Rethinking Build vs. Buy Decisions in Enterprise Software: Navigating Trade-offs through a Structured Decision-Support Approach
2026-06-29 • Software Engineering
Software Engineering
AI summaryⓘ
The authors address the challenge companies face when deciding whether to build software themselves or buy existing solutions. They propose a structured method that uses a detailed framework of decision factors like strategy, costs, risks, and technical needs to help make these choices clearer. Their system works even without past data by using rules and matching techniques, allowing users to weigh pros and cons and get clear recommendations. They demonstrate this approach with a finance example, showing how it helps explain decisions and when they might change over time.
Build-versus-buyEnterprise software developmentDecision-support systemOntologyRule-based reasoningCloud-native technologiesLow-code platformsTrade-off analysisRisk management
Authors
Janardan Misra, Vikrant Kaulgud, Adam Burden, Sanjay Podder
Abstract
Build-versus-buy decisions remain a persistent challenge in enterprise software development, shaped by competing strategic, technical, cost, and risk considerations. The increasing availability of third-party solutions alongside the growing feasibility of custom development through cloud-native technologies, APIs, and low-code platforms has further amplified the complexity of these decisions. In practice, organizations often rely on fragmented expertise and informal reasoning, making it difficult to systematically analyze trade-offs or justify decisions over time. This paper presents a structured decision-support approach designed to augment build-versus-buy decision-making in such contexts. The approach is grounded in an ontology of decision factors spanning strategic considerations, application characteristics, cost and budget constraints, and risk dimensions. It combines this factor model with rule-based reasoning and reference-level matching to support decision-making even in cold-start scenarios where historical data is unavailable. The approach is implemented as a lightweight advisory artifact that enables users to evaluate relevant factors, explore trade-offs, and derive recommendations with transparent reasoning. The applicability of the approach is illustrated through a finance domain case, demonstrating how structured factor analysis can clarify decision rationale and highlight conditions under which decisions may change over time. The results suggest that making decision criteria explicit and systematically comparable can improve the quality, transparency, and auditability of build-versus-buy decisions in enterprise settings.