Lincoln’s experts outline how real AI advantage in transport & logistics comes from scaled deployment across existing networks.

Transport & Logistics sector value

Source: CapIQ

T&L has never been short on technology promises. Digital brokers, control towers and visibility platforms have each been billed as transformative. Yet for most operators, profitability still comes down to the same fundamentals: labor productivity, pricing discipline and asset utilization. These fundamentals align directly with private equity value creation levers, particularly cost efficiency, operating leverage and return on invested capital. Artificial intelligence is now the latest headline. But after the noise settles, the story looks less like disruption and more like an opportunity to optimize operational execution.

A large opportunity is opening in the T&L sector, but only for those players with the capability to turn the opportunity offered by AI into real results. The operators embedding AI directly into workflows are already seeing tangible results. Businesses with digital-ready, clean data and standardized systems are seeing increased interest from sponsors and strategic acquirers alike. The article below outlines how management teams and investors can turn ideas for improvement ideas into tangible, bottom-line results and accelerate delivery on value-creation targets.

Where the value really sits

Across freight forwarding, contract logistics and integrated 3PLs, AI is emerging not as a silver bullet, but as a steady margin engine. The value is not futuristic autonomy or fully automated supply chains. It is faster quoting, fewer manual touches, cleaner data and better daily decisions. In other words: compounding operational excellence.

Across transport and logistics, AI consistently drives four economic levers:

• First, productivity. Automated document capture, email parsing and workflow tools remove repetitive tasks that historically required large back offices. Automated document processing in forwarding and customs has reduced manual entry by up to 80% in some cases, cutting both costs and errors.
• Second, pricing and yield. Machine learning-based quoting engines allow operators to respond instantly to tenders, using historical and real-time market data to protect margins. Maersk and Schenker, for example, are integrating dynamic pricing into customer portals to improve conversion and profitability.
• Third, utilization. Load matching, routing and predictive planning increase throughput per warehouse, truck, or lane. DHL and DSV are using AI to improve load building and routing decisions, lifting asset productivity while reducing empty miles.
• Fourth, service. Predictive ETAs and disruption alerts enable proactive exception management, which improves reliability and customer stickiness. Maersk, Kuehne+Nagel, and others are deploying AI-enabled visibility and forecasting tools for exactly this reason.

None of these levers sound dramatic, but together, they are powerful. Even small improvements, such as –3-5% fewer touches, 1–2% better pricing and a few points of higher utilization, compound quickly in an industry where efficiency matters a lot.

Warehousing and contract logistics: start with planning, not robots

Contract logistics is arguably the most AI-ready part of the value chain. Processes are repeatable, environments are controlled and data is structured. Here, the temptation is to jump straight to robotics. But the better returns often come earlier. Labor forecasting, slotting optimization and digital twins frequently deliver faster payback than hardware-heavy automation.

Consider the example most often cited across the sector. C.H. Robinson’s Lean AI operating model has automated quoting, order creation and appointment scheduling through agentic tools. The company reports shipments per employee up more than 40% since 2022, alongside margin expansion and market share gains, achieved with only modest increases in tech spend. That combination of productivity, leverage and scalability is what practical AI looks like in logistics, and with the proper execution, it works.

Volume growth in LTL and TL is delivered against a declining driver headcount

Source: CH Robinson company data

DHL, Schenker and GXO, for example, are already using AI to guide picking, sorting, and inventory positioning, improving throughput and accuracy while reducing labor intensity. The lesson is sequencing: standardize data and planning first, then automate selectively.

The outcome is not lights-out warehouses, but fewer touches per unit and more predictable service economics.

Freight forwarding: eliminate the manual touches

Forwarding remains one of the most manual corners of the industry and therefore one of the most attractive for AI. Emails, PDFs, rate sheets, customs forms and spreadsheets still dominate many processes. That is exactly where document automation and workflow engines shine.

DSV has focused on digitizing manual customer requests and feeding them directly into its systems, supported by an internal AI factory developing tools for customs and booking workflows. DHL has rolled out agentic use cases across customer service, customs declarations and dispatch functions to remove repetitive work and speed execution.

The logic is simple. Small, low-complexity shipments should flow straight through digital paths, while humans focus on exceptions and value-added tasks. The result is lower overhead and faster responses, which in forwarding often translates directly into higher win rates.

Speed is now a competitive weapon. The forwarder that quotes in seconds rather than hours wins.

Execution beats experimentation

When everyone has access to the same technology, execution is what separates winners from laggards. Clean data, standardized systems and common processes matter more than the latest model or tool, as AI layered onto fragmented IT rarely scales. Experience across the sector suggests that only a small portion of success comes from algorithms themselves. Most of the benefit comes from systems integration and changing ways of working. In short: operational discipline beats hype.

Source: CH Robinson company data

For both strategics and private equity, this has real implications. Historically, deals were about scale and density. Today, digital readiness is just as important. A business with clean data and standardized systems can be plugged into a shared AI toolkit quickly. One with fragmented legacy systems becomes an integration headache.

Strategics are increasingly building centralized AI factories, developing pricing engines, forecasting tools and workflow automation once, then deploying them across the group. DSV’s focus on simplifying and harmonizing its IT landscape is a case in point: simplify first, automate second, scale third. Digital readiness is becoming a key diligence factor and integration risk determinant.

For private equity, the opportunity is operational alpha. A platform can centralize back-office functions, deploy shared pricing and control towers and roll up bolt-ons onto the same stack. Each acquisition becomes easier to integrate and more profitable. The exit story shifts from simply bigger to structurally more productive.

The bottom line

AI will not transform logistics overnight. It will not eliminate cycles or replace physical networks. What it will do, quietly and consistently, is improve the math: Fewer touches. Faster quotes. Better pricing. Higher utilization. Lower costs.

These improvements drive durable EBITDA growth, stronger free cash flow and improved exit multiple outcomes, which make AI a practical lever for private equity value creation rather than a speculative bet.