Agricultural AI Value Capture: From Constraint Removal to Repeatable Deployment

Agricultural artificial intelligence can create substantial operational value without producing an equally strong technology business. A robot may reduce a scarce task, a decision system may improve reliability, or an autonomous system may expand feasible production, yet the supplier can still fail to identify the right payer, overcome adoption friction, deploy repeatably, or retain enough of the created value to sustain the offering. This paper develops an Agricultural AI Value-Capture framework that treats commercialization as a linked sequence from technology to constraint removal, economic value, buyer identification, adoption, deployment, and value capture. The framework is positioned against recent research on digital-agriculture adoption, interoperability, platformization, servitization, market power, and business-model innovation. Existing work establishes that value creation and value capture are distinct, that profitability and technical support shape adoption, and that digital agriculture increasingly relies on service- and platform-based models. The contribution here is narrower and operational: it makes deployment an explicit strategic link between adoption and capture, defines Deployment Replicability as the ability to reproduce a stable solution with bounded site-specific adaptation, and introduces Buyer-Value Alignment and Value-Capture Leakage as diagnostic concepts for agricultural AI businesses. The framework implies that superior AI is neither necessary nor sufficient for a scalable agricultural technology business. Stronger positions arise when a technology removes a binding constraint for an identifiable payer, creates value large enough to justify operational disruption, enters heterogeneous farms through a repeatable deployment system, and retains an economically viable share of the value after service, financing, competition, and ecosystem effects. The central claim is that agricultural AI should be evaluated not only by what it can technically accomplish, but by whether the path from constraint removal to repeatable value capture remains intact.