Problem
Research questions and friction points this paper is trying to address.
make-or-buy decision
agentic AI
enterprise software
transaction cost economics
SaaSocalypse
The Buy-or-Build Decision, Revisited: How Agentic AI Changes the Economics of Enterprise Software
🤖 AI Summary
The rise of generative AI, particularly autonomous coding agents, has rendered the traditional “build-or-buy” decision logic for enterprise software obsolete. This study integrates transaction cost economics and the resource-based view to systematically reconstruct this strategic framework, arguing that in the AI era, in-house development has evolved into a hybrid governance model that combines code ownership with reliance on external AI systems. The paper identifies seven core factors shaping this decision and develops an application typology to demonstrate that generic tools and highly differentiated applications are better suited for in-house development, whereas compliance-critical and mission-critical systems remain more appropriate for procurement—thereby refuting claims of an impending “SaaS apocalypse.” Employing conceptual analysis independent of specific technical implementations, this work offers a novel theoretical paradigm for enterprise software strategy in the age of AI.
📝 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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Agentic AI
Make-or-Buy Decision
Transaction Cost Economics
Hybrid Governance
Enterprise Software
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