RWS has published a commissioned Forrester Consulting study estimating that a composite global technology company could generate a 212% return on investment by combining AI-powered language technology with expert linguistic services. The September 2026 Total Economic Impact study models $21.7 million in localization-related benefits against $6.9 million in costs over three years, producing a reported net present value of $14.7 million. The findings highlight how enterprises are approaching AI-assisted translation not simply as a way to reduce language costs, but as a means of scaling multilingual content and supporting international launches.
RWS study links AI translation with measurable business value
The study examines an integrated RWS offering that combines Trados Enterprise, Language Weaver and expert linguistic services. It models how an enterprise could gradually increase automation while retaining specialist linguistic input for tasks that require human judgment, quality control and contextual understanding.
The financial estimates come from interviews with four decision-makers at organizations using RWS. Forrester combined their experiences into a hypothetical composite organization: a global technology company with annual revenue exceeding $30 billion. The model assumes the company expands its localization operations across 32 languages by the third year.
The results are estimates for this modeled organization, not audited results from one customer or a guarantee of returns for other businesses. The study’s assumptions, cost estimates and risk adjustments are central to understanding the headline ROI figure.
Automation is only part of the localization equation
The model identifies several sources of potential value. Translation savings account for one category, while avoided revenue loss from delayed international launches, more efficient localization project management and lower third-party validation costs contribute to the broader business case.
This approach reflects a challenge for enterprises that publish product documentation, software interfaces, support materials and marketing content across multiple markets. Translation is only one stage in a larger workflow that includes terminology management, review, quality assurance, approvals and publication.
Increasing translation throughput does not automatically eliminate those downstream tasks. If automated output requires extensive correction or additional validation, savings at the translation stage can be offset elsewhere. Combining machine translation and workflow automation with experienced linguists is intended to manage that trade-off.
RWS says the modeled organization increased its use of translation memory, machine translation, automation and self-service over the three-year period. As adoption matured, the financial benefits grew year over year. The underlying premise is that automation can expand as teams establish workflows, governance and confidence in the technology.
Human expertise remains part of the AI model
The study also points to benefits that are harder to quantify directly, including consistent brand voice, stronger governance, security and compliance readiness, and improved visibility into translation activity.
These considerations matter when organizations use AI to generate or translate content across regions. Literal accuracy alone may not preserve product terminology, cultural context, legal meaning or a company’s established tone. In regulated industries, content errors can also create review burdens and compliance concerns.
An integrated language platform can help centralize workflows and apply repeatable processes, but it does not remove the need for human oversight. Enterprises still need to decide which content can be handled through automated translation, which requires specialist review and how quality will be measured across markets.
The study’s financial model therefore makes a broader argument than replacing human translators with AI. It presents a staged approach in which automation handles more work as the operating model develops, while expert services remain available for tasks where linguistic and subject-matter judgment matter.
The limits of a modeled ROI
The headline figure should be interpreted in the context of the study’s methodology. Forrester’s Total Economic Impact™ framework models technology costs, benefits, flexibility and risks using customer interviews and financial assumptions. Forrester says its commissioned studies do not predict the returns every buyer will achieve and should not be treated as endorsements.
For companies considering similar investments, the business case will depend on existing translation volumes, language coverage, content complexity, review requirements and the costs of current localization operations. Organizations with highly specialized or regulated content may also need more human review than those translating routine materials.
RWS CEO Ben Faes argues that localization should be treated as a way to support international growth rather than solely as an operating expense. The study provides a financial model for that position, although individual companies would need to test the assumptions against their own workflows and costs.
The broader takeaway is that enterprise language AI is increasingly being evaluated as an end-to-end operating model. The potential gains extend beyond translation speed to launch timing, workflow management and quality assurance. Whether those gains materialize will depend on how effectively organizations combine automation with linguistic expertise and governance.
The study, The Total Economic Impact™ Of RWS: Cost Savings And Business Benefits Enabled By RWS’s AI-Powered Language Intelligence Platform And Expert Services, was conducted by Forrester Consulting on behalf of RWS in September 2026.
Market Landscape
Enterprise localization is shifting from translation-focused workflows toward integrated language operations that connect machine translation, translation memory, automation, terminology management and human review. Vendors are positioning AI as a way to scale multilingual content, while enterprises must weigh efficiency gains against quality assurance, security, brand consistency and regulatory requirements. Commissioned ROI studies can help frame the business case, but their composite-company assumptions should be assessed against each organization’s own costs and needs.
Top Insights
- RWS’s commissioned Forrester study models $21.7 million in benefits over three years, against $6.9 million in costs for a composite global technology company.
- The reported 212% ROI depends on modeled assumptions, customer interviews and risk adjustments; it is not a guaranteed return for every RWS customer.
- The solution combines Trados Enterprise, Language Weaver and expert linguistic services to connect AI translation with human review.
- Modeled benefits include translation savings, faster international launches, more efficient localization management and lower third-party validation costs.
- The study highlights brand voice, governance, security readiness and translation visibility as additional benefits beyond its quantified financial model.
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