Can AI-Built Internal Apps Solve Enterprises’ Digitalization Problems?

Summary

The article argues that tools such as WorkBuddy are lowering software development barriers and allowing business teams to quickly create forms, approval flows, reports, scripts, and internal query pages. This addresses long-tail, departmental, lightweight needs that often wait too long in IT queues, but it does not amount to a complete enterprise digital transformation. AI-built systems are best suited to simple, local, read-heavy workflows used by individuals or small teams; replacing or integrating ERP, CRM, and MES systems, governing master data, maintaining transaction consistency, handling complex permissions, and operating reliably at scale remain difficult. The article identifies enterprise integration as the missing foundation: AI control layers need registered tools and connectors, while legacy systems may require APIs, read-only replicas, intermediate tables, RPA, message queues, or specialized adapters. Write operations should not directly alter business data when stable interfaces are unavailable. Security must also be enforced below the model through user-identity propagation, policy engines, metadata describing tool permissions and risk, sensitive-field filtering, approval for high-risk actions, and complete audit logs. For software companies, basic CRUD and simple customization face pressure, while connectors, secure permission middleware, legacy modernization, vertical agents, and ongoing operational services become more valuable. The proposed enterprise structure is an AI control layer, tool registry, integration layer, identity and permission layer, and data governance layer, with IT shifting from pure builder to platform enabler and gatekeeper.