GymBeam replaced 42 translators with AI. The real story is its automation operating model

GymBeam’s reported replacement of 42 translator roles is only one part of a wider transformation. Its localisation, customer service, logistics and internal workflows show what automation at European e-commerce scale actually requires.

A headline about GymBeam replacing 42 translators with artificial intelligence is designed to provoke a simple reaction: AI has taken people’s jobs.

That happened according to the company’s founder and CEO, Dalibor Cicman, whose comments were reported by Root after a Startitup discussion. Cicman said GymBeam had dismissed 42 translators as AI took over translation capacity. He also said the company now has approximately 30 engineers developing autonomous AI agents.

But the number of dismissed translators is not the most useful part of the story.

GymBeam is not applying one translation tool to one department. It is redesigning how a fast-growing European e-commerce company handles products, content, customer service, software, procurement and logistics across many markets.

The real lesson is therefore not that every company should replace translators. It is that automation creates a competitive advantage only when it becomes part of the operating model.

What is confirmed and what is reported
The dismissal of 42 translators and the figure of approximately 30 AI engineers come from statements attributed to GymBeam founder Dalibor Cicman in Czech and Slovak media coverage.
GymBeam’s own 2025 annual report confirms the wider automation strategy, including AI localisation for more than 16 markets, autonomous agents, automated customer service, logistics robots and a reduction in headcount while revenue continued to grow.

The company behind the automation story

GymBeam began in Slovakia in 2014 and has grown into a major European direct-to-consumer sports-nutrition and fitness business.

Its unaudited 2025 annual report states that the group generated EUR 232 million in revenue excluding VAT, an increase of 24 percent year on year. It served approximately 2.68 million active customers, sold to more than 50 countries and operated localised teams in 16 European markets.

The scale of its content operation is particularly important. GymBeam manages approximately 11,500 stock-keeping units across sports nutrition, food, clothing and accessories. Every market may require product names, descriptions, ingredients, instructions, category pages, campaigns, customer emails, help content and search metadata.

At that volume, translation is not a stack of documents waiting for a linguist. It is a continuous production flow connected to product data, compliance, content management and market launches.

GymBeam reports that automation within its product-information system allows it to localise content for more than 16 markets almost immediately. This is the operational foundation behind the headline about translators.

Translation was probably not the first thing that changed

A company cannot safely automate multilingual publishing by giving thousands of product descriptions to a general chatbot.

Before AI can scale localisation, the underlying content must be structured. Product attributes need consistent names. Ingredients, sizes, units, warnings and claims must live in defined fields. The system must know which text is commercial copy, which is regulated information and which must remain unchanged.

A workable automated localisation pipeline typically needs:

a reliable source of product and content data,
rules defining which fields can be translated,
approved terminology for each language,
context about product category and intended audience,
automated quality checks,
human review for selected content and risk levels,
direct publishing into the correct market channel,
monitoring after publication.

The language model is only one component. The competitive advantage comes from connecting the full process.

GymBeam is automating far more than translation

The company’s own reporting describes artificial intelligence and robotics across several areas.

Product localisation

AI is connected to product-information management so that content can be adapted for more than 16 markets. This reduces the manual delay between creating a product and making it available internationally.

Customer service

GymBeam says its AI chatbot resolves approximately 10 percent of incoming customer requests autonomously. Another 15 percent is handled through a hybrid model combining automation and human support.

Internal AI agents

Autonomous agents are used for operational purchasing, data analysis, content-project management, software quality assurance and logistics. The annual report says departments using these agents have reduced manual work by 15 to 20 hours per week.

Software development

The company reports using AI-assisted development to shorten some feature cycles from months to approximately two weeks without expanding the team.

Creative production

Generative AI is used for product images, labels and voice recordings. GymBeam says this reduced creative-production costs by 68 percent.

Physical logistics

The automation strategy extends beyond software. GymBeam operates robotic AutoStore systems, picking equipment and production technology. Its Milan distribution hub includes 85 robots and 74,000 storage bins.

This matters because the company is not treating AI as a separate innovation project. Digital and physical automation are being combined around one objective: process more products and orders across more markets without increasing headcount at the same rate.

The measurable result is productivity, not the number of AI tools

GymBeam reports that its workforce fell from 703 people in 2024 to 650 in 2025, a reduction of 8 percent. During the same period:

revenue increased by 24 percent,
the number of active customers increased by 21 percent,
orders increased by 20 percent,
revenue per employee increased by 33 percent,
EBITDA increased by 35 percent.

These are company-reported figures and should be read in the context of an unaudited annual report. They nevertheless illustrate the metric that matters.

Automation should not be evaluated by the number of prompts, licences or internal AI demonstrations. It should be evaluated by whether the business can process more orders, launch more products, support more customers or enter more markets with lower marginal operating effort.

The dismissal of 42 translators is a visible and socially sensitive consequence. The strategic result is a different cost structure for international growth.

Translation and localisation are not the same task

AI can generate fluent text in many languages. That does not mean every output is ready for publication.

Translation transfers meaning from one language to another. Localisation adapts a product and customer experience to a specific market.

For an e-commerce company, localisation includes:

terminology customers actually use,
local search behaviour,
units, currencies and number formats,
product and packaging requirements,
permitted health and nutrition claims,
delivery and payment information,
returns and complaint procedures,
market-specific promotions,
tone and brand expectations,
customer-support escalation.

A grammatically correct translation can still be commercially weak, legally risky or confusing to a Czech customer.

This distinction is particularly important for sports nutrition and food. Ingredients, allergens, dosage, warnings and claims should not be handled with the same risk tolerance as an inspirational blog post.

The safer model is risk-based automation

Companies often frame the decision as human translation versus AI translation. A better model assigns different workflows according to risk.

Low-risk, high-volume content

Examples include internal categorisation, basic product attributes, ordinary support summaries and first drafts of repeated descriptions.

This content can often be automated with systematic checks and sample-based review.

Medium-risk commercial content

Category pages, campaigns, SEO text, email communication and important product descriptions need market context and brand control.

AI can produce and adapt the first version, but a local market owner should review terminology, customer relevance and factual accuracy.

High-risk content

Legal terms, safety information, allergens, regulated claims, medical-adjacent language and crisis communication require qualified human responsibility.

AI can support preparation and consistency checks, but the organisation must know who approves the final version and accepts responsibility for it.

The goal is not to place a human reviewer after every automated sentence. It is to design clear thresholds that route only the right content to human expertise.

What happens to the translator’s role

The GymBeam report is uncomfortable because 42 positions reportedly disappeared. It would be misleading to present this only as employees being freed from repetitive tasks.

At the same time, the automation of translation does not remove the need for language expertise. It changes where that expertise creates value.

A mature localisation operation still needs people who can:

define terminology and style,
evaluate model output,
identify cultural and commercial errors,
review regulated or high-risk content,
analyse customer feedback and search behaviour,
train systems using corrected examples,
decide when automation should stop,
take responsibility for final publication.

The difference is that one language specialist may supervise a system processing far more content than a traditional translation workflow could handle.

This creates fewer pure production roles and more responsibility for quality, governance and market knowledge. Not every employee can or wants to make that transition, which is one reason automation has real organisational consequences.

Why simply copying GymBeam would fail

GymBeam has several advantages that many smaller companies do not.

It has an internal technology team, structured product data, high content volume, operations across many countries and enough repeated work to justify custom automation. The annual report states that 14 percent of its workforce is allocated to IT and development.

A company with fifty products and two occasional foreign campaigns may not recover the cost of building autonomous localisation agents. A simpler translation-management process with AI assistance and human review may be more economical.

Automation also amplifies weaknesses. If product data is inconsistent, the system distributes inconsistent information faster. If terminology is unclear, every market receives a different interpretation. If no one owns quality, errors can reach thousands of product pages before anyone notices.

Companies should therefore not begin by asking which AI model GymBeam uses. They should ask whether their own content, data and approval process are ready to be automated.

What foreign companies entering Czechia should learn

A foreign company often treats Czech localisation as a task at the end of market entry: translate the existing website, publish it and begin advertising.

The GymBeam case shows a different approach. Localisation is an operating capability connected to product launches, customer service, data and logistics.

For entry into Czechia, that means answering several questions early:

Which content is generated repeatedly?
Where is the approved source information stored?
Which Czech terms must remain consistent?
Who checks product, legal and safety information?
How will Czech customer questions improve future content?
Can translations move directly into the website, product system and support tools?
Which content can publish automatically?
Which content requires a Czech specialist?

A Czech website is only the visible output. The real system must continue working when prices change, products launch, regulations are updated and customers start asking questions.

A practical automation roadmap

A company does not need thirty engineers to begin. It does need a controlled sequence.

1. Measure the current process

Record how much content is translated, how long publication takes, what it costs and where errors appear. Without a baseline, automation savings cannot be proven.

2. Structure the source content

Separate product facts, claims, instructions, metadata and promotional copy. Define which system is the source of truth.

3. Create terminology and market rules

Build an approved Czech glossary, tone guide, prohibited claims and formatting rules. Include examples of correct and incorrect output.

4. Automate one narrow flow

Start with a repeated, lower-risk content type. Connect input, translation, quality checks, review and publishing rather than testing isolated prompts.

5. Add risk-based review

Route high-impact or uncertain content to a Czech specialist. Do not waste expert time checking every predictable field.

6. Measure business outcomes

Track publication speed, cost per product, correction rate, search performance, conversion, support contacts and customer complaints.

7. Expand only when quality is stable

Move to more languages and content types after the first workflow produces reliable results. Scaling an unstable process creates a larger problem.

Metrics that reveal whether localisation automation works

Useful measures include:

time from approved source content to Czech publication,
localisation cost per product or content item,
percentage of content published without manual correction,
percentage routed to human review,
critical-error rate,
terminology-consistency score,
organic search impressions and ranking by market,
product-page conversion rate,
customer contacts caused by unclear content,
return or complaint rates linked to product information,
revenue per localisation employee,
time required to launch a new market.

Reducing translator headcount is not a sufficient success metric. The system must also protect trust, compliance and sales performance.

The real competitive advantage is the feedback loop

The strongest automation system does not only generate text. It learns from what happens after publication.

Search queries reveal the language customers use. Support tickets expose unclear descriptions. Returns identify missing information. Conversion data shows which explanations help customers decide. Human corrections reveal where the model repeatedly fails.

When this information flows back into terminology, prompts, product data and quality rules, the localisation system improves.

That loop is difficult for competitors to copy because it depends on the company’s own data and operations. The language model may be available to everyone. The connected process is not.

The main conclusion

GymBeam’s reported replacement of 42 translators is a real employment story, but it is not the complete automation story.

The company has connected AI to product information, customer service, software development, content production, procurement and logistics. Its own figures show why: it wants to increase revenue, orders and international coverage without building a workforce comparable to a traditional multinational retailer.

Other companies should not copy the layoffs or assume that fluent AI output eliminates local expertise. They should study the operating model.

Successful localisation automation requires structured data, approved terminology, risk-based review, clear ownership, direct system integrations and measurable business outcomes. Without those foundations, AI only produces words faster.

With them, localisation becomes infrastructure for international growth.

How Kodo can help

Kodo helps international companies build the practical Czech layer of their market entry. We connect local positioning, Czech web content, product information, customer communication and automation into workflows that can continue operating after launch.

We can help identify which localisation tasks are suitable for automation, prepare Czech terminology and content rules, map human review points and coordinate implementation with internal or external technology teams.

Contact Kodo

Sources

The figures on GymBeam’s performance and automation are based primarily on the company’s unaudited 2025 annual report. The reported dismissal of 42 translators comes from public statements attributed to the company’s founder.