Q&A: AI-led decision intelligence and addressing fast-moving supply chains

Ongoing geopolitical volatility, shifting trade routes, extreme weather and other disruptions are putting more pressure on companies to build supply chains that can adapt in real time. Because of that, supply chain technology is entering a new phase as companies look beyond simply seeing and understanding disruption to using AI and decision intelligence to determine what matters and act faster.

Decision intelligence treats decision-making itself as something to be designed and measured. It rests on three pillars: trusted data, composite AI, and contextual analytics.

This requires an understanding as to how how ‘decision intelligence’ is changing the way companies prepare for and respond to global supply chain disruption. To gain insights, Digital Journal caught up with Brian Cooper, project44 CMO.

Digital Journal: As supply chains become more complex and fast-moving, why is Decision Intelligence becoming more important, and what are enterprises expecting it to enable that wasn’t possible five years ago? 

Brian Cooper: Five years ago, the industry was in survival mode. Container rates spiked more than 500% in a single year, and ships queued for miles outside major ports, unable to unload. The bar for success was low: know where your shipments were and hope they showed up. 

Supply chains today face just as much disruption, but the expectation has completely changed. Enterprises aren’t trying to survive one crisis anymore. They’re operating in a state where some kind of disruption is always live, somewhere in the network, and they’re expected to keep performing through it. That’s not a bar you can clear by reacting faster. It requires understanding the full network in real time, at a scale that wasn’t possible five years ago; today that means drawing on a live network of 282,000 carriers and more than 1.5 billion annual shipments to predict disruption up to 48 hours out, across more than 1,000 risk factors, and knowing what to do about it before it becomes a crisis. 

 Decision Intelligence isn’t built to help a company survive one crisis. It’s built for conditions that are permanent now. A dashboard can tell you a trend happened. It can’t tell you in time to do anything about it, and by the time the pattern is clear enough to act on, the opportunity to fix it is already gone. 

DJ: project44 has seen significant growth over the past year, including larger enterprise deals and deeper customer commitments. What does that momentum tell you about how the priorities of supply chain leaders are changing? 

Cooper: From our perspective, supply chain leaders haven’t changed their priorities, whether it’s reacting before a delay becomes a cost, trusting ETAs enough to plan around them, or protecting their own customer commitments. What’s changed is our ability to deliver on those priorities. We spent a decade building the world’s most accurate real-time logistics data graph, and it’s a foundation that only project44 has at this scale. 

Over the past year, we’ve focused that data foundation on more than just showing customers their network. We’ve turned it into action, by delivering technology that can find the signal in that noise, determine what action it calls for, surface it to the right person at the right moment, and increasingly, execute that response autonomously with AI. 

project44 is seeing that shift directly in our business. Customers are making bigger commitments because they’re asking us to take on bigger problems. Our new ARR grew 34% year over year in the first half, new-logo ARR grew 67%, and we’re seeing larger enterprise relationships, including our first $5 million ARR customer. 

 It’s also part of why we separated project44 and LSP44 into two focused businesses this year. Enterprise shippers and logistics service providers are both moving quickly toward AI, but their needs are fundamentally different. Shippers need an intelligence platform that can help run increasingly complex operations. Logistics service providers (LSPs) need infrastructure they can build their own differentiated experiences on top of. The market is getting more sophisticated about what it expects technology to do.  

DJ: With global supply chains facing overlapping disruptions — from geopolitical conflict and shifting trade routes to port congestion and extreme weather — how are companies changing the way they prepare for and respond to disruption in real time? 

Cooper: Supply chain risk management used to work scenario by scenario; one plan for a port strike, another for a storm, another for a trade disruption. That worked when disruptions mostly stayed in their lane. They don’t anymore. A single event rarely stays contained to the place it started. It moves through the network and changes conditions somewhere else before anyone would think to check. 

That changes what preparation even means. You can’t write a playbook for every combination of risks that might overlap. What you build instead is a network view that shows how a disruption in one place is already changing exposure somewhere else, before that second-order effect becomes its own crisis. That’s what project44’s Disruption Navigator Agent is built to do: it monitors more than 8 billion data sources across 120-plus risk categories, from natural disasters to labor strikes to geopolitical events, and maps them directly onto in-transit inventory. 

We publish that view publicly, too. Since the Strait of Hormuz closure began earlier this year, we’ve released our own data on it every few weeks: vessel diversions, port dwell times, which lanes are absorbing the rerouted cargo, and how that pressure moves downstream. Not a one-off report, but what a live network view looks like when a company actually has one. 

The companies handling this well aren’t the ones with the best playbook. They’re the ones who can see the whole network at once and catch where risks are stacking, before any one of them would show up as an emergency on its own. 

DJ: AI is now being introduced across nearly every part of supply chain operations. Where do you think AI is making the biggest difference today, and where are companies struggling to turn AI investment into real operational impact? 

Cooper: Most AI in supply chain still has a context problem. Companies plug in a general-purpose model, expect it to reason like an analyst who already knows their operation, and it doesn’t. It gives you an average-case answer, or a confident one that’s wrong for that specific network, because it was never grounded in this business’s shipment data, carrier history, or rules in the first place. That’s where most AI investment stalls: models that can reason well in general but have no way to reason about this operation specifically. 

The biggest gains happen when AI has access to real-time operational data, historical context, and the business rules that determine what matters. That’s where it starts taking real work off an operator’s plate across freight procurement, exception management, disruption response, and transportation planning. 

Mo, our conversational AI analyst, is a good example. A supply chain leader can ask a complex question in plain language and get an answer grounded in what’s actually happening in their operation, not an industry average, turning what used to be hours or days of manual analysis into an answer in minutes. Customers are already seeing what that’s worth: a $6 million reduction in detention and demurrage exposure, $200,000 in working capital freed from a single question, and up to $1.8 million in savings tied to a single disruption event. 

That’s where AI becomes real. It moves from generating an answer to helping an operator make a consequential decision with the context to back it up. 

DJ: Looking ahead, what do you think will separate the companies that build truly resilient, AI-enabled supply chains from those that simply add more AI tools to their existing operations? 

Cooper: You can connect an AI model to an enterprise quickly, but teaching it how that enterprise actually operates takes time. Every supply chain has years of carrier history, customer commitments, operating procedures, business rules and past decisions that determine what the right action is in a given moment. That’s what the world’s largest, most accurate, real-time logistics data graph is: more than 282,000 carriers’ worth of history, connections, and outcomes, continuously updated as freight moves. That institutional context is what allows AI to move from answering questions to making decisions you can trust. 

Autonomy is a dial you control. Companies will give AI more responsibility as the data, context and outcomes earn that trust. Some decisions will remain with humans. Others can increasingly be handled autonomously because the system understands the parameters and has enough context to act confidently. 

If you don’t trust the data, you’ll never trust the action. The companies building that foundation now will be the ones ready for where supply chain operations are going next.