The Buy-or-Build Decision, Revisited: How Agentic AI Changes the Economics of Enterprise Software

TL;DR

This paper reevaluates how agentic AI impacts enterprise software decisions, showing AI shifts the economics of building versus buying, especially for commodity and custom applications, but not for regulated systems.

Contribution

It offers a factor-level analysis of AI's influence on decision determinants, a typology of enterprise applications based on AI sensitivity, and insights into governance shifts in in-house development.

Findings

AI reduces costs for building software in certain categories

Most enterprise applications are less affected by AI-driven make-or-buy shifts

Regulated and mission-critical systems remain predominantly bought

Abstract

Advances in generative artificial intelligence, particularly agentic coding systems capable of autonomous software development, are disrupting the economics of the make-or-buy decision for enterprise applications. The "SaaSocalypse" narrative predicts that AI will render large segments of the Software-as-a-Service market obsolete by enabling firms to build software in-house at a fraction of historical cost. This paper adopts a conceptual research approach, combining transaction cost economics and the resource-based view with an assessment of current AI capabilities, to systematically re-evaluate the factors underlying the make-or-buy decision. It makes three contributions. First, it provides a factor-level analysis of how AI reshapes seven canonical decision determinants: cost, strategic differentiation, asset specificity, vendor lock-in, time-to-market, quality and compliance, and organizational capability. Second, it develops a typology of enterprise applications by their sensitivity to AI-induced shifts in make-or-buy economics. Third, it demonstrates that AI fundamentally transforms the governance properties of the Make option, shifting it from Williamson's pure hierarchy to a hybrid governance form that combines code ownership with external AI infrastructure dependency, with qualitatively different economics, capability requirements, and governance structures than pre-AI in-house development. The analysis finds that the SaaSocalypse thesis is overstated for most enterprise application categories; Make is most compelling for commodity utilities and differentiating custom applications in the AI era, while regulated and mission-critical systems remain predominantly in the buy domain.