Enterprises Abandon Internal AI Builds as In-House Share Falls to 24%
Updated
Updated · O'Reilly Media · Jun 17
Enterprises Abandon Internal AI Builds as In-House Share Falls to 24%
3 articles · Updated · O'Reilly Media · Jun 17
Summary
Enterprise AI teams are being warned that internal “agent platforms” are usually mis-scoped projects, because memory, governance, evaluation and orchestration each behave like separate product categories rather than add-on features.
Menlo Ventures data underpins the case: the share of enterprise AI solutions built internally dropped to 24% in late 2025 from 47% in 2024, suggesting the build-versus-buy market flipped within 12 months.
The biggest hidden costs sit in agent-specific requirements such as persistent memory, action-level governance and trajectory-based evaluation, with 78% of executives lacking confidence they could pass an AI governance audit within 90 days.
Orchestration is also still unsettled across competing frameworks and emerging protocols, making custom stacks expensive to maintain as models, tool standards and design patterns keep shifting.
The article argues companies should build business-specific agents on top of bought components, especially with the EU AI Act fully enforceable for high-risk systems in August 2026 and Gartner expecting 40% of agentic AI projects to be canceled by 2027.
Why are 40% of corporate AI agent projects doomed to fail before 2028?
Your new AI agent has 'excessive agency.' How do you prevent it from going rogue?
76% of Enterprise AI Now Vendor-Built: The Rapid Shift, Risks, and Strategies for Sustainable Adoption (2025-2026)
Overview
In 2025, enterprise AI adoption experienced a dramatic and rapid transformation. Companies shifted sharply from building AI solutions internally to purchasing them from external vendors, with only 24% of use cases developed in-house compared to 47% the previous year. This change marked a clear preference for external solutions, as 76% of AI deployments were vendor-supplied. The move away from resource-intensive internal development allowed businesses to access specialized AI capabilities more quickly and efficiently, signaling a pivotal moment in how enterprises approach AI integration and setting the stage for a new era of vendor-led innovation.