By Maureen Phiri, Director at Oxyon People Solutions

In the past, the logistics of workforce management have tended to happen behind the scenes. Clients saw the end result of staff arriving on site, projects being delivered, and operational requirements being met, but the processes that made it possible were mostly invisible. However, this approach is now beginning to change, thanks to Artificial Intelligence (AI) becoming more integrated into workforce management.

Workforce providers can now give their clients greater visibility into the processes behind staffing. This means that clients have a better understanding of workforce readiness, compliance, and operational performance, which in turn allows them to make faster, more informed decisions. For workforce providers, this means that administrative functions can become strategic assets that strengthen collaboration, build trust, and create greater value for clients.

AI is Connecting the Workforce Journey

Clients are looking for increased transparency when it comes to their outsourced staffing processes. For workforce providers to be able to do this, they need to bring together information from across the workforce lifecycle. Previously, this would have been a labour-intensive task, but AI makes it much quicker and easier to collate information from across separate systems like recruitment, onboarding, compliance, and workforce management.

Connecting these functions allows workforce providers to have a much clearer view of workforce readiness and allows them to share relevant information with clients in real time. This means that instead of waiting for reports or updates, clients can see where people are in the recruitment or onboarding process at any time. It also helps them to  understand whether compliance requirements have been met, and identify any potential issues in the process.

This changes the role of workforce logistics, because the information that was previously only used to support internal operations can become a competitive advantage. With AI behind the scenes, this information can help clients plan more effectively, respond more quickly, and work more closely with their workforce partners.

Making the Back Office Visible

Take onboarding as an example. Traditionally, clients would only become involved in the process once a worker was ready to start, but using AI means that workforce providers can give their clients visibility throughout the process. This means that clients can see how onboarding is progressing as it happens, and any problems that might cause delays can be identified and dealt with early.

The same applies to compliance. With visibility throughout the recruitment processes, clients can easily see whether compliance requirements are being met, rather than only being notified when a problem occurs. This reduces risk, because issues can be handled before they can have a negative effect on client operations.

When it comes to workforce administration, the benefits are similar. Having real-time visibility into information that used to only be used internally at a workforce provider means that clients are more informed and can make better operational decisions.

More Transparency Builds Stronger Partnerships

Better visibility helps to improve reporting, but it can also change the relationship between workforce providers and their clients. If workforce providers give their clients ongoing access to more information, the whole process becomes more proactive. Problems can be identified quickly and decisions can be made faster. Added to this,  both clients and workforce providers can understand the workforce journey better, as it happens. 

It is important, however, to remember that responsible data management is essential. Giving clients more visibility into workforce processes does not mean providing unrestricted access to personal employee information. AI should help organisations share the information clients need to make better business decisions, but it should also be used to make sure that personal employee data is always protected.

Clients and workforce providers also need to remember that technology should never replace human judgement. AI can process information quickly and can identify potential problems, but any decisions about recruitment, onboarding, and workforce management must still be made by people who can use their experience to consider the circumstances and take responsibility for the outcome.

The Future of Workforce Logistics

AI is increasingly becoming part of workforce management, but the real value of these tools is not just in automating administrative tasks. If workforce providers can use AI to make workforce processes more transparent, they can give their clients better visibility into the information that supports operational decisions, including onboarding and compliance. This is fast becoming a competitive advantage that helps workforce providers strengthen client relationships, support better decision-making, and demonstrate the value of their expertise.

Customers rarely think about procurement.

When they walk into a shop, place an online order or receive a delivery at their door, they’re focused on one thing: whether the experience lives up to their expectations.

Is the product available?

Did it arrive when it was supposed to?

Was it the quality they expected?

Most people never stop to consider that those moments often begin months before they become customers. Long before an order is packed or a delivery vehicle leaves the warehouse, someone has already decided who will supply the product, how it will be sourced and how it will make its way through the supply chain.

Those decisions quietly shape almost everything the customer experiences.

The Customer Only Sees the End Result

Businesses spend a great deal of time discussing procurement, inventory, warehousing and logistics.

Customers don’t.

They simply judge what happens at the end.

If a product is out of stock, they don’t wonder whether a supplier experienced production delays. If a delivery arrives late, they’re unlikely to think about transport schedules or inventory planning.

They remember the business they bought from.

That’s what makes procurement so important. The decisions made long before a customer places an order often determine whether that customer leaves satisfied or frustrated.

Every Supplier Changes the Experience

Choosing a supplier isn’t simply a purchasing decision.

It’s a decision about how the business wants to operate.

One supplier may offer a lower price. Another may have a stronger record for delivering on time. A third might be more responsive when plans suddenly change or demand increases without warning.

None of those qualities appears on the shelf beside the product.

Yet every one of them can influence whether customers find what they’re looking for when they need it.

By the time procurement teams sit down to compare suppliers, they’re often thinking about much more than the quotation in front of them. They’re considering how dependable each supplier has been, how easily they communicate and how confidently the business can rely on them when circumstances become less predictable.

The Best Customer Experiences Start Earlier Than Most People Think

One delayed shipment doesn’t necessarily create a problem.

Neither does one supplier running behind schedule.

Supply chains are remarkably good at absorbing small disruptions.

The real challenge comes when those small disruptions begin happening more often. Deliveries start slipping by a day here and there. Inventory takes longer to replenish. Warehouses begin adjusting schedules to accommodate late arrivals.

Eventually, those small changes reach the customer.

From the customer’s perspective, it feels as though the business has become less reliable.

In reality, the first signs may have appeared much earlier inside the supply chain.

Procurement is Really About Trust

The strongest supplier relationships aren’t built on price alone.

They’re built on confidence.

Confidence that products will arrive when they’ve been promised. Confidence that suppliers will communicate when circumstances change. Confidence that both businesses will work together when unexpected challenges appear.

Those qualities don’t always stand out during a tender process.

They usually become obvious months later, when the supply chain comes under pressure.

Customers Remember How You Made Them Feel

Most customers will never know who supplied the product they purchased.

They’ll never see the purchase order, negotiate a contract or visit the warehouse where their order was packed.

What they will remember is whether the experience felt effortless.

That’s why procurement reaches much further than purchasing products.

Every decision made at the beginning of the supply chain has the potential to shape the experience waiting at the very end.

Long before a customer forms an opinion about a business, procurement has already helped write that story.

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.