On October 14, 2026, FourKites, the leader in AI-powered supply chain transformation, unveiled Outcome-as-a-Service for Supply Chain™, a go-to-market model focused on the business outcomes that its customers are measured against. FourKites commits to deploying AI agents to accomplish one billion hours of operational duties for its customers over the next decade, as measured by a new company mission.
The shift reflects the company’s view of how enterprise software is evolving. With AI maturing, customers care less about the tools themselves and more about the end product those tools were built to create. The typical Fortune 2000 supply chain operates on 10 to 15 separate tools, each optimizing one function, and few companies can link them to the cost, service, and revenue mandates their executives set.
Outcome-as-a-Service for Supply Chain begins with those mandates. Each engagement starts with the number a customer is on the hook for, including on-time-in-full performance, expedited goods spend, or cost to serve. FourKites then reverse engineers the AI agents, workflows, and data needed to move that number.
Says founder and CEO of FourKites, Mathew Elenjickal, “We founded real-time visibility to show customers where the problem was. Outcome-as-a-Service is how we solve it for them. Every leader we work with is accountable for an outcome, not a capability. A VP of supply chain isn’t measured on whether they’ve adopted AI. They’re measured on whether the product arrives on time and in full, at the right cost.”
The network behind each result
When one commits to outcomes, FourKites AI agents solve problems for a customer, rather than flagging them, and that kind of autonomous execution is only as effective as the context behind each decision. Most enterprise AI is built on a company’s internal data, describing what goes on within its own four walls. The signals that determine if an outcome is met are outside the systems of any one firm. These include carrier performance on a lane and conditions at a port or receiving dock.
FourKites’ network graph links 1,600+ brands, 500,000+ carriers, and 3.6 million facilities across 176 countries. FourKites’ AI agents use that context before each action and learn from what happens after each action, so every prediction and decision is more accurate than a model trained on one company’s data alone. The agents also learn from each outcome, getting better over time for that customer. AI models are turning into a commodity.
Elenjickal adds, “AI models are becoming a commodity. What can’t be copied is real-world network data and the context around it. That context is what lets our agents act on a customer’s behalf, and it’s why we can commit to outcomes.”
For more than a decade, FourKites has been on a mission to create that network. The new mission evaluates the work it does for its customers.
FourKites has rebuilt every internal function over the last year to operate AI-native to deliver that scale of outcomes. Engineering automated the software development lifecycle end to end, going from biweekly releases to regular daily releases.
Instead of a traditional product catalogue, the new fourkites.ai has a page for each of the 70+ results, each connected to the AI agents that perform it. Visitors can browse by industry or domain or can start with Owen, an agentic outcome advisor that initiates every conversation with the buyer’s concern.
