A procurement manager in Durban needs a bonded warehouse. A few years ago she would have searched Google, scanned the first page and made three phone calls. Today there is a reasonable chance she types the question into ChatGPT instead, reads a paragraph of considered-sounding advice, and works from the handful of companies it names.

That shift is quietly rewriting how supply chain businesses get found. And it comes with a complication most operators have not yet noticed: the answer she gets depends heavily on which tool she opens. Ask ChatGPT, Google’s AI Overviews and Perplexity the same sourcing question and you will often get three different sets of cited companies, sometimes with barely any overlap.

This is not one of the systems getting it wrong. As a recent analysis by Johannesburg digital agency IMS sets out, each platform runs a genuinely different process for deciding which sources are trustworthy enough to name. Understanding those differences matters more to a freight forwarder or 3PL than knowing the acronym for it.

ChatGPT: Being Read Is Not the Same as Being Named

When ChatGPT searches the web, it reads far more than it credits. Independent analysis of its browsing behaviour has found that only a fraction of the pages it retrieves end up cited in the final answer. The rest inform the response without ever being named. For a logistics company, that is the difference between a shipper seeing your name and a shipper seeing a competitor’s while your website quietly supplied the background.

ChatGPT also does not search the live web on every question. It is far more likely to go looking when the query carries commercial or comparative intent; “best cold chain provider,” “Transnet vs road freight costs,” “cheapest customs clearance Cape Town 2026”, than when someone asks it to explain what a bill of lading is. Which is convenient, because commercial intent is exactly where supply chain buyers sit.

The pages that survive the cut tend to share the same traits: they answer the specific question directly, they carry concrete figures rather than general claims about service excellence, and they are structured so a passage can be lifted out cleanly.

Google AI Overviews: A Page One Ranking Is Not Enough

AI Overviews sits on top of Google’s existing search index, but qualifying for the AI panel is a separate process from ranking. A logistics site can hold position two for a competitive term and still be skipped, while a page sitting at position eight gets quoted because it states the answer in two clean sentences with a number attached.

Researchers studying AI Overview outputs describe the process as a funnel: a large pool of candidate pages narrowed through semantic relevance matching, then authority and expertise filtering, then a final re-ranking before a small number of sources are stitched into the summary. Depth counts here in a way it does not for ChatGPT. Google appears to favour sites showing sustained expertise across several interlinked pages on a subject, rather than one strong article standing alone.

For an operator, that has a practical translation. A single well-written page about your temperature-controlled fleet is worth less than a cluster of connected pages covering cold chain compliance, load monitoring, last-mile handover and the regulations that govern them.

Perplexity: Built to Cite From the Ground Up

Perplexity is the outlier, usefully so. Citation is not a feature added to a chat product; it is the product. It runs a retrieval-augmented pipeline with multiple ranking layers scoring relevance, freshness, factual accuracy and structural clarity before anything reaches the answer.

It is also the most selective of the three. Perplexity will typically visit around ten pages for a query and cite only three or four. Structural trust signals carry real weight: named authors, visible editorial standards, and claims corroborated across more than one independent source rather than appearing on a single company page.

This is where trade publications, industry associations and conference coverage become genuinely valuable to a logistics business, not as vanity placements, but as the independent corroboration these systems are specifically looking for.

Why the Three Disagree

Put the pipelines side by side and the disagreement stops being mysterious. ChatGPT is deciding whether a page is worth naming after it has already read it. Google is applying an authority-and-extractability filter on top of an index built for a different purpose. Perplexity is built around sourcing and rewards signals the other two barely weigh.

The divergence is not random, either. A 2024 audit of ChatGPT, Bing Chat and Perplexity by researchers Alice Li and Luanne Sinnamon, published in the Proceedings of the Association for Information Science and Technology, found that generative search systems lean heavily on news, media and business publications for their sources, and showed measurable commercial and geographic bias in which sources they use to support claims.

Geographic bias deserves attention in this market. A system weighted toward North American and European business media is a system that may not surface the South African freight forwarder that is genuinely the right answer to a South African question.

What the Research Says Actually Works

The most rigorous evidence available comes from “GEO: Generative Engine Optimization,” a study by Pranjal Aggarwal, Vishvak Murahari and colleagues at Princeton, Georgia Tech and IIT Delhi, presented at KDD in 2024. The researchers built a benchmark of roughly 10,000 real queries and tested nine content optimisation strategies against generative engines.

The strongest performers were not keyword tactics. Adding statistics and adding direct quotations were among the most effective changes tested, improving visibility by roughly 30 to 40 percent against an unoptimised baseline. Authoritative language and explicit sourcing also helped. Keyword stuffing did close to nothing.

That finding sits comfortably with how the logistics sector already communicates. On-time delivery percentages, claims ratios, dwell times, fleet utilisation figures, tonnage handled, accreditation numbers; the industry is unusually rich in exactly the kind of concrete, quotable detail these systems reward. Most of it never makes it onto a company website, where it is replaced by phrases like “world-class logistics solutions.”

What This Means for the Industry

Three consequences follow.

The first is that vague marketing language is now actively costly. A page claiming end-to-end excellence gives an AI engine nothing to quote. A page stating that a facility holds 12,000 pallet positions, operates to a stated temperature tolerance and cleared a specific volume of customs entries last year gives it something to work with.

The second is that being visible on one platform tells you nothing about the others. Because the pipelines differ, and because independent research confirms real bias in what they select, a company cited confidently by Perplexity may be invisible in ChatGPT and Google’s AI Overviews. Each has to be checked on its own terms.

The third applies to the buying side. Supply chain managers using these tools to shortlist providers should treat the results as a starting point shaped by a particular set of preferences, not a market survey. A carrier’s absence from an AI answer says something about its web presence. It says very little about its trucks.

The industry has spent a decade learning to be found on Google. The engines that increasingly sit between a shipper and a supplier now work differently from each other, and differently from search. That is worth understanding before the next tender or RFP goes out.

For years, logistics was measured by what people could see. Trucks left the depot, containers arrived at the port and pallets moved through warehouses. Success depended on getting goods from one place to another safely and on time.

That hasn’t changed, but something else has. Every one of those movements now creates information. A delivery being delayed, inventory running low or a vehicle taking longer than expected to complete its route all leave behind data that businesses can use to make better decisions.

Increasingly, it’s that information – not just the movement of goods – that is shaping modern supply chains.

Every Movement Tells a Story

A truck doesn’t simply complete a delivery anymore. It records where it travelled, how long the journey took, where delays occurred and when it arrived. Inside the warehouse, inventory systems track how quickly products move, which items are picked most often and where bottlenecks begin to develop.

On their own, those numbers don’t mean much. Put them together over weeks or months, however, and patterns start to emerge. Businesses can see where time is being lost, which routes perform consistently well and where small changes could improve efficiency.

Turning Information Into Action

Most businesses already have access to vast amounts of operational data. The real challenge isn’t collecting more information—it’s knowing what deserves attention and what can be ignored.

A delayed delivery, slower picking times or a recurring bottleneck in the warehouse might seem like isolated incidents. Over time, though, those patterns can reveal where processes are slowing down, where costs are creeping in or where customer service is starting to suffer. The businesses gaining the greatest value from data aren’t necessarily collecting more of it. They’re using it to make everyday decisions with greater confidence.

Spotting Problems Before They Grow

Not long ago, supply chain reports were largely used to explain why something had gone wrong. By the time the numbers reached someone’s desk, the disruption had already happened and teams were focused on recovering rather than preventing it.

Today, businesses have a much clearer view of what’s happening as goods move through the supply chain. A warehouse beginning to fall behind, unexpected congestion on a transport route or stock running lower than expected can often be identified early enough for teams to step in before those issues become much bigger problems.

It’s Not About Having More Data

The amount of information flowing through the supply chain continues to grow, but that doesn’t automatically make a business more efficient. Poor-quality data can be just as frustrating as having no data at all, especially when different teams are working from conflicting information.

For many organisations, the focus has shifted from collecting more data to making existing information more accurate, consistent and accessible. When everyone is working from the same reliable picture, decisions become quicker, communication improves and the supply chain becomes far easier to manage.

The supply chain will always be built around moving goods, but understanding what happens between each stage has become just as important. Businesses that can turn everyday operational information into practical decisions will be far better placed to respond as the industry continues to evolve.

Artificial intelligence may dominate boardroom conversations, but in African logistics, intelligence has never been confined to systems or algorithms. Long before AI became shorthand for efficiency, African supply chains relied on people solving local challenges: long distances, varied terrain, cross-border requirements, uneven infrastructure, language differences and the pressure to keep essential industries moving.

This does not downplay the role of technology. Real-time data, telematics, predictive maintenance, business intelligence and vehicle innovation already help operators work smarter, safer and with greater visibility. However, the real test in Africa is whether technology can be adapted to the conditions in which African industries operate.

This is where African Ingenuity becomes a competitive advantage. It means designing practical solutions for local realities, not forcing them to fit imported templates. It is the intelligence of people who know the road, the customer, the product, the border, the season, the terrain and the risk, and who turn those variables into workable systems.

Few people understand this better than Noddy Ramroop, Head of Business Development for the Freight Division at Unitrans. Noddy recently celebrated 40 years with the business. Since joining Unitrans in 1986, he has worked across the organisation and in multiple markets, including agriculture, fuel and chemicals and in our operations in Botswana, Malawi and South Africa. Over that time, he has seen the industry change dramatically. 

Today, logistics is a specialist, technology-enabled industry. Data, systems, advanced vehicles and operational expertise are part of daily delivery. Yet the evolution of logistics in Africa has never been as simple as moving from manual to digital. It has been about making each new tool work in environments where conditions are not uniform.

A system designed for one route or customer may need to work differently elsewhere. African Ingenuity is about identifying constraints early, understanding local realities, designing around them and improving solutions over time. This is especially important when supply chains span different countries, languages, regulations, infrastructure conditions and customer needs.

That kind of complexity is not a side issue in African logistics. It is the operating environment.

“For me, relationships are vital. I can’t actually put a value on it. That’s what makes our business tick,” says Noddy.

Relationships matter because no logistics solution stands alone. It depends on customers, teams, communities, technology partners, drivers, planners and operational specialists working towards the same outcome. In markets where conditions can shift quickly, trust and collaboration often decide how effectively a solution adapts when the plan meets reality.

Noddy believes the best logistics partners are those who can look at complexity differently. Instead of seeing a challenge only as a barrier, they look for the opportunity inside it. “As I always say to my team, whatever the complexity is, turn it on its head and come up with a solution,” he says. “That is how you find the opportunity.”

That mindset can be seen in practical ways across the sector.

In areas with inconsistent network coverage, live tracking can be interrupted. The answer is not to abandon visibility, but to design systems that work with that reality. Technology can record and preserve vehicle data while the asset is outside coverage, then upload the information once the signal returns, protecting the data trail and supporting accountability.

Performance-Based Standards vehicles provide another example. These heavy vehicles are designed and assessed according to strict safety and performance standards, allowing operators to improve efficiency while maintaining safety on approved routes. In Africa, efficiency is often tied to distance, road conditions, payload, route approvals and the safe movement of high-risk products. This is engineering applied to operational need.

Agriculture offers a further example. In cane operations, progress has often come through trial, adaptation and close collaboration with growers. Noddy points to the evolution from traditional transport into more integrated field services, where teams had to consider field conditions, compaction, loading methods and the time between cutting and crushing. Through testing, technological advancement and operational adjustment, the process evolved to support better movement from field to mill.

The same discipline applies to product integrity and fuel loss prevention. In high-risk, high-value sectors, vigilance is not a once-off intervention. It is a continuous process of monitoring, learning, improving and adding new layers of control.

This is where talent becomes vital. Experienced employees hold institutional knowledge that systems cannot provide. They know why routes behave differently, where gaps may emerge, how customer needs have changed and which details can influence larger outcomes.

At the same time, younger talent brings digital skills, new ideas and ease with emerging tools. African logistics does not need to choose between experience and technology. The sector should deliberately combine both, pairing operational knowledge with digital skills to create teams that understand ground realities and improve the systems that support them.

For Unitrans, this is where logistics becomes more than a transport function. By moving goods, materials, people and essential products, the sector forms part of the operating infrastructure that enables industries to grow, communities to function and economies to progress. As Noddy’s 40-year journey shows, artificial intelligence will continue to shape supply chains, but Africa’s real advantage lies in organisations that combine smarter systems with African Ingenuity, developing practical, fit-for-purpose solutions around local conditions. The real AI in African logistics is therefore about the people whose expertise, adaptability and problem-solving keep industries moving and make growth possible where it matters most.

Noddy Ramroop, Executive Business Development Unitrans