The Question That Changed
Having implemented over 65 AI transformations with industry leaders as CTO and Co-Founder of Raft, I’ve gained a unique vantage point into how technology is reshaping global logistics. And across every engagement, the pattern is unmistakable: the companies winning the race aren’t the ones with glossy “AI strategies.” They’re the ones asking a far more practical question:
“Which processes that take three hours today should take three minutes tomorrow and why haven’t we fixed them yet?”
From my experience, that mindset shift is the strongest predictor of AI success. But mindset alone isn’t enough. Real impact demands organizational readiness, teams capable of absorbing change and cultures willing to operationalize AI at scale. The debate over whether AI is a bubble or a breakthrough misses the point. Hype is cyclical. Operational transformation is permanent. What’s happening beneath the surface is real, measurable, and already reshaping how work gets done.
In this article, I'll go over where adoption is accelerating and what changes AI will bring as it becomes the defining competitive layer across our industry in 2026 and beyond.
The Investment Got Serious
The strongest signal came from where budgets were being approved. What once lived in innovation labs has moved squarely into boardrooms - now funded as capex-backed transformation programs, with CFOs committing to multi-year ROI frameworks often built around the very outcomes Raft enables.
Industry data and what we’re seeing daily reflect the following:
- Capex-backed AI programs are accelerating. McKinsey reports a tripling of logistics AI initiatives funded as long-term capital investments.
- Board involvement is rising. Gartner identifies executive and board oversight as the strongest predictor of successful AI deployment.
- Jobs to be done are the driving motivations that define adoption paths.
- Freight operations teams prioritize speed and scale.
- Customs teams focus on accuracy and compliance assurance.
- Finance leaders seek efficient reconciliation, accurate and timely supplier payments, and faster shipper collections.
What unites these isn't technology, it's impact. AI budgets go where bottlenecks are most painful and ROI is most measurable. At Raft, we see this daily: organizations invest where operational constraints are blocking scale, and where removing friction unlocks immediate competitive advantage.
The Rise of Production-Grade AI in Supply Chain Operations
Over the past two years, one shift has fundamentally reshaped the industry: AI has moved from pilot to production. The era of cautious experimentation is over. Organizations are now deploying AI across mission-critical workflows at scale and the results speak for themselves. Across Raft's global deployments, organizations are reducing customs declaration time by 80-90%, increasing throughput 5-10×, and dramatically reducing compliance errors and post-entry corrections.
These aren't lab results, they're live operations under real conditions. AI is no longer proving it can work - it's proving it works, every day, in production and at scale. Teams trust it because it performs reliably while keeping humans in control where it matters most. The organizations moving fastest aren't simply adopting AI, they're redesigning their operational core around it.
This shift rests on a foundational principle: AI delivers maximum value when paired with human judgment. In high-stakes domains like customs and financial reconciliation, AI does more than process quickly and accurately, it surfaces the right information at the right time, flags exceptions that matter, and seamlessly hands off decisions that require human expertise.
Raft's global deployments prove this pattern consistently: the most powerful AI applications don't diminish human expertise, they amplify it to superhuman scale. The results are stronger controls, fewer corrections, faster workflows, and a fundamentally transformed customer experience.
What Our Platform Reveals About The Industries AI Adoption
Raft's platform reveals what's really happening in the industry: rapid, compounding acceleration. Document volume has surged over 700% since 2022. But the numbers only tell part of the story. The technology and organizational shifts underneath are equally transformative
First: Deployment speed has surged.
Implementations that used to take 6 to 9 months now routinely go live in 8 to 12 weeks. That is not just a function of platform maturity. It is the result of clearer requirements, stronger executive sponsorship, and teams increasingly knowledgeable about AI-first transformations.
Second: Adoption inside organizations has fundamentally changed.
In 2024, customers started with low-hanging fruit - single use cases rolled out to individual teams to prove the concept. Now in 2025, once validated, they're expanding programmatically and at scale, launching multiple use cases in parallel. The bottleneck has shifted from "Does this work?" to "How quickly can we deploy this across the organization?
Third: the questions have evolved.
We have shifted from "Can AI do X?" to "How do we prioritize the five things AI already does well?" and "How do we execute at scale?" The capability is assumed; the conversation is now about execution, upskilling staff, securing critical stakeholder buy-in, measuring ROI, and exploring downstream benefits like data standardization.
User growth, transaction volume, and engagement are all climbing. AI in logistics is no longer an experiment. It is becoming standard operating procedure.
The Path Forward: From Capability to Competitive Advantage
The gap between "AI can do this" and "our organization extracts value from this" remains punishingly wide. The difference between transformation and success AI deployments comes down to four principles:
- Start with business problems, not technology capabilities.
- Identify where time bleeds, errors cost money, and visibility gaps create risk.
- Deploy AI directly into those pressure points with measurable outcomes.
- Build depth before breadth - three fully deployed capabilities outperform fifteen disconnected pilots.
AI in logistics is no longer an aspiration. It is an operational reality. Not universally, not evenly, but undeniably so for the companies that execute with intention and discipline. Yes, we are still early. Many workflows remain untouched. The full transformation curve will unfold over years, not quarters.
But the fundamental question has already shifted.
It is no longer: “Will AI transform logistics?”
It is: “Who will scale fast enough to lead, and who will be stuck competing against business models they can no longer match?”
That divide is forming right now. And it is being written one deployment, one workflow, and one strategic leap at a time.