In a recent episode of The Agile Localization Podcast, Jourik Ciesielski, CTO of ELAN Languages, highlights a significant shift in how translation management systems (TMS) are perceived and utilized in the localization industry. He argues that traditional feature-based decision-making is becoming obsolete as AI integration reveals the limitations of existing TMS architectures. Instead of merely ticking boxes for features, localization teams should focus on their actual workflows and build technology around those needs.

This shift towards a workflow-first mindset is crucial for both enterprise buyers and language service providers (LSPs). Enterprises require specific integrations and streamlined processes, while LSPs need flexible solutions that can adapt to various client demands. Ciesielski emphasizes that TMS vendors should move away from rigid, feature-bundled products and embrace an open ecosystem that allows users to customize their workflows with building blocks like APIs and AI models.

The key takeaway for localization professionals is the importance of architecture over superficial features. As AI continues to evolve, TMS platforms must prioritize connectivity and flexibility, empowering users to create tailored solutions that meet their unique business objectives.

Source: crowdin.com