions, not just respond to prompts.
In logistics, this means tools that can track a shipment, detect a delay, recalculate a delivery route, notify the client, and update the schedule, without human input.
For South African importers and freight operators, agentic AI could be a game-changer: it reduces manual oversight, speeds up operations, and frees teams to focus on strategy, not admin.
The hidden barrier to AI success: Outdated systems, fragmented data
Before South African logistics operators and importers can benefit from AI, they need to address the biggest foundational challenge: broken data flows and legacy systems. AI thrives on clean, connected, real-time information, but many businesses simply aren't there yet.
Key hurdles include:
- Legacy systems: Many firms still rely on manual processes or outdated software that doesn’t integrate with modern AI tools.
- Siloed data: Shipment, inventory, customs and vehicle tracking information are often kept in unstructured or incompatible systems, making it difficult to train or deploy AI models effectively.
- Lack of real-time input: AI systems depend on current data from sensors, GPS or warehouse systems. Without it, they can’t respond to fast-moving changes in supply chains.
These challenges are becoming even more pressing as trade patterns shift from full container load (FCL) shipping to less than container load (LCL). Smaller, more frequent shipments mean containers now carry goods from multiple importers, creating more documentation, more handoffs, and more points where errors or delays can creep in.
One solution is API integration – a way of connecting different systems so they can “talk to” each other in real time, rather than leaving data trapped in silos.
“API integration is one of the levers that can close that gap,” notes Govender who says that Investec Business Banking has started leveraging AI integration within supply chain management for clients. “By facilitating real-time data access, improving collaboration, automating processes and providing scalability, businesses can start unlocking the real value of AI in their supply chains.”
For example, an importer working with Investec could use API integration to link their freight forwarder’s tracking system with their banking and customs documentation. Instead of manually reconciling invoices, shipment updates and clearance forms, the data would flow automatically between systems – speeding up cross-border payments, reducing errors and giving operators visibility into the entire process.
Turning Freight Challenges into Smart Wins
The benefits of AI are no longer limited to large multinationals, thanks to affordable cloud platforms and subscription-based tools that are now putting AI within reach for mid-market operators too.
“What separates successful adopters isn’t scale, it’s strategy. The businesses seeing results are those that start small, target specific pain points and focus on ROI over hype,” says Govender.
Here’s how local operators can get started:
- Pick a problem, not a platform: Identify your biggest friction point – be it invoicing delays, cross-border paperwork, or unpredictable fuel costs – and look for AI tools that solve that issue.
- Use what's already in the cloud: Many logistics-focused AI tools are now available on a pay-as-you-go basis via cloud providers, with no major IT overhaul required.
- Automate one process first: Start by using AI to automate a repetitive admin task, like pulling data from customs documents or sending client shipment updates.
- Lean into local fit: Choose tools built for emerging market conditions, those that can handle unstructured data, mobile-first interface, or offline functionality.
- Build trust with small wins: Show your team how AI saves time or cuts costs on one specific process before scaling it across the business.
As logistics volatility increases, from fuel prices to shipping delays, AI offers a way for importers and freight companies to respond faster, work smarter and operate leaner. The technology is ready. The key is starting where the value is clearest. DM