In a nutshell: A modern AI automation strategy is only as powerful as the underlying ERP infrastructure supporting it.
Many companies fail to implement AI strategies because their outdated ERP systems in the backend cannot keep pace with modern automation requirements. The gap between ambitious AI plans and legacy infrastructure is increasingly becoming a critical success factor.
The problem lies in a classic asymmetry: While areas such as marketing and sales are already thinking about intelligent workflows and AI-based processes, companies operate their core processes – inventory management, production, financial accounting – on legacy systems whose architecture dates back to the early 2000s. These older ERP platforms typically offer limited or no native interfaces for modern AI models or cloud services.
The technical hurdles are considerable: data silos in monolithic systems, missing APIs or proprietary integration protocols, lack of real-time data processing, and limited scalability for machine learning workloads. Any attempt to integrate AI solutions such as Claude AI or other large language models into such systems requires extensive customization or data bridges that increase latency and error susceptibility.
For CTOs, this means: a credible automation strategy does not begin with AI evaluations, but with an honest assessment of ERP infrastructure. Companies must decide whether to modernize their legacy systems – for example through API-first architectures or cloud migration – or switch to more flexible platforms. Without this step, AI investments remain fragmented and their return on investment limited.
Source: itwelt.at · Published June 19, 2026
Lumi AI News — AI-assisted curation in accordance with Article 50 EU AI Act. Paraphrase and classification by Lumi News Pipeline v1.7.1.