For LSIs, Language Technology Platforms Are the Main Gateway
to AI Translation
语言技术平台已成为语言服务
集成商的AI 翻译主通道
Language Technology Platforms (LTPs) have become the primary gateway for Language Solutions Integrators (LSIs) to access AI translation, according to the Association of Language Companies (ALC) 2025 Industry Survey, conducted by Slator.
语言技术平台（LTP）已成为语言服务集成商（LSI）接入 AI 翻译能力的主通道，这是 Slator 受美国语言公司协会（ALC）委托，在其《2025 年度行业调查》中得出的结论。
The survey collected responses from 114 small and mid-sized LSIs — mostly based in the US. Of these, 54 respondents confirmed they apply AI translation in their workflows and answered questions about how they access AI translation.
本次调查共收到 114 家小规模 LSI 回复，这些公司大多位于美国，其中 54 家确认已在生产流程中引入 AI 翻译，并进一步回答了接入方式。
The survey found that more than half (52%) of LSIs now access AI translation via translation management systems (TMS) — typically provided by LTPs — that connect to multiple AI translation engines.
调查结果显示，超过半数（52%）的 LSI 通过翻译管理系统（TMS）接入AI翻译，TMS则——通常由对接多家 AI 翻译引擎的LTP 提供。
This marks a nine-point increase from 43% in 2024 and signals a move toward flexible, multi-engine setups that allow LSIs to switch or mix models for different clients, domains, or languages, reducing reliance on any single engine.
这相比于2024 年的 43% 上升了9 个百分点。这一变化表明，LSI 正转向灵活的多引擎架构，可按客户、领域或语种自由切换或混合模型，从而降低对单一引擎的依赖。
The share of LSIs working directly with AI translation providers — such as DeepL, Google, or Microsoft — remained stable at 46%, suggesting that LSIs are not abandoning these partnerships but are increasingly routing access through the TMS layer, where integration, automation, and performance management can be centralized.
与人工智能翻译服务提供商（如 DeepL、谷歌或微软）直接合作的LSI所占比例保持在 46%不变，说明它们并未终止这类合作，而是越来越多地把接入点集中到 TMS 层，以便统一整合、自动化流程并集中管理性能。
In-house development of AI translation engines continues to decline — falling from 16% in 2024 to 11% in 2025. The report attributes this drop to “high costs, technical complexity, and faster innovation from external providers,” which makes in-house development harder to justify — especially for small and mid-sized LSIs.
人工智能翻译引擎的自研比例持续下降，——从 2024 年的 16% 降至 2025 年的 11%。该报告将这一下降归因于“高昂的成本、技术复杂性以及外部供应商更快的迭代速度”，这使得内部开发难以自圆其说——尤其是对于中小型LSI而言。
Rather than maintaining direct connections to AI translation providers or investing in costly in-house development, LSIs are increasingly relying on LTPs and their TMSs as the central hub for AI translation and AI-enabled workflows.
与其维持与 AI 翻译供应商的直连，或斥巨资自研引擎，LSI 愈发依赖 LTP 及其 TMS 作为 AI 翻译与 AI 赋能流程的“中枢”。
The ALC report concludes that “TMS vendors are becoming more strategically important” within the AI translation ecosystem — a shift that could redefine how LSIs, LTPs, and AI translation providers interact in the years ahead.
ALC 报告总结指出，“TMS 供应商在 AI 翻译生态中的战略地位正持续上升”，这一趋势或将重新定义 LSI、LTP 与 AI 翻译供应商未来几年的互动格局。
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特别说明：本文内容选自Slator官网，仅供学习交流使用，如有侵权请后台联系小编删除。
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摘译编辑：张易可
指导老师：刘婷婷
项目统筹：刘怡坤
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