Carrier Selection Criteria

A logistics CEO put it plainly: "I'm not getting fewer people. I'm getting fewer headaches." His operations team spent forty percent of their week on status updates, spreadsheet reconciliation, ticket tracking and other administrative overheads that added no strategic value. Six months after deploying autonomous AI, the team size remained unchanged but their work shifted entirely. They now focus on carrier relationship management and route optimization that drives actual cost savings and requires human judgment. The productivity gains come from elimination of administrative friction. I have tracked this pattern across fifteen organizations over twelve months. The companies treating AI as a headcount reduction tool consistently underperform. They optimize for the wrong metric. The high performers reframed the entire approach. Instead of pursuing leaner operations, they identified where AI could remove low-value work and redirect human capacity toward high-judgment tasks. Strategy over cost-cutting. Capability enhancement over efficiency theater. Most leadership teams still default to the reduction mindset. Fewer people, lower payroll, improved margins. That calculus misses the competitive dynamic completely. Your competition is not figuring out how to operate with fewer people. They are determining how to eliminate the work that prevents their people from operating at full capability. That gap in strategic thinking creates the separation between organizations that deploy AI and organizations that gain advantage from it. #AutonomousAI #FutureOfWork #AIAutomation #DigitalTransformation #AIForLeaders #ReclaimYourDay #Productivity #AIReadiness #BusinessTransformation #AIHumanCollaboration

Ask your team how they use AI. You will hear a mix of answers. Some swear by it. Some tried it once and moved on. Some have never touched it. This inconsistency is the real story of AI adoption today. The technology works. The organisation does not work with it. What is missing is not tools or training. What is missing is the operational layer: the habits, workflows, ownership structures, and validation routines that turn occasional AI use into reliable AI use. AI was added on top of existing processes. It became extra effort, not better effort. And when something feels like more work, people stop doing it. Because nobody redesigned how work actually happens. The shift from experimentation to operation requires a different approach: ❌ Not more training → ✅ Embedded habits  ❌ Not more tools → ✅ Intentional standardisation  ❌ Not more pilots → ✅ Redesigned workflows  ❌ Not blind trust → ✅ Built-in validation  ❌ Not shared responsibility → ✅ Named ownership In this week's newsletter, I break down the practical steps to close the gap between trying AI and actually using it. If AI still feels like a side project in your organisation, this will change how you think about adoption. One word: How would you describe your organisation's AI adoption right now?

Airlines aren’t just talking about AI - they’re already using it to smooth operations, save fuel and keep passengers moving. Delta Air Lines’ Operations Control Centre runs a machine‑learning tool that studies weather patterns and re‑sequences flights hours before storms bite, cutting knock‑on delays. Avionics International easyJet has fitted its entire Airbus fleet with Skywise Predictive Maintenance. Engineers now replace parts before they fail, reducing technical delays and cancellations. Airbus Alaska Airlines dispatchers use Flyways AI to pick the most efficient routes in real time. On long sectors that’s delivering 3‑5 percent fuel and CO₂ savings-over a million gallons a year. Alaska Airlines News PR Newswire Qantas puts personalised fuel‑efficiency analytics in every pilot’s hand via GE’s FlightPulse, driving behaviour changes that trim both fuel burn and emissions. geaerospace.com Lufthansa Systems’ NetLine/Ops ++ aiOCC gives controllers an AI “copilot” that turns masses of live data into recommended actions, helping curb cascading delays across the network. Lufthansa Systems Three take‑aways for carriers still on the fence: AI thrives in the messy middle. It surfaces the next best action when plans unravel. ROI is tangible. Minutes saved, gallons saved, cancellations avoided—every metric lands on the P&L. Humans stay in control. The most successful roll‑outs pair smart algorithms with experienced dispatchers, engineers and pilots. If your airline is still juggling spreadsheets during disruptions, the sky is sending a clear signal: it’s time to bring AI into day‑to‑day ops.

When your “shortcut” turns into a nose-first plunge down 50 feet of stairs, there’s a problem bigger. This looks like a driver blindly following a GPS instead of looking at reality. When a truck’s out there rolling without visibility, you’ve got a 80k-lb blind spot on wheels. Without telematics, you don’t know⬇️ ➡️Where it’s routed (or mis-routed). ➡️What the driver’s doing behind the wheel. ➡️How fuel, brakes, and maintenance are holding up. ➡️If your reefer temp is safe or your cargo is at risk. ➡️Who’s even in the truck. Yet name is on the door, your finances are on the line, and your reputation is what gets crushed when something like this goes viral. Federal Motor Carrier Safety Administration’s own crash causation study shows 22% of truck crashes involve drivers unfamiliar with their route. Pair that with GPS mistakes and you get situations like this. Add in that over 40% of all truck OOS violations in 2024 were maintenance related, and it’s clear: operating blind is not an option. I drive. I get the privacy argument, off-duty and sleeper time should be private. But when that truck’s in motion, it’s a moving workplace. You should know what’s happening with the asset, the cargo, and the driver. That’s where Motive comes in. Their platform gives fleets visibility into routing, driver behavior, maintenance, cargo temps, fuel use, and more. It’s about protecting lives on the highway and protecting your business at the same time. At the end of the day, when the truck takes a wrong turn down a set of stairs, it’s not just sheet metal at stake, it’s your business, your people, and your future. #riskmitigation #exposuremanagement

Following my last post on technology adoption and value realisation, a few people asked: So how do you actually build capability maturity on site?   From my experience supporting technology rollouts across surface and underground mining operations in Africa, capability maturity does not improve through training alone. It improves when technology becomes part of how the operation plans work, executes work, and reviews performance. Guidance from the Global Mining Guidelines Group (GMG) and the World Economic Forum’s Mining & Metals Digital Transformation Initiative both highlight that performance gains from digital systems are only sustained when they are embedded into daily operational routines ; not deployed as stand-alone tools.   In practice, this means: Planning routines must use system outputs: Short-interval plans and shift targets should be informed by haul cycle times, queue data, and payload variance from optimization platforms. (GMG, Data Integration and Interoperability in Mining, 2020) Supervisory routines must reinforce system decisions: Shift handovers and production meetings should review performance using system-generated KPIs. (McKinsey & Company, How digital innovation can improve mining productivity, 2015) Execution must follow optimization logic: Dispatch and operators must make decisions within system logic rather than reverting to manual allocation or experience-based judgement. (WEF, Digital Transformation Initiative: Mining & Metals, 2017) Where these routines are absent, technology often automates existing inefficiencies. Capability maturity improves when leadership routines, planning workflows, and frontline execution are aligned with the system, turning deployment into sustained performance. Adoption is not achieved at commissioning. It is achieved when the operating model changes. #MiningTechnology #OperationalExcellence #DigitalTransformation #MineIQ

As we look at the evolution of tracking visibility in logistics, it's clear we're on the cusp of a new era. Here's how I think of the "history" of freight visibility: 📱Visibility 1.0 (2010-2016): This phase was characterized by basic tracking using cell phones: • Simple location pings from drivers' mobile devices 🖥️ Visibility 2.0 (2017-present): This phase marked a significant improvement with: • Integration of Electronic Logging Devices (ELDs) and GPS systems • More accurate and reliable tracking data • Real-time freight visibility, allowing shippers to react swiftly to unforeseen events • Integration into TMS platforms • Multi-modal tracking capabilities • Ability to monitor efficiency of operations and manage resources effectively 📶 Visibility 3.0 (Emerging future): This phase represents the next evolution in load tracking, featuring: • Multi-dimensional tracking beyond just location • Temperature tracking for sensitive cargo • Altitude monitoring • Potential for advanced data analytics and predictive capabilities • Comprehensive visibility systems using data cleansing, machine learning, and multimodal stitching • Data products built on top of visibility products The progression from 1.0 to 3.0 shows a clear trend towards more sophisticated, data-rich, and proactive visibility solutions. It's been a blast to drive the industry toward richer, more actionable visibility at Trucker Tools. Brokers and shippers - what do you want to see out of the era of Visibility 3.0?

This industry is sitting on billions in hidden opportunities. For years, supply chains have been held back by blind spots. Now operators can see what was invisible before: Picture this: thousands of trucks moving across the UK right now. Many running empty, a problem that's barely changed in decades. After 25 years in this industry, I've watched operators call the same few carriers, hoping someone can take the job. By the time they find a truck, it's miles from optimal, while closer trucks sit empty. Complete visibility changes the game entirely. Take service failures. Without visibility, operators make desperate calls for hours. With real-time tracking, you instantly see a carrier 20 miles away while others are 50+ miles out. One decision saves hours and hundreds in costs. Two-legged journeys reveal even bigger wins. The old approach: 2 trucks each running one empty leg. With visibility: 1 truck handles both legs, the freed truck earns elsewhere. But here's what really shifts profits... Real-time data exposes invisible patterns: • Lanes consistently short on capacity • Routes with untapped supply • Times when rates spike predictably Smart operators reposition assets before demand hits. They capture opportunities others don't even see. The technology isn't just tracking. It's unified visibility across internal fleets and external carriers. Trustd removes compliance bottlenecks. Carriers get verified in hours, not weeks. When multiple 3PLs optimize using this visibility, capacity flows from oversupplied lanes to underserved routes. Efficiency compounds. Individual profit capture becomes industry transformation. We've been building this at TEG for over 2 decades. The operators winning today combine operational efficiency with deep market intelligence. They know exactly when and where to deploy newly efficient capacity. You've eliminated empty miles. Great start. But volatile markets demand current data to price lanes confidently. Facts protect margins. That's why we publish the TEG Price Index. Real market data showing what's actually happening with rates. Want to monitor what others are missing? Check the comments below to subscribe for free to the TEG Price Index 👇

For years, the industry has been chasing a comforting myth: the idea that real-time #visibility is just a "switch you flip". Lately, that promise has evolved into "instant onboarding". It sounds impressive. It makes for great headlines. But for many supply chain leaders, it’s a gamble that ends in a cycle of failed projects and data that teams simply cannot trust. The uncomfortable truth? Connectivity alone has never been the hard part. A connection is just a pipe. If what flows through it is incomplete, inconsistent, or poorly governed, you haven’t solved visibility – you’ve just automated a data junkyard. Real visibility is a deployment challenge, not a software install. It requires organisational readiness: cross‑team alignment, clear ownership, and a project‑based approach. It requires active involvement from procurement teams to ensure carriers provide the right data points – and continuous follow‑up to keep that data complete and reliable over time. At Shippeo, we’ve always viewed this differently. We don’t treat visibility as a passive software install or a "black box" of automated connections. We view it as an engineering discipline. We call it Visibility Engineering. That’s why we don’t just “onboard” carriers. We work across the full deployment lifecycle — from internal readiness to carrier engagement to ongoing data quality controls. And it’s why our Visibility Assessment isn’t just a technical checklist. It’s designed to answer a harder question: is your organisation actually ready to operationalise trusted data at scale? And most importantly, it’s the only reason we can confidently offer the industry’s only SLAs for Tracking Compliance, ETA Accuracy, and Carrier Onboarding. In a world where the window between planning and execution has shrunk to near-zero, you don't need more "pipes". You need engineered trust. Read more about the rise of Visibility Engineering here: https://lnkd.in/eb9uRcHT #SupplyChain #VisibilityEngineering #RealTimeVisibility #Shippeo

In 2014, I put eye-tracking devices on airline operations controllers. I wanted to measure cognitive load during a full OCC shift. Controllers and system users told me: “The ops system works fine. We know where everything is.” The eye-tracking data told a different story: → 47 unnecessary eye movements per decision → 12-second average search time for information that should be immediate → Repeated returns to the same screen because context was lost → Visual fatigue patterns identical to physical exhaustion One controller’s comment haunts me: “I didn’t realize how much I was searching until you showed me the video. I thought I was working. I was just… hunting.” Here’s what happened next: The eye-tracking partner I worked with, a company in Brandenburg, Germany, was acquired by Apple. That same technology now sits in every iPhone on every operations controller’s desk. Millions of people use it daily. Navigate with their eyes. Hands-free control. Seamless interaction. But airlines haven’t adopted it operationally. Not because it doesn’t work. Because it’s “not proven in aviation.” This is the innovation paradox: The people who run systems brilliantly can’t see what’s broken, because they’ve adapted so completely that dysfunction feels like normal workflow. Their expertise incorporates the workarounds. The hunting becomes invisible. The cognitive load becomes “just how the job is.” Then we ask them to evaluate replacement systems. And they score vendors on: “Does it work like we currently work?” Innovation gets rejected as “too different.” Incremental improvements win. Five years later, same problems. I published a white paper on this in 2017. Documented the cognitive cost. Showed how eye-tracking could revolutionize operations. Seven years later, the technology is consumer-grade. Available. Proven. Still unused in the operations centers that need it most. The question I can’t stop asking: Should the people who operate systems be the ones evaluating their replacements? Or are we asking brilliant operators to be visionary innovators, and wondering why we get incremental improvements instead of transformation? Operations professionals: When you evaluate new systems, do you score them on “works like we work” or “works better than we work”? Technology teams: What innovation are you building that operators will initially resist, because it’s that different? Full analysis further below.

⁉️ 𝙏𝙝𝙚 𝙦𝙪𝙚𝙨𝙩𝙞𝙤𝙣 𝙩𝙝𝙖𝙩 𝙘𝙝𝙖𝙣𝙜𝙚𝙨 𝙩𝙝𝙚 𝙙𝙚𝙢𝙤 I'm sitting in a carrier's conference room, watching a vendor wrap up a sleek AI claims demo. Fast triage. Clean interface. Impressive accuracy. Then the claims leader next to me asks: "𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝗸𝗲 𝗺𝘆 𝗮𝗱𝗷𝘂𝘀𝘁𝗲𝗿𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝘁𝗵𝗲𝗶𝗿 𝗷𝗼𝗯𝘀?" Silence. The vendor searches his slides like the answer might be hiding in 8-point font, then pivots to cycle times and cost per claim. The claims leader nods. But I watch the energy leave the room, not because the tech was weak, but because the human question wasn’t part of the story. I flew home thinking about that silence. And it changed the way I listen in demos. Here's what I've learned separates InsurTech vendors who get traction from those who stall: 1️⃣ 𝗧𝗵𝗲𝘆 𝗱𝗲𝘀𝗶𝗴𝗻 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗵𝘂𝗺𝗮𝗻 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗼𝗼𝗽, 𝗻𝗼𝘁 𝗮𝗿𝗼𝘂𝗻𝗱 𝘁𝗵𝗲𝗺 Insurance isn’t e-commerce. The adjuster calling a policyholder after a house fire isn’t a bottleneck to be automated. She’s the reason the policyholder renews. The best tools elevate her judgment, cut the grunt work, and surface sharper information exactly when she needs it. Peter Piotrowski put it well in his recent Digital Insurance article: technology should pass the "does it elevate our people" test. 2️⃣ 𝗧𝗵𝗲𝘆 𝗱𝗼𝗻'𝘁 𝗷𝘂𝘀𝘁 𝘀𝗲𝗹𝗹 𝗮 𝘃𝗶𝘀𝗶𝗼𝗻…𝘁𝗵𝗲𝘆 𝘀𝗼𝗹𝘃𝗲 𝘁𝗵𝗲 𝘀𝗲𝗾𝘂𝗲𝗻𝗰𝗶𝗻𝗴 Carriers don’t struggle with 𝘸𝘩𝘦𝘵𝘩𝘦𝘳 to adopt AI. They struggle with 𝘸𝘩𝘦𝘳𝘦 𝘵𝘰 𝘴𝘵𝘢𝘳𝘵 when systems are older than half the team and institutional knowledge lives in the heads of adjusters nearing retirement. Vendors who win help carriers pick one practical, high-impact starting point and bring people along without losing what makes them good. 3️⃣ 𝗧𝗵𝗲𝘆 𝗯𝘂𝗶𝗹𝗱 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗲𝘃𝗲𝗿𝘆𝗱𝗮𝘆, 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗱𝗲𝗺𝗼 The AI tools carriers can't live without rarely start glamorous...shaving 30 minutes off a repetitive task or surfacing a critical detail at the exact moment it matters. But the companies gaining real traction use that early trust to go deeper, shifting from "nice efficiency boost" to "load-bearing infrastructure." That's the difference between a point solution and a long-term partner. ➡️ 𝗛𝗲𝗿𝗲'𝘀 𝘁𝗵𝗲 𝗴𝘂𝘁-𝗰𝗵𝗲𝗰𝗸 𝗜 𝗻𝗼𝘄 𝘂𝘀𝗲 𝘄𝗶𝘁𝗵 𝗳𝗼𝘂𝗻𝗱𝗲𝗿𝘀: If your product disappeared tomorrow, would your clients notice by lunchtime…or next month? Build for lunchtime. I think about that claims leader often. He wasn’t trying to derail the deal. He was offering a roadmap to win it: "𝗛𝗼𝘄 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗮𝗸𝗲 𝗺𝘆 𝗮𝗱𝗷𝘂𝘀𝘁𝗲𝗿𝘀 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝘁𝗵𝗲𝗶𝗿 𝗷𝗼𝗯𝘀?" Answer that - clearly and specifically - and you won’t watch a room go quiet again. #InsurTech #AgenticAI #InsuranceInnovation