Mind The Gap

‍What We’re Learning About AI in Logistics (And Why It Matters)

Over the past few months, I’ve been having some revealing conversations with logistics leaders across North America about what’s actually happening. Through these, often frank discussions with customers and in our podcast series (The AI Ripple Effect), I have been able to get a real pulse on the big themes, issues and opportunities in logistics and how operators are adopting and adapting to AI.  Not the hype, not the theory, but the real, messy, human side of transformation.

The Pattern We Keep Seeing

There is a clear pattern that emerges in nearly every conversation. A logistics leader will say something like: “We tried automation before. It didn’t work.” Then they’ll describe a 12-month RPA implementation that broke the first time a document format changed. Or a WMS that promised intelligence but just gave them more dashboards to check.

I get it. The industry has been burned by solutions that sound transformative but deliver… complexity.

But here’s what’s different now, and why I’m genuinely excited about where we are headed.

It’s Not About Replacing People (It Never Was)

One conversation stands out. A VP of Operations told us: “I don’t need AI to replace my team. I need my team to stop spending 60% of their day copying data from PDFs.”

That hit home.

Because when you actually look at what’s consuming time in logistics operations, it’s not strategic work. It’s not customer relationships. It’s the repetitive, manual tasks that nobody wants to do but everyone agrees have to get done.

Processing BOLs. Extracting data from rate confirmations. Chasing down PODs. Triaging thousands of emails. Checking compliance on customs forms.

These tasks don’t require human judgment, they require human time. And that time adds up. Fast.

Our customers are seeing 90% time savings on document processing. Not because AI is smarter than their teams, but because AI doesn’t get tired of doing the same task for the 3,000th time that month.

The Data That Validates What We’re Seeing

I was reading a recent BCG report on AI in logistics, and it crystallized something we’ve been experiencing firsthand. They worked with a global logistics leader to implement GenAI agents for automating business-critical documentation, RFPs, customs paperwork, contractual agreements.

The results? Dramatically streamlined workflows, significantly reduced turnaround times, and greater agility in responding to customer demands.

What struck me most: they are seeing full ROI within 18 to 24 months. Not some distant, theoretical payback. Real returns are coming in fast!

But here’s the part that should make every logistics executive pause: BCG’s conclusion is that “proactive adoption is no longer optional but imperative.” Early adopters aren’t just getting efficiency gains—they’re positioning themselves as market leaders through superior customer service and operational agility.

This isn’t Ripple saying it. This is BCG documenting what’s happening across the industry right now.

The Jobs That Emerge When You Remove the Grind

The most eye-opening learning is not just the efficiency gains, it is what happens after the automation is in place.

Teams start doing different work.

A brokerage that automated their quote processing didn’t reduce headcount. Instead, their ops team started proactively identifying margin opportunities across lanes. They had time to build actual relationships with carriers and customers, not just transact with them.

A fulfillment provider told us their team went from “document processors” to “exception handlers and strategists.” The AI handles the 95% that’s routine. The humans focus on the 5% that actually requires judgment, creativity, and relationship-building.

That’s not a job replaced. That’s a job elevated.

It reminds me of something I learned from my time at GE. When Jack Welch first engaged the workforce in transformation discussions, an employee told him: “For twenty-five years, you paid for my hands when you could have had my brain as well, for nothing.”

That statement has stuck with me. Because that’s exactly what happens when teams are buried in manual or admin work. You’re paying for expertise, creativity, and strategic thinking, but getting data entry instead.

AI doesn’t just automate tasks. It unlocks the human potential that’s been buried under repetitive work all along.

And this aligns perfectly with what BCG found: by automating repetitive tasks, AI frees human talent to focus on strategic initiatives, innovation, and building deeper customer relationships, the actual differentiators in an increasingly competitive market.

Why 70% of Logistics Still Runs on Manual Processes

Here is the uncomfortable truth: while AI-enabled competitors are gaining 15-20% margin advantages, most of the industry is still drowning in manual work.

Not because they don’t want to change. But because the tools available haven’t been built for logistics.

Generic AI tools don’t understand tariff codes, chassis rules, or carrier pricing structures. Traditional RPA breaks when documents don’t look identical. Your TMS wasn’t designed to learn and improve over time, it was designed to record what already happened.

This now becomes less about technology, but more about purpose-built intelligence that actually understands the work.

What “Purpose-Built” Actually Means

When we say our AI agents are pre-trained on logistics workflows, here’s what that looks like in practice:

An agent processing a customs declaration doesn’t just extract HS codes, it knows which codes are high-risk, which require additional documentation, and which might trigger delays. It has been trained on years of freight, drayage, brokerage, and customs data.

Another agent handling quote requests can understand margin thresholds, customer history, lane profitability, and can flag opportunities or risks in real time.

Or Agents that can read hand written notes, understand the freight acronyms and automatically build the right loads first time in your nuanced TMS.

That’s why we’re seeing 99% accuracy versus 60-70% for generic tools. It’s not magic. It’s domain expertise baked into the intelligence.

The BCG case study highlighted something similar with RFPs, their AI agent draws on existing customer data and similar proposals to create document structures, highlighting gaps while allowing for personalization. The key here is the informed automation that understands context.

The Speed Thing (And Why It Actually Matters)

Customers now expect quote responses in seconds, not hours. That’s not a nice-to-have—it’s table stakes.

When your quote turnaround drops from 10 minutes to 30 seconds, you can suddenly respond to market changes in real time. You can test pricing strategies. You can handle 3x the volume without adding headcount.

Speed creates strategic flexibility. And in logistics, flexibility is often the difference between profit and loss.

BCG puts it well: AI adoption positions logistics companies to respond proactively rather than reactively to market shifts, enabling unprecedented agility and resilience. That’s not just efficiency, that’s strategic advantage.

The Integration Reality

One of our customers said something that stuck with me: “We don’t need another platform. We need something that makes our existing platforms actually work together.”

That’s the real challenge. Most operations are running on a patchwork of systems—TMS, WMS, accounting software, email, spreadsheets, carrier portals. The problem isn’t the systems themselves. It’s the manual work required to move data between them.

AI agents that can integrate across your existing tech stack don’t replace your infrastructure, they make it functional. They become the connective tissue that should have existed all along.

Mind the Gap

I am definitely seeing a gap opening up in the industry.

The operators who move now, by testing, learning, and deploying AI, will have a compounding advantage. Every month their AI agents are learning, improving, and handling more complexity.

Meanwhile, companies still evaluating or waiting for “the perfect time” fall further behind. Not because they are bad operators, but because the intelligence gap compounds faster than most people realize.

Category leaders historically capture 60%+ market share in logistics tech adoption. We are seeing that play out in real time.

BCG’s warning is stark: “Companies that delay embracing GenAI risk falling behind, unable to deliver on evolving customer demands or navigate complex supply chain disruptions effectively.”

That’s not fear-mongering. That’s pattern recognition from the world’s leading strategy consultancy.

Where This Goes Next

The most exciting conversations I’m having now are about what becomes possible when the manual work is automated.

What if your ops team could focus entirely on customer success instead of data entry?

What if you could identify profitable opportunities across your entire network in real time?

What if scaling your business didn’t require scaling headcount proportionally?

This is happening with customers right now. And the data backs it up, improved productivity, customer responsiveness, and data-driven decision-making capabilities aren’t future promises. They are current outcomes.

Final Thought

The recurrent theme from clients and guests on our podcast is that AI in logistics isn’t about replacing people. It’s about removing the repetitive work that prevents people from doing what they’re actually good at. That is building relationships, solving complex problems, and making strategic decisions.

The manual processes everyone accepts as “just part of logistics”? Those don’t have to be anyone’s job anymore.

And that is what I think the future might look like. Not fewer logistics professionals, but logistics professionals doing work that actually matters.

BCG recommends that executives begin by identifying high-value use cases tailored to their organization’s operational bottlenecks, establish clear performance metrics, and engage strategic partners to implement and scale AI solutions.

That is exactly the conversation we are having with our customers. It is not “should you automate?” but “where do we start to create the most impact, now?”

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Would love to hear your perspective. What manual processes are consuming your team’s time? What would you focus on if those were handled automatically? Let’s discuss – book a call today

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