Insights

The dominance of standard software like SAP and Salesforce was built on the high cost of custom development. As AI and 'vibe coding' make custom solutions affordable, this foundation is collapsing, rendering the rigid 'process standard' obsolete while making the underlying 'technical standard' of data consistency and system integration more critical than ever.

This article introduces a five-stage maturity model for enterprise AI, arguing that true maturity lies in process and data readiness, not just AI autonomy. It reframes progress by measuring the reduction of manual 'glue work' and helps organizations assess their current state before investing in AI solutions.

The data lakehouse architecture is becoming essential AI infrastructure, enabling a single dataset to serve two distinct consumers: the analytical 'signal layer' for predictions and the process-oriented 'AI agent' for contextual action. This shift is crucial as ERP vendors tighten direct data access, making the company-controlled lakehouse a strategic asset for scalable AI.

Salesforce and SAP have adopted opposing AI strategies: Salesforce's Headless 360 enables direct API access for AI agents, whereas SAP's new policy restricts it, forcing integrations through its proprietary Joule system. This divergence creates significant architectural and licensing challenges for companies using both platforms.

Salesforce's Headless 360 strategy opens its entire platform via APIs, enabling AI agents to operate within enterprise-grade governance. This architectural shift invites industrial companies to build AI-native processes on a trusted foundation, rather than risking security and compliance on external platforms.