Beyond the Hype: What Companies Are Really Learning About AI Translation

In the relentless pursuit of efficiency and cost reduction for translation services, many technology and eLearning companies that we know have rapidly embraced machine translation (MT) and AI tools over the past year.

We cannot blame them, although we’re a translation company that might perceive the tools as a threat to our jobs. Because the allure is irrefutably undeniable: a swift, seemingly effortless path to global content.

But what starts as a promising cost-cutting strategy often reveals serious cracks when internal teams try to manage linguistic quality alone without a linguistic or translation background.

We recently spoke with a mid-sized eLearning platform provider that decided to replace its language service providers (LSPs) with AI and internal post-editing of machine-translated or AI-powered content. Their initial optimism turned into a cautionary tale. One that’s becoming increasingly common across industries.

In this blog, we’ll walk you through:

  • What AI-powered post-editing is and why companies adopt it

  • The key challenges this client faced (with specific examples)

  • The real cost of “free” translation

  • How to balance AI use with expert support for best results

What Is AI-Powered Post-Editing and Why Does It Matter?

AI-powered post-editing refers to a process where machine-translated content is revised by a human editor either a trained linguist or an internal employee. It’s seen as a middle ground:

  • Not fully automatic like raw machine translation

  • Not fully manual like human-only translation

For many companies, this seems like an efficient compromise: leverage AI for speed, then use existing staff to clean up the result. A legitimate cost containment strategy at first sight.

But there’s a catch. Without linguistic training and proper tools, the post-editing burden becomes far heavier than expected, often cancelling out the initially perceived cost savings.

Case Study: 10+ Hours a Week Lost to Fixing “Good Enough” Translations

The eLearning company we spoke to recently made the switch. Their plan was:

  • Use MT for all content that was handled by LSPs

  • Let internal team members (non-linguists) do the post-editing

They were initially optimistic: English-French and English-German language pair output looked “pretty good”. Around 80% usable. English-Slovak and English-Polish language pairs were trickier. Closer to 70% usable.

However, this initial assessment severely underestimated the true effort required to bridge that remaining 20–30% gap, revealing a much higher hidden cost than anticipated.

High or low resource languages also impact the translation costs

This happened because of the phenomenon called high or low resource languages. High-resource languages are those for which vast amounts of digital text data are available. This includes:

  • Training data for AI’s large language models (LLMs): LLMs, like those powering advanced MT systems, are trained on enormous corpora of text from the internet. Languages with a large online presence and a wealth of translated content (e.g., parallel corpora like government documents, international organization texts, software localization files) are considered high-resource.

  • Monolingual data: Extensive amounts of text in a single language help LLMs and MT systems understand grammar, style, and context.

  • Linguistic tools and research: More research and development often go into high-resource languages, leading to better tokenizers, part-of-speech taggers, and other linguistic tools.

Conversely, low-resource languages have significantly less digital text data available for training. This scarcity impacts the performance of MT systems and LLMs.

The Immense Loss of Content Manager’s Time

Each content manager spent around 10 hours per week reviewing AI translations. These were highly skilled staff such as product specialists, content strategists, and instructional designers who suddenly found themselves:

  • Searching for terminology in online and internal glossaries

  • Double-checking prepositions, conjugations, and adjective agreements

  • Consulting native grammar forums and regional usage guides

  • Manually verifying high-visibility terms in client-facing materials

For example:

  • “Preventive vs. preventative” caused confusion in a medical learning module

  • “Habilitation vs. rehabilitation” was flagged by legal advisors for regulatory implications

  • French headers required gender and verb form adjustments that MT consistently missed

This wasn’t an edge case. It became routine.

The Hidden Costs of “Free Translation”

On paper, the company had eliminated a supplier invoice. But in reality, they incurred multiple hidden costs:

1. Lost Opportunity Cost

Instead of propelling the business forward with new product launches, cultivating client relationships through bespoke content, or innovating on core offerings, highly skilled team members found themselves bogged down in the tedious, time-consuming task of proofreading AI output. This wasn’t just lost time; it was lost opportunity for growth and strategic development.

2. Lowered Quality and Consistency

Even after hours of review, inconsistencies crept in. One German reviewer flagged:

  • Inconsistent use of “Sie” and “du”

  • Mixed application of Oxford commas and local punctuation rules

  • Shifts in tone across similar sections, affecting the learner experience

In one instance, an eLearning module designed to teach complex software functions used inconsistent terms for the same action across different sections. This directly hindered user comprehension and led to increased support tickets.

3. Increased Risk

Public-facing content was left vulnerable. For example, a Slovak translation of a critical safety warning in a training module, left vague and misleading for two weeks, created potential compliance risks and undermined user safety perception.

Why Language Expertise Still Matters

Translation is not just about rendering words from one language into another. It involves:

  • Adhering to linguistic conventions and grammar norms

  • Ensuring terminology consistency across learning modules

  • Adapting tone and formality for target audiences

  • Avoiding misleading or ambiguous phrasing

  • Preventing AI hallucinations where text appears correct but is factually wrong

This is where LSPs provide measurable value. At Starling, we blend:

  • Human post-editors with subject-matter expertise

  • Terminology tools and client-specific glossaries

  • AI assistance for speed always with rigorous human QA layers and linguistic review ensuring accuracy, consistency, and cultural appropriateness

How to Get the Best of Both Worlds

If you’re a tech company, eLearning provider, or smart device manufacturer, you don’t need to choose between automation and quality. There’s a better hybrid approach.

1. Use MT Selectively

Apply MT only to content types that tolerate lower stakes like internal memos, first drafts for review, or highly repetitive technical documentation with pre-approved terminology. Crucially, avoid raw MT for public-facing marketing materials, legal documents, critical user interfaces, or anything requiring high cultural nuance and brand voice.

2. Predefine and Enforce Terminology

Use a structured terminology database. Tools like Trados, memoQ, or Phrase can enforce term consistency and integrate with MT engines.

3. Work with Expert Post-Editors

Use trained linguists who specialize in your sector such as medical, technical, compliance, etc. to review content. Internal content managers or other employees can supplement, but they should not carry the entire QA burden.

4. Track Hidden Costs Accurately

Quantify internal hours spent on post-editing. Assign an hourly rate to your staff’s time (including benefits and overhead) to calculate the true internal cost of DIY post-editing. Use this data to compare with an LSP’s rates for full-service human+AI workflows.

5. Run A/B Quality Benchmarks

Translate one module internally and another via a professional workflow. Compare tone, accuracy, speed, and post-publication error reports.

Tips Before You Cut the LSP

Before going fully DIY, keep these reminders in mind:

  • MT quality varies widely across language pairs, especially for CEE languages

  • Native speakers notice inconsistencies and unnatural phrasing

  • Without structured QA, errors compound across modules and updates

  • Your in-house team’s productivity and morale suffers when overloaded with tasks outside their core competence

The Cost-Cutting Lessons Learned

The eLearning provider we spoke with learned a valuable lesson:

  • AI is useful, but not turnkey.

  • Post-editing takes more time than expected.

  • Internal talent is better used on growth and innovation.

After several months of mounting inefficiencies, they re-engaged with language experts and restructured their process, retaining the speed of AI, but restoring quality and reliability through expert oversight.

The result: higher learner satisfaction, fewer support tickets, and regained internal bandwidth.

StarLing‘s Workflow Audit for Your Translation Cost Savings

Are you wondering if your AI-based translation process is truly saving you money?

We offer a free 30-minute workflow audit where we’ll:

  • Assess your current translation setup

  • Quantify hidden costs and quality risks

  • Recommend a smarter hybrid model tailored to your content types and language pairs

Let Starling help you get more from your translation efforts, without overburdening your team.

Book your audit today here.