In an environment where volatility is the only constant, simply tracking shipments isn’t enough. Supply chain professionals have outgrown passive visibility. They need systems that do more than just show them where a truck is. They need systems that help them decide what to do next.
That’s the shift project44 is taking on. Its Movement platform, built on a foundation of robust data connectivity and powered by Supply Chain AI, isn’t just telling people what’s happening. It’s helping them take action—and, in some cases, letting the system take action on their behalf.
The evolution of project44
Over the past decade, project44 has gone from being one of the early API visibility players to something much bigger: a decision intelligence platform designed to manage disruption at scale and enable smart automation across the supply chain.
Chapter 1: Connect
When project44 launched in 2014, it focused on one critical gap in the freight ecosystem: the lack of real-time digital connectivity between shippers, carriers and logistics providers. EDI was still the norm, and that made everything slower than it needed to be. The team behind project44 saw an opportunity to introduce API-based communication to a space that desperately needed more timely, accurate data.
By 2015, project44 had launched the industry’s first one-to-many LTL API network, a key milestone that brought near-instantaneous data exchange to a traditionally fragmented mode. This early push into digitization helped fuel the company’s rapid growth and earned them some major customer relationships.
This wasn’t a quick or cheap win. According to project44 Founder and CEO, Jett McCandless, building out this foundational layer required hundreds of millions of dollars in investment. It also took years of hard work to establish a standard of quality and reliability that could support high-stakes decision-making for players across different modes and across the globe.
That investment paid off. The company’s connectivity infrastructure became the backbone for everything that followed.
Chapter 2: See
Once those digital pipes were in place, project44 shifted focus. Instead of just piping raw data between players, the team started to contextualize it—presenting it through clean, unified dashboards that offered real-time shipment visibility across all modes and geographies. This was the “visibility” phase of the platform’s development.
“Visibility was chapter 2 for project44,” McCandless said.“You’re transforming raw data into actionable, contextualized end-to-end intelligence.”
What made project44 stand out was its ability to deliver it in a consistent, accurate way across an increasingly global supply chain. Today, the platform supports tracking in over 180 countries and integrates with more than 1,200 telematics devices, 190+ TMS systems and thousands of global carriers.
Still, seeing a problem and solving it aren’t the same thing.
Chapter 3: Act
As customers began relying on project44 to monitor their supply chains, they started asking for more. The question shifted from “Where’s my shipment?” to “What should I do about this delay?” That shift in customer expectation led to the next phase of the platform: intelligent workflows that could guide users through response and recovery.
“Our customers don’t just want to see problems, they want to solve the problems. And they really want it instantly,” McCandless said.
This “Act” layer of the platform is where automation really starts to take hold. Instead of just flagging a disruption, the platform now helps users understand which shipments are affected, what the downstream consequences might be and what actions they can take next. For example, if a shipment is late and risks missing a key deadline, the platform can surface pre-vetted rerouting options, complete with cost estimates and carrier performance data.
What’s notable here is that this isn’t AI for the sake of trend-chasing, it’s AI grounded in real operations. According to McKinsey, AI-enabled supply chain planning can reduce logistics costs by up to 15%, improve inventory levels by 35%, and boost service levels by up to 65%. Platforms like project44 are putting those numbers within reach.
Chapter 4: Automate
The latest phase in project44’s evolution is automation. This phase is not just about alerts or suggestions, but actual decision-making carried out by AI agents working within predefined parameters. This phase is where project44 envisions the supply chain of the near future: one in which critical decisions like booking, routing or mode-shifting can happen autonomously, triggered by real-time data and guided by historical patterns.
This doesn’t mean removing humans from the equation. Quite the opposite. The platform is built to empower supply chain professionals to focus on high-level strategy rather than being trapped in tactical firefighting. In other words, the AI handles the noise so the humans can focus on the signal.
It’s a vision that’s gaining traction. Gartner predicts that by 2026, over 75% of organizations will have adopted some form of AI-based decision intelligence in their operations. With supply chains growing more complex and more global, the need for this kind of scalable automation is becoming business-critical.
The future: decision intelligence in action
At its core, project44’s journey reflects a broader industry shift from data aggregation to data action. Visibility alone no longer satisfies the demands of modern logistics. The real value lies in what companies do with that information.
In today’s high-pressure environment, decision latency can be as damaging as data inaccuracy. The ability to anticipate a disruption, evaluate the options and execute a plan—all within seconds—can be the difference between meeting a customer’s expectations and losing a contract.
That’s the power of decision intelligence. It’s not just about knowing what’s happening. It’s about being ready to act before the problem snowballs. And increasingly, it’s about letting the system act for you when the conditions are right.
The long-term vision for project44 is to serve as the control tower not just for logistics tracking, but for logistics decision-making. That means continuing to refine the platform’s AI models so they’re not just accurate, but trustworthy and consistent.
In the end, the company’s trajectory mirrors that of the broader logistics industry. Connectivity was step one. Visibility was step two. Action and automation are what comes next.
For logistics teams facing labor shortages, cost pressure and increasing customer demands, that progression feels less like a luxury and more like a necessity. The question is no longer whether to automate, it’s how soon can you get started.
Learn more about how project44 is helping supply chains move faster and smarter at project44.com.
