Doing more with the same: how corporate localization teams are scaling smarter

Corporate localization teams are facing a familiar challenge in 2025 where translation demand is rising, but budgets aren’t. According to the latest Translation Technology Insights 2025 (TTI) report, which surveyed almost 2,000 translation professionals, 74% of corporates reported stable or growing internal demand for translated services, even as budgets remain flat year-on-year.
This squeeze is reshaping how localization gets done. Rather than slowing down, teams are re-engineering their workflows by leaning on automation, adopting selective outsourcing models and redefining what success looks like.
Automation as a budget multiplier
The TTI report showed that 58% of corporate teams have five or fewer employees highlighting lean operations. Faced with more content and fewer resources, many teams are turning to automation and internal tools to stretch their budgets further. Machine translation and AI-driven quality tools are no longer just about speed, they’re helping teams triage content, reduce rework and maintain brand trust.
High-volume, low-risk material can now be translated automatically and refined through automatic post-editing, while complex or high-impact content is reserved for human experts. This hybrid model lets teams maintain quality where it matters most, while cutting cost and turnaround time across the rest of their content mix.
Strategic outsourcing replaces volume delivery
The TTI 2025 data also shows a rise in selective outsourcing: corporate teams are sending fewer, smaller projects to language service providers (LSPs) –— but the projects they do outsource are strategically important.
Increasingly, this approach is supported by AI tools that can evaluate the quality of machine-generated translations and apply automatic post-editing to improve them. These tools help organizations triage content, identifying which segments truly require human input and refining the rest automatically. The result is a more selective outsourcing model, where only the content that AI can’t confidently handle is passed on for human review - potentially contributing to smaller project size.
Building a lean, intelligent localization ecosystem
The use of integrations and connectors to content repositories has increased , mainly by LSPs (rising from 18% to 24%). This growth is expected - LSPs gain significant efficiency when they can plug directly intotheir clients’ content workflows. What is promising is that 37% of respondents (and nearly half of LSPs) say that better integration with third-party tools is one of the most valuable ways translation software vendors can help them tackle current challenges. Integration isn’t just a technical feature - it’s a strategic enabler.
To evolve corporates must take a systems-level view of localization by auditing and optimizing workflows to identify where automation fits best, consolidating technology stacks across marketing, product and operations, and connecting localization platforms directly to business systems like CRM and analytics.
This integration gives teams visibility into how localized content performs, whether it boosts engagement, adoption or customer satisfaction, helping them prove the business impact of translation rather than measuring only volume or turnaround time.
The human + AI balance
The report makes it clear: the goal isn’t to replace humans, but to rethink how humans and AI work together. With 83% planning to invest in AI capabilities imminently, AI is now a core enabler of scale, but it’s the orchestration of knowing when and how to use it that defines success. For example, AI can help you to improve your translations further before you bring humans in for example with Language Weaver’s Machine Translation Quality Estimation (MTQE) and Trados Smart Review.
With 40% of corporate respondents saying they review more than half of their AI-generated translations, the future isn’t AI-only, it’s hybrid workflows that combine speed and scale with quality and trust. The most resilient localization teams are those who use automation intelligently while preserving human oversight, cultural nuance and brand integrity.
