How Agentic AI Changes the Economics of Enterprise Software
Research on how agentic coding systems reshape make-or-buy decisions by dramatically reducing development timelines and CAPEX for enterprise applications.
How Agentic AI Changes the Economics of Enterprise Software
A new analysis of agentic coding systems reveals a fundamental shift in enterprise software procurement. Where custom development once required dedicated teams working over months, AI-augmented small teams can now achieve deployment speeds approaching SaaS procurement—eliminating one of the traditional arguments for buying off-the-shelf solutions.
What changed. Leading agentic systems now resolve approximately 80% of tasks on the SWE-bench Verified benchmark (which evaluates agents against real-world GitHub issues), up from just 13.86% achieved by early systems in 2024. This dramatic improvement means greenfield implementations, standard integrations, and reporting tools that previously required sprint cycles can now be completed in a fraction of the time.
Why it matters. When in-house development with AI agents becomes faster and cheaper than SaaS procurement, the economics of enterprise software flip. CAPEX for custom applications drops sharply, and ongoing adaptation (feature additions, refactoring, modifications) accelerates proportionally. This reshapes how enterprises evaluate make-or-buy decisions.
Builder takeaway. If you’re building agentic systems, focus on well-scoped applications with clear requirements—this is where agents deliver measurable speed gains and where enterprises will first adopt them at scale.