• CargoAi is embedding AI directly into its booking platform and email workflows, automating quoting, pricing, and customer interactions while preserving human oversight for complex requests.
• Proprietary models trained on millions of global transactions allow accurate rate predictions, personalised offers, and real-time optimisation of yield, boosting forwarder productivity and airline automation without overhauling legacy systems.
• Early adopters see measurable gains, including 30–50 percent faster quote response times, 10–15 percent higher conversion, and the ability to handle thousands of transactions automatically, creating a widening performance gap for late adopters.

As the air cargo industry wrestles with digitisation at scale, artificial intelligence is shifting from hype to implementation. And according to Matt Petot, founder and CEO of CargoAi, that shift is happening directly inside the inbox.

While many forwarders and airlines are still navigating basic e-booking adoption, CargoAi’s latest push embeds AI tools into quoting, pricing, and customer interaction — automating everything from rate prediction to natural language email replies. If it works as claimed, it could significantly raise productivity without overhauling legacy systems.

Conversational AI

CargoAi’s approach puts AI front and centre in its booking platform, CargoMART, but avoids separating it into standalone apps. Instead, AI operates under the hood — predicting rates, identifying the best routing options, and now responding to emails.

“Our new CargoCopilot AI Agent is a full conversational interface,” Petot says. “Forwarders can simply type ‘Book 300 kg from SIN to LHR next Tuesday’ or ‘Show me rates to JFK’, and get instant results.” Airlines and GSAs, he adds, are using the same tool to triage thousands of customer emails daily — from availability checks to delivery instructions.

While digital quoting tools are not new, Petot argues that AI turns static interfaces into dynamic decision-makers. “It’s not just about speed. It’s about replacing repetition with reasoning.”

One of the key differentiators Petot highlights is that CargoAi trains its models in-house using its own proprietary dataset — not third-party or public LLMs.

“Our LLMs are trained by us, on our own servers, with our own dataset,” he explains. “That ensures customer data is never shared, never exposed, and never used to train public models.”

The dataset in question spans millions of searches, quotes, and bookings from more than 150 countries. Combined with anonymised industry inputs and upcoming integrations with IATA ONE Record, the result is a rare blend: transaction-level data at global scale.

This data allows the platform to make predictions that, in Petot’s words, “no single airline’s RMS can make.” For instance, it can forecast with up to 90 percent accuracy which offer a forwarder is likely to accept — giving carriers a chance to optimise offers in real time.

CargoAi claims its tools deliver measurable returns, especially for forwarders caught between growing volumes and limited resources.

“For mid-sized forwarders, we see 30–50 percent faster quote response times and a 10–15 percent uplift in conversion rates,” Petot says.

“One Southeast Asian forwarder processed four times more quotes per rep within 90 days — without adding headcount.”

On the airline side, the automation scale is more aggressive. “One GSA now processes over 10,000 quotes per month automatically. That’s not proof of concept. That’s production.”

Beyond volume, Petot is keen to stress that the models prioritise profitability, not just filling space. “Our models are built to protect yield. In volatile markets, the system adapts its priorities in real time — volume never comes at the expense of margin.”

That might be the most important distinction. AI in air cargo often leans toward speed and automation, but ignoring revenue management nuances would risk cutting into the very profits it promises to boost.

What may appear at first as an email plugin is, in fact, where Petot sees the real strategic shift — using email content, CRM history, and booking data to personalise offers and automate replies without compromising on context.

“The Agent understands cargo type, service level, even past negotiation patterns,” he says. “When confidence is high, it responds instantly. When it’s low, it flags for human review.”

The decision to build in confidence scoring — and keep a “human-in-the-loop” for sensitive or complex requests — reflects a practical understanding of how AI tools should operate in regulated, high-value logistics.

Surprisingly, this is also attracting smaller forwarders who aren’t running full TMS platforms. “Over 40 percent of our plugin users don’t use a TMS,” Petot notes. “They connect via email or CargoMART directly — no system overhaul needed.”

That’s no small detail. With much of the industry still running on email, spreadsheets, and semi-structured workflows, AI adoption needs to meet users where they are — not demand a digital leap they’re not ready for.

Despite the progress, Petot admits the industry remains divided. A few airlines are leaning into full integration — seeing clear gains in quoting speed, conversion, and load factor management. But many remain stuck in pilot mode.

“Adoption speed matters more than ever,” he warns. “AI compounds its advantages: faster learning, better data, more market share. The performance gap between early adopters and latecomers will be huge.”