AI has started to change distribution economics in ways the industry has never experienced. McKinsey estimates that end-to-end AI adoption can reduce inventory by as much as 30 percent, cut logistics costs by double digits, and drive major improvements in service levels and working capital efficiency. Across the broader supply chain, early adopters are seeing significant gains in speed, resilience, and operating margin. And with AI-powered logistics projected to more than triple in market size by 2032, the direction is clear: the next era of distribution will be dominated by companies that can make the leap to operate like expert software platforms and maintain while optimizing their valuable warehouse networks.
The path to becoming an AI-first distributor begins with the foundational move of structuring all operational data in the cloud, even if the ERP and core systems remain on-prem. Using modern API and event-driven sync technologies, distributors can stream everything (quotes, purchase orders, shipments, transactions, supplier feeds, customer signals) into a cloud database that becomes the company’s first unified, real-time model of its business. This likely will have to be done step by step so it does not disrupt ongoing operations. It will take time and patience but will set the foundation of clean, structured, accessible data from which all future AI systems will be driven.
Once that cloud data spine exists, distributors can begin deploying specialized, AI-enabled point applications that immediately close capability gaps in their legacy systems. Pricing engines become more precise and governed. Quoting cycles shrink from hours to seconds. PO intake and document processing become automated. Predictive inventory models run independently of the ERP. Codified rules will be able to govern operational workflows with consistency. The hardest part is getting an entire organization to agree on what the operational workflow rules are. This is where your experts on the ground come in. Only the people that execute these tasks daily are the ones who can write the rules in such a way that will set up success. But the reality is that these systems will replace their daily functions, and for many (or dare I say most), this creates a lot of fear. Upskilling, paths to the AI-enabled future, and genuine organizational trust as just as critical to the success of this stage of the transformation as the clean cloud data. These targeted applications function as intelligence layers grafted onto the existing architecture, delivering quantifiable ROI while quietly preparing the organization for a much larger transformation.
Over time, this incremental shift gives rise to the operating model of the AI-first distributor. In this future state, data becomes the actual system of record, not the ERP. Every operational event is captured in the cloud as a time-stamped, query-able event and rule-bounded AI engines sit on top to optimize decision making and routing. Instead of siloed systems making isolated choices, a coordinated AI-driven orchestration layer determines what to buy, where to place inventory, how to route orders, how to price, and most importantly, when and what to escalate to people that can make nuanced, impactful decisions. People transition from repeatable, transactional tasks to managing policies, exceptions, and relationships, the work that our teams are uniquely equipped for.
This vision requires cloud-native architecture as both a hosting choice and also an operating philosophy. Microservices, event buses, elastic compute, and API-first integrations create the speed, visibility, and flexibility needed to respond to volatility with intelligence rather than brute force. In this model, the distributor becomes a software platform that also happens to move physical goods. The physical network is still the core competency but the center of gravity shifts so everything orbits with precision around the cloud.
Digital inventory becomes an engine for growth. When a distributor has a reliable, real-time digital representation of stock, availability, and conditions across suppliers, 3PLs, customers, and global nodes, it can orchestrate far more inventory than it physically owns. This unlocks new business models such as marketplace fulfillment, selective inventory in high-value categories, and the ability to price and promise with unprecedented precision. Growth becomes constrained not by warehouse space or working capital but by how much of the ecosystem’s inventory the platform can see, model, and influence.
There are a couple potential paths to be the first AI-first global distributor. One is the SaaS platform that steps into distribution by combining cloud-native workflows with behavioral intelligence, then taking selective inventory risk where it can generate leverage. While technologically feasible, this route will struggle due to a lack of deep domain expertise. Distribution is a relationship driven, reputation sensitive, extremely complex ecosystem. Outsiders often underestimate how genuinely difficult it is to scale inside it.
The second path is an incumbent distributor that successfully shifts to cloud based and data first operation. These companies have unmatched supplier relationships, operational expertise, and physical networks. And many are already experimenting with AI in logistics, warehousing, sales, and operations. Their challenge is not vision. Rather, it is the massive amount of technical, process, and organizational debt. They must standardize data models across business units, move intelligence out of the ERP, and redesign workflows so AI-enabled decision engines operate within clear, compliant guardrails. The success stories here will be those who take these steps early, deliberately, and with long-term discipline, who can implement these changes without disrupting the flow of business.
The most likely winner is a hybrid: a company that blends the architectural ambition of a software firm with the operational mastery of a distributor. It will start in a narrow vertical where data advantages matter most, build a cloud-native data and events platform as the backbone, layer AI-driven decision engines on top, and maintain a thin but strategically important physical layer supported by a world-class exceptions and relationship team. Once the digital foundation is strong, it can plug into new physical networks globally and expand with less capital than those who have not made the leap. I envision a possibility where an established distributor separates it’s cloud based operating system as an independent venture that operates like a software company, leverages the physical inventory of said distributor, and creates a broad network of partners to maximize access to physical inventory with minimal operational overhead.
Begin building your cloud data foundation now, even if your ERP remains on-prem for years. This single step unlocks all subsequent innovation and allows your organization to begin deploying the AI-enabled applications that will define the next decade of distribution. And then decide strategically how you want to participate in the evolving landscape: either double down on physical assets while partnering with cloud-native software platforms, or invest directly in the digital orchestration layer yourself. Either path is viable, what matters most is conviction.
The first true AI-first global distributor will treat data, intelligence, and decision engines as the core business, orchestrating a vast network of digital inventory while remaining capital-light in physical assets. They will expand through integrations, partnerships, and software and they will redefine how distribution flows.
I’m excited to see who builds it.
