Organizations are moving from purchasing commercial software to building their own applications with AI tools, shifting the ongoing maintenance and accountability from vendors to internal teams and MSPs. This creates governance gaps and operational risk that go unpriced, particularly as automation and custom app development become easier for enterprises, small businesses, and IT providers. Evidence comes from McKinsey’s State of AI survey, GoDaddy’s self-built app hosting, and direct MSP case studies.
McKinsey found that 32% of organizations—and nearly half of AI “high performers”—built functionality in-house using AI instead of buying it. GoDaddy now enables deployment and management of such customer-built apps. MSPs like ITECH Solutions and WheelHouse IT illustrated how staff can rapidly implement automations or replace core business software, increasing reliance on internally developed tools.
Despite accelerated development, ongoing responsibilities for patching, security reviews, and identity management remain. Incidents at Meta and OpenAI, as well as survey data from VentureBeat, highlight persistent risk and the lack of robust controls—especially around identity and security—for internally developed or AI-generated applications. Vendors such as Apple are responding with operating system-level controls, shifting the burden of informed consent and risk mitigation to the device layer.
For MSPs and IT leaders, each client- or internally-built tool represents a new asset to manage, often without additional compensation. Failure to explicitly govern, price, and assign ownership to these tools increases hidden costs and risk, particularly with flat-fee agreements. Practical measures include treating every custom build as a managed asset, assigning identities and permissions, and itemizing them in contracts to align pricing with actual support delivered.
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