Forwarders should target lost gross profit with artificial intelligence rather than treat the technology as a productivity tool, according to Erik Muttersbach, founder of AI software company Zauber.

Speaking on The Loadstar Podcast, Muttersbach set out a three-to-five-year horizon for the transformation. He argued AI could turn freight forwarding into a higher-value, higher-margin business over that period, with a growing divide between companies that adopt it strategically and those that do not.

What does this change for forwarders?

The practical question is what AI changes for a forwarding house's margin structure. On Muttersbach's analysis, the answer is gross profit recovery — capturing revenue and margin that current manual processes leave on the table — rather than headcount reduction or faster document handling alone.

That framing shifts the business case. Productivity gains cut cost per shipment. Gross profit capture raises revenue quality, through pricing decisions, quotation accuracy and customer retention. Muttersbach positions tailored customer service as the second pillar: AI applied to customer data allows forwarders to differentiate service in ways that manual operations cannot scale.

A divide in three to five years

The core claim is timing. Muttersbach expects a visible separation within three to five years between forwarders that use AI to restructure their commercial model and those that limit it to back-office efficiency. Companies in the first group, on his argument, move toward higher value and higher margins; the second group competes on cost in a market where differentiation increasingly depends on data.

That divide matters because freight forwarding is a low-margin, high-volume trade where small percentage-point shifts in gross profit per shipment compound across the book. A tool that recovers even a fraction of lost margin per file changes the economics of the business more directly than equivalent savings in processing time.

Claims against the record

The three-to-five-year timeline is a founder's projection, made by the head of a company that sells AI software to the sector. It is not a measured outcome. Forwarders weighing the argument should test it against their own numbers: the volume of quotations that expire unanswered, the margin variance between similar shipments, and the share of gross profit leakage attributable to manual pricing and documentation errors.

What is verifiable today is narrower. AI-based tools are already in commercial use across freight forwarding for document extraction, quotation support and customer communication. The higher-margin business model Muttersbach describes remains a projection, dependent on adoption rates and on whether customers reward tailored service with higher yields.

The strategic read

For forwarders, the immediate decision is where to point the technology first. Productivity use cases deliver measurable savings quickly. Gross profit capture requires clean shipment data, pricing visibility and a willingness to let software inform commercial decisions that staff currently make by judgment.

Muttersbach's message to the industry is sequencing: start with the margin problem, not the efficiency problem, because the margin problem is where competitive divergence will appear. Whether that divergence materialises on his timeline will show in the gross profit figures of early adopters over the next several reporting cycles.