AI-Ready or Squeezed Out: How Transport & Logistics Suppliers Win Bigger Contracts

Big customers now screen hauliers on data, uptime and integration. Here are the AI workflows - worth 5-20% on logistics cost - that keep smaller carriers in the game.

8/24/20264 min read

A line of semi trucks in a traffic jam on a highway

The transportation industry is quietly changing. The shippers, primes and 3PLs that smaller carriers depend on are pushing hard into AI, real-time visibility, and integrated data — and they increasingly are screening their suppliers based on operational performance, timeliness, and visibility. McKinsey's benchmark work found that early adopters of AI-enabled supply-chain management improved logistics costs by 15%, inventory levels by 35%, and service levels by 65% against slower-moving competitors [1]. When your customer is chasing gains like that, the transport partner who can plug into their systems and prove performance grows in a challenging market. The one who still uses spreadsheets and does not understand their own performance metrics will get squeezed out.

The good news: you don't need a data-science team or a seven-figure platform to be that partner. You need a handful of well-chosen workflows and the ability to glue your existing tools together. Here's where a small operator gets the most leverage.

Route and load optimisation: the fastest, lowest-risk win

Routing and load planning is where AI pays back quickest because the savings hit fuel — the second-biggest cost after wages for transportation providers. McKinsey estimates AI-enabled distribution and logistics optimisation can cut logistics costs by 5–20% [2]. Vendor case studies routinely claim 15–30% fuel reductions from dynamic routing and driver-behaviour scoring [3], and our own clients have seen over 15% fuel savings in recent months.

The point for a smaller carrier isn't the headline number — it's that route optimisation can be available today, built upon existing tools that need no significant capital outlay, and produces actionable metrics (cost per km, empty miles) you can use to optimise your revenue and enhance your margins. That edge is what turns a struggling operation into a profitable one.

Cut the empty running you're not even measuring

In a recent engagement with a mid-sized haulier, roughly a third of all trips carried zero customer revenue — empty legs, repositioning and unbilled backhaul. None of it showed up clearly because the data lived in three different places. Simply making empty running visible, then targeting backhaul and repricing, created the single biggest margin lever in that transport business, and helped the business increase profits.

Predictive and condition-based maintenance: sell uptime, not excuses

Nothing damages a supplier relationship faster than a missed collection because a truck broke down. Condition-based and analytics-driven maintenance let you intervene before a failure. McKinsey's operations research documents condition-based programmes cutting labour, downtime and parts costs by around 30%, and advanced-troubleshooting approaches delivering an 18–25% reduction in maintenance costs [4]. For a fleet, that translates directly into the uptime and reliability KPIs your customers actually score you on.

Demand and capacity forecasting: stop guessing

Better forecasting is where AI is most mature. McKinsey reports that AI-driven demand forecasting has helped supply-chain operators improve forecast accuracy materially, feeding straight into the service-level gains cited above [1]. Even a modest capacity forecast — knowing next week's likely volume by lane — lets a small operator commit trucks with confidence instead of scrambling or turning work away.

The niche that makes you indispensable

Here's the strategic direction to consider: don't try to be a cheaper generalist. Be the supplier who delivers something the big customer's core systems can ingest. That means real-time status updates via API, clean proof-of-delivery data, and compliance evidence in the format they require. When your data slots neatly into their platform, switching away from you becomes a cost they'd rather avoid. Niche, integrated reliability beats scale you can't match.

The real skill: gluing platforms together with a small team

Most transport SMEs already run a telematics system, a transport management or trip-logging tool, an accounting package and a fuel-card portal — plus the inevitable master spreadsheet. The mistake is thinking the answer is one big system to replace them all. It rarely is. Rip-and-replace is slow, expensive and risky for a lean team.

The flexible move is to connect what you have. Modern integration tools — the no-code and low-code "glue" layer such as Make, Zapier or n8n, plus the APIs your existing vendors already expose — let a small team wire telematics, TMS, accounting and customer portals into one flow. Fuel data links to the trip; the trip links to the invoice; the invoice status links to the dashboard. This is also what gives you flexibility: when a new customer runs a different portal, you connect to theirs rather than forcing them onto yours.

This matters because AI adoption in UK SMEs is still shallow — most firms using AI are applying generic tools to existing tasks rather than integrating them into operations [5]. The carriers that win the next five years won't be the ones with the most tools. They'll be the ones whose tools talk to each other, and to their customers'.

Start with one workflow, prove the number, then connect the next. That sequence — small, evidenced, integrated — is how a lean transport business makes itself AI-ready enough that its biggest customers keep buying.

If you're a transport or logistics operator working out which workflow to tackle first, InfinityX Digital (https://infinityxd.uk) helps smaller firms pick the highest-return move and glue their existing systems into one.

Sources & quotes

[1] McKinsey & Company — "Succeeding in the AI supply-chain revolution"** (Metals & Mining Practice; widely cited AI supply-chain benchmark).
https://www.mckinsey.com/industries/metals-and-mining/our-insights/succeeding-in-the-ai-supply-chain-revolution

[2] McKinsey & Company — "Harnessing the power of AI in distribution operations"** (Distribution blog, November 15, 2024).
https://www.mckinsey.com/industries/industrials/our-insights/distribution-blog/harnessing-the-power-of-ai-in-distribution-operations

[3] Fuel-saving case studies (vendor/secondary).** Industry write-ups report 15–30% fuel savings from AI route optimisation and driver-behaviour scoring (e.g. a 180-vehicle fleet cutting fuel consumption 22% over 12 months).
"AI route optimization reduces fleet fuel costs 15-30% within 90 days."
https://heavydutyjournal.com/ai-route-optimization-for-fleets-cut-fuel-costs-15-30-in-90-days/

[4] McKinsey & Company — "Establishing the right analytics-based maintenance strategy" (Operations Practice, July 2021).
https://www.mckinsey.com/capabilities/operations/our-insights/establishing-the-right-analytics-based-maintenance-strategy

[5] UK SME AI adoption (DSIT/ONS data via secondary coverage, 2025).** UK SME AI adoption reached roughly a third in 2025, but most adopters use generic tools to support existing work rather than integrating AI into operations.
https://www.moneypenny.com/uk/resources/blog/the-state-of-ai-adoption-in-uk-businesses/