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.

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

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 NEXT TRANSFORMATION OF U.S. SHIPBUILDING - A.I.  Interview with Eric Chewning, the executive vice president of maritime systems and corporate strategy at HII. - "the key to successful AI adoption in a set of shipyards more than a century old isn’t just about the technology, it’s about earning trust through demonstrated value." - we have to be hyper-focused on the change management aspects of tool introduction — finding the champions that will pilot an approach and letting them be the champions for their own craftsmen. - It all comes down to demonstrating value and getting pull from within the organization. As these early wins compound, we’re building a culture where shipbuilders see AI as a tool that amplifies their expertise rather than replaces it. - Across industries, AI development thrives where there is a wide body of curated data that it can use to test, train, and iterate. There are two fundamental challenges when we consider the shipyards: the nature of our production data and the fragmentation of our information technology systems. - On the first point, the places you see AI have a huge impact in manufacturing areas with high-volume and or standardized production. One can consider automotive manufacturing with hundreds of thousands of identical units being produced. In aerospace, the production lots are much smaller, but are much more standardized. 1. we are growing our capacity with strategic investments like the recently acquired manufacturing campus in Charleston, South Carolina. Upon transaction close, the unused capacity at W International was immediately repurposed to support submarine and nuclear-powered aircraft carrier production as part of our Newport News Charleston operations. - Second, we are investing in new industry 4.0 technologies like digital engineering, additive manufacturing, AI, automation, and robotics. Our partnership with C3 AI is demonstrating the value of AI to the deck plate by improving how we run our machine shops. Our High-Yield Production Robotic (HYPR) initiative leverages a network of emerging industrial technology companies like Path Robotics to rapidly accelerate our use of advanced automation solutions in the fabrication process. - Third, we are expanding the maritime industrial base by growing our supply chain and implementing a distributed shipbuilding strategy that will outsource over two million hours of work in 2026, a 30 percent increase from 2025. We have partnered with 23 smaller shipyards and manufacturing centers to make ship modules for integration within our shipyards. - Fourth, we are hiring, retaining, and training a 21st-century workforce through wage increases, a network of vocational schools, in-yard education, and world-renowned HII apprentice schools. - Fifth, we are building new shipbuilding infrastructure with over $600 million in capital investments planned for 2026 alone. - https://lnkd.in/eGJUhaag

In 2026, AI is the theme for telcos. Not just as a way to reset the cost base, but increasingly as a growth story: better personalization, and eventually new services. But here's the pattern I keep seeing: a lot of telcos are stuck in the POC phase. The ones pulling ahead are the ones ready to pivot to scale - getting real MVPs into production, testing with customers, and doing it at proper scale. KPN is one of those. Together with KPN - Netherlands' largest telecom operator - and QuantumBlack, AI by McKinsey, we've been building an agentic AI engine for customer care. Not a chatbot on a webpage: voice-to-voice, handling real calls, at the scale of millions a year. What it takes to get it right comes down to four things, and KPN moved on all of them: 1. Strategy: pick a domain that matters and reimagine it end to end. Customer care first, as the proof point for an AI-native enterprise. 2. Adoption: get real MVPs into production and test with customers, rather than polishing pilots. KPN reviews up to 100 real call transcripts each morning, refines the prompts, and ships the updates by evening. 3. Reskilling: as AI takes verification, order status, and troubleshooting, people move to the moments where judgment and empathy count. Adoption success rate is already over 86%. 4. Technology: the right tech stack and the right ecosystem choices. A reusable platform, sub-2-second voice responses, and "barge-in" so customers can interrupt naturally. The ambition: agentic AI handling 10–20% of service calls by 2027, with satisfaction already comparable to human-handled calls. Get the strategy, adoption, reskilling and technology right, and you can genuinely rewire a telecom operator to compete and win. Grateful to the KPN team and my colleagues Maarten Baeten, Dirk Hofland, Mate Maczik and Tobias Jongbloets. Read the full case: https://lnkd.in/exSw8G4V #AgenticAI #Telecom #CustomerExperience #AIbyMcKinsey

Every carrier says they’re ‘tech-enabled.’ Almost none can tell me why I should care. It started off as 'We're a tech-enabled carrier.' Now the trend is: “We’re an AI-powered carrier.” No doubt there is incredible technology out there. And to be fair—carriers have built some legitimately impressive tools: - Pricing engines - Visibility systems - Automation workflows - Dashboards - Bots - AI decision logic All of that absolutely helps your internal team become more efficient. But here’s the part most carriers never think through of when they say they are tech-enabled. If you can’t tie your tech to ROI for the shipper… then it’s not a differentiator. It’s just an internal tool. As an enterprise shipper, my reaction is always the same: "Thats Great!....what does that do for me?!” So if you're going to say you're a tech-enabled cyborg AI supercarrier then take it a step further and explain: - How it reduces your cost-to-serve - How it improves reliability - How it increases execution consistency - How it eliminates friction for the shipper That’s what matters. We dont price freight competitively because we have to. We price it competitively because we're more efficient. Now you’ve got my attention. That’s not a tagline. That’s a sustainable competitive advantage—in any market. Tie your tech to ROI. Tie the ROI to the shipper. Then lets talk.

The “hammer-and-nail” problem in fleet AI Is AI a real-world tool for fleet efficiency, or just a “hammer looking for a nail?” In the latest episode of the Fleet Lead, I sit down with Hans Galland and Mahriah Alf from BeyondTrucks to cut through the marketing fluff. We dive into why the 2019 ELD mandate was an “iPhone moment” for transportation data and how specialized carriers can move past “data overload” to find actual ROI in their operations. We also discuss the psychology of tech adoption—why dispatchers often ignore “perfect” mathematical routes—and how BeyondTrucks is using natural language interfaces to let non-technical staff speak directly to complex algorithms. From their new AI RateAgents that automate nuanced fuel surcharges to the high-stakes cost of errors in bulk hauling, this is a must-listen for fleet leaders looking to modernize without losing sight of “rubber-meets-the road” reality. Key takeaways: The power of multi-tenancy: Why a shared code base is the secret to cost-effective innovation for mid-sized fleets. Beyond the black box: Solving the adoption hurdle by creating AI recommendations that dispatchers actually trust. AI RateAgents: A look at how natural language is turning complex customer contracts into executable code in seconds. The “innovation” effect: Why two-thirds of surveyed carriers say AI’s biggest impact isn’t just automation—it’s a more creative workforce. Listen to the full episode here: https://lnkd.in/gSEHch-K #Trucking #AI #TransportationTech #BulkTransporter #FleetManagement #BeyondTrucks #LogisticsInnovation