Forrester: Quality control is becoming a problem for AI localization

The increasing role of artificial intelligence in localization is transforming how organizations approach global content and communication strategies, presenting both opportunities and challenges for businesses seeking to expand their international reach.

Current landscape: The localization industry is experiencing rapid transformation due to AI advancements, particularly in areas like real-time translation, content automation, and quality assurance.

• Traditional localization challenges are being reimagined through the lens of AI-powered solutions
• Organizations face new decisions about technology adoption, vendor relationships, and strategic planning
• The integration of AI tools is creating both opportunities and potential risks in global communication

Key technological shifts: Machine translation and large language models (LLMs) are fundamentally changing localization workflows and capabilities.

• AI-powered transcription and dubbing are becoming standard expectations for multinational meetings and events
• Neural machine translation (NMT) and LLMs are enabling expansion into previously unsupported languages
• Translation-as-a-Feature (TaaF) is being embedded into various business applications, though this requires careful management

Critical challenges: Organizations must address several key issues to optimize their localization strategies.

• Employee stress around language barriers can be mitigated through AI-powered communication tools
• Manual workflows persist despite available automation options
• Quality assurance needs to be built into the entire process rather than treated as a final step
• Many companies still limit themselves to traditional language pairs despite expanded AI capabilities

Strategic considerations: Success in modern localization requires thoughtful leadership and planning.

• Organizations need specialized localization leaders who understand both technical and strategic aspects
• Executive education about AI capabilities and limitations is crucial
• Vendor relationships should evolve from transactional to strategic partnerships
• A balanced scorecard approach helps measure ROI across customer experience, financial impact, agility, and operations

Security and governance: The implementation of AI in localization requires careful attention to security and oversight.

• Public AI tools pose risks when used for business communication
• Centralized oversight of translation features is essential to maintain consistency and efficiency
• Organizations should invest in secure, trained LLMs rather than relying on free public tools

Future implications: The rapid evolution of AI-powered localization tools suggests a fundamental shift in how organizations approach global communication, with traditional language support models potentially becoming obsolete within years. Success will depend on finding the right balance between AI automation and human expertise while maintaining a clear focus on audience needs and preferences.