AI Coding Agents Drive Corporate Shift From Buying Software to Building In-House

Corporate technology spending is undergoing a fundamental shift as agentic AI coding tools enable companies to develop custom software internally. According to a McKinsey & Company report titled 'The state of AI in 2026,' organizations are increasingly abandoning the traditional 'buy' model for third-party products in favor of a 'build' strategy.

Data from the report shows that approximately 32% of surveyed organizations have decided against purchasing specific software features or products because they can now be built in-house using agentic AI. This trend is most pronounced in the technology sector, where 41% of firms report this shift. Other high-adoption sectors include healthcare payers and providers at 39%, followed by professional services and energy/materials, both at 38%. Significant trends toward internal builds are also appearing in financial institutions (36%), media and telecom (34%), and pharma and medical products (33%).

The Rise of Agentic Development

Historically, corporate leaders viewed advanced AI capabilities as being beyond the reach of internal technical teams. This perceived gap necessitated external partnerships and the purchase of off-the-shelf software to maintain speed and competitiveness. However, the emergence of 'agentic' coding tools—AI systems capable of autonomously planning, writing, and reviewing code—has significantly lowered the barrier to entry for custom development. This allows firms to take direct ownership of their technology agendas rather than relying on the roadmaps of external vendors.

Implications for the SaaS Model

This shift poses a direct threat to the traditional Software as a Service (SaaS) business model by reducing the demand for generic corporate software. As companies move from the experimentation phase to scaling AI, the ability to create bespoke tools allows them to align software precisely with their specific internal workflows. By treating operating costs as a design constraint, enterprises are shifting the power dynamic away from software vendors and toward internal engineering teams.

The Enterprise Scaling Gap

There is a widening divide in how different sized companies are adopting these tools. Larger enterprises with revenues exceeding $1 billion are scaling AI significantly faster; 54% of these firms have scaled AI enterprise-wide, compared to only 33% of smaller firms. Furthermore, agentic AI adoption in larger firms increased from 27% to 40% across one or more functions, while adoption among smaller firms remained flat at 22%.

Lieven Van der Veken, a Senior Partner at McKinsey, notes that larger organizations are not only scaling faster but are beginning to take greater ownership of their change agendas. He identifies the rise of software coding agents and in-house development as a clear signal of this broader strategic pivot. Moving forward, the industry will be watching whether smaller firms can close the adoption gap or if the 'build' advantage will remain a luxury of the largest enterprises.