A View from the GALA 2025 Conference

These are uncertain times for many in the language services and localization industry. There was a palpable air of concern and angst in Montreal. This is to be expected given all the changes that we face from so many directions:  

• ·         Disruption of established government and trade policies
• ·         AI hype in general is threatening many white-collar jobs
• ·         Unrealizable expectations about the potential capabilities of AI technology from C-suite leaders that cannot be delivered
• ·         An emerging global economic slowdown after an already tough business year
• ·         High levels of economic and business uncertainty

 The day after the conference, I saw the following in my inbox from CSA Research:

I also saw an announcement for an upcoming webinar from Women In Localization with the theme: Maintaining motivation during disruption, which added the byline, "with constant change, staying motivated can be hard."  There is concern in the industry far beyond the community present at GALA.

However, the keynote presentation by Daniel Lamarre, CEO of the Cirque du Soleil Entertainment Group, provided a memorable, uplifting, and inspiring message to the attendees. I rate it as one of the best, if not THE best, keynotes in all the years I have been attending localization conferences. His message was relevant, authentic, and realistically optimistic while speaking to the heart.

He is uniquely qualified to speak to a doomy, gloomy audience, as he also faces challenges and has risen from what seemed insurmountable odds. In response to pandemic shutdowns in March 2020, Cirque du Soleil suspended all 44 active shows worldwide and temporarily laid off 4,679 employees, 95% of its workforce. Annualized revenue dropped from over $1 billion to zero almost overnight. And today, Cirque has to work to remain relevant to digitally obsessed world where many youth have never experienced a circus.

He engineered a recovery, and by early 2023/2024, revenue had climbed back to the pre-pandemic level of approximately $1 billion, though growth is expected to moderate around this level for the next couple of years. Leadership stated the recovery exceeded expectations according to financial market observers.

For someone whose primary focus is to find outstanding artists from around the world, provide them with a regular living, and curate entertainment that leaves the audience enthralled and inspired, he had a clear understanding of the challenges that business translation professionals might have in this age of AI madness.  Somewhat similar to what his organization faced during the pandemic, when the possibility of large audiences congregating to watch a magical musical circus-like performance in 45 cities across the world was an impossibility.

The heart of his message was about building the right mindset as we face challenges, to break through, which he said begins with continual investment in research and development and a strong focus on creativity. This is very much the ethos of Cirque and pervades their overall approach and culture. A summarized highlight of his message follows:

• ·         Creativity is foundational since it leads to innovation which in turn often results in market leadership.
• ·         Ongoing and regular reflection is essential to building creativity. 

• ·         Deep curiosity and the questions that it generates are a building block to discovering successful outcomes.
• ·         While it is important to focus on the problem to get a clear definition of the challenge, it is even more important to focus creatively on possible solutions.
• ·         Investment in research and development (e.g., AI impact on translation-related processes) and organizational creativity is essential to finding your value-add in challenging times.
• ·         Debate is a foundation of evolutionary creativity, and a culture that encourages debate is most likely to find the best outcomes and the best ideas that are not possible with hierarchical mandates.
• ·         Finding a purpose is more likely to create successful outcomes than goals and objectives.
• ·         The best ideas will come from a clear mandate, debate, and a defined sandbox rather than through unstructured, frequent meetings with rambling, unfocused discussions.
• ·         He encouraged the audience to focus much more on communicating the value-add of the business.

I’m not sure I have captured the essence effectively, and as they say, “you had to be there,” as he was successful in lifting everyone’s spirits. His ability to build connections and empathy was indeed unique. He closed by encouraging the GALA community to make more concerted and active efforts to raise their profile and communicate LSP/localization value creation characteristics in the marketplace aggressively, given the unrelenting AI hype.

 

Raising the LSP Industry Profile

This keynote triggered a recurring theme that attendees raised across several sessions about finding a better way to describe the value of service/product offerings to the marketplace. There were different opinions and views on whether translation, localization, langops, or something else would be the most effective professional self-descriptor to build a value-oriented communication message.

I decided to do some basic research on this subject, via LLM, and I have mixed feelings about the utility of the output, as it lacks insight and understanding. I summarize the unedited responses from 3 different LLM models (which all had very similar results) below:

GPT 4.1 Summary  

Normalized Google Trends Frequency (2022-2025)

 

	Avg Absolute Monthly Search Volume	Normalized Monthly Searches 0-100 Scale 3-Year Mean
Translation	1,000,000	79.9
Localization	200,000	40.1
LangOps	2,000	5.2

Which term provides the greatest reach?

• Translation is by far the most recognized and searched term globally. It is used by major platforms like Google, DeepL, and Microsoft for their consumer-facing services, which reinforces its dominance and public familiarity.
• Localization is important for industry professionals and clients needing cultural adaptation and more sophisticated services, but it has a narrower audience.
• LangOps is emerging in industry circles as a concept for scalable, AI-driven language operations, but its search volume and public awareness remain very low.

Key Points:

• If your goal is maximum visibility and broad customer acquisition, “translation” is the most effective term. It captures the widest audience, aligns with consumer expectations, and is the default for everyday users seeking language services.
• “Localization” is valuable for targeting clients who require cultural adaptation and market-specific solutions, but it should be used as a supporting term rather than the primary one.
• “LangOps” is best reserved for thought leadership, technical blogs, or when targeting enterprise clients already familiar with advanced localization operations.

“Translation” is the term with the greatest and widest reach for LSPs seeking to increase visibility and attract a broad customer base. It is the industry standard, the most searched, and the most recognized by both consumers and businesses. Using “translation” as your primary keyword will maximize your discoverability and support value-driven messaging for the widest possible audience.

Claude Sonnet 3.7 Summary  

Search Term Frequency Analysis

The Google Trends data shows significant differences in search interest across the three terms. "Translation consistently maintains the highest search interest, with values ranging from approximately 80-85 on the relative search interest scale (0-100). "Localization shows moderate interest, with values between 30 and 35, while LangOps demonstrates minimal interest, with values hovering around 2-3.

Translation

"Translation" consistently dominates the search landscape with approximately 2.5 times more search interest than "localization" and about 40 times more interest than "LangOps." This term has maintained its strong position throughout the three years with a slight upward trend.

Localization

"Localization" maintains a consistent moderate interest level, showing some fluctuation but generally staying within the 30-35 range on Google's scale. While significantly lower than "translation," it still demonstrates substantial search volume that has remained stable over the three years.

LangOps

"LangOps" shows minimal search interest, consistently remaining below 5 on Google's scale. Despite being positioned as "the future of scalable, efficient, AI-powered localization" and "the next evolution of localization," it has not gained significant traction in search behavior.

Building Value-Oriented Messaging

To build strong value-oriented messaging:

1.      Lead with "translation" in primary marketing materials, website headers, and SEO strategy to maximize visibility and reach.

2.      Differentiate through "localization" by explaining how your services go beyond basic translation to include cultural adaptation, which adds significant value.

3.      Introduce "LangOps" as an advanced concept for enterprise clients, positioning it as the strategic integration of language services into business operations.

4.      Bridge the terminology gap by explaining how translation evolves into localization and ultimately into LangOps as part of a maturity model for global businesses.

Perplexity Pro Summary  

This output is essentially the same as the data already provided above, as it probably draws its answers from these models. However, this summary seems to be more accommodating of the potential of LangOps, quite possibly because of the extensive use of the term AI in its descriptive materials. They also analyzed each term and its implications in more detail.

Strategic Term Selection for LSPs
Which Term Provides the Greatest Reach?

For an LSP seeking to increase visibility and reach a broader customer base, the choice of terminology is strategically important:

Translation: Maximum Reach, Minimum Differentiation

"Translation" would provide the widest potential reach due to its universal recognition. It's the term used by major platforms like Google, DeepL, and Microsoft[query], indicating its mainstream appeal.

Advantages:

• Highest search volume and recognition
• Understood by both technical and non-technical audiences
• Already associated with established tools and services

Disadvantages:

• Highly competitive space
• May position services as commoditized
• Doesn't communicate advanced capabilities beyond basic translation

Localization: Balanced Reach with Differentiation

"Localization" represents a middle ground, with growing recognition and the ability to communicate more sophisticated services.

Advantages:

• Communicates a more comprehensive approach than basic translation

• Established industry term with growing recognition
• o   Aligns with current industry trends for 2025
• Suggests cultural adaptation and market-specific expertise

Disadvantages:

• Lower search volume than "translation"
• May not capture the attention of clients seeking basic translation services

LangOps: Niche Appeal with Maximum Differentiation

"LangOps" offers cutting-edge positioning but with significantly limited reach.

Advantages:

• Positions the company at the forefront of industry evolution
• Communicates AI integration and advanced capabilities
• Potential to attract forward-thinking enterprise clients
• Lower competition for the term itself

Disadvantages:

• Very limited search volume and recognition
• Requires explanation to most potential clients
• May alienate clients seeking traditional service

Perplexity Conclusion

For LSPs seeking to maximize market reach and build value-oriented messaging, "translation" remains the most effective foundational term due to its universal recognition and substantial search volume. However, a strategic approach that leverages all three terms at appropriate stages of the customer journey will likely yield the best results.

As the industry continues to evolve, LSPs should monitor the relative popularity of these terms and adjust their terminology strategies accordingly. The rapid growth of "LangOps," despite its currently smaller search volume, suggests that forward-thinking providers may benefit from early adoption of this terminology when targeting enterprise clients seeking comprehensive language solutions.

Ultimately, the most successful terminology strategy will align with both current search behaviors and the evolving direction of the industry, positioning LSPs at the intersection of what clients are looking for today and what they'll need tomorrow.

AI in Enterprise Localization Panel

I was also involved in an interactive panel organized by Johan Sporre with Britta Aagaard, Gaëtan Chrétiennot, Georg Kirchner, and Konstantin Savenkov, who auto-summarized the session with GPT shown here.  We discussed misconceptions, opportunities, and the changing role of humans.

Here is the auto-summary:

🔹 AI is not just a better translation tool. It’s a set of technologies that require the right setup, people, and processes to work.
🔹 Many AI deployments in the enterprise are not delivering ROI. Localization is one of the few areas where AI shows clear value—but only when applied with care.
🔹 Clients now care about language in a new way. That opens the door to conversations we couldn’t have before—across IT, marketing, and other teams.
🔹 The real work is not about chasing new buzzwords. It’s about understanding complexity and helping others navigate it.
🔹 Our role is changing—from translation providers to solution architects, guiding AI through data, process, and purpose.

Also, a shoutout to Marina Pantcheva, who gave an instructive and entertaining presentation, which somehow managed to make Cleaning Dirty TM sound fun.

Congratulations to Allison Ferch and the GALA team for holding a successful and substantial conference in such difficult and tumultuous times.

The Evolving LLM Era and its Potential Impact

With the advent of Large Language Models (LLMs), there are exciting new possibilities available. However, we also see a large volume of mostly vague and poorly defined claims of "using Al" by practitioners with little or no experience with machine learning technology and algorithms. 

The signal-to-noise (hype-to-reality) ratio has never been higher, and much of the hype fails to meet real business production use case requirements. Aside from the data privacy issues, copyright problems, and potential misuse of LLMs by bad actors, hallucinations and reliability issues also continue to plague LLMs.

Enterprise users expect production IT infrastructure output to be reliable, consistent, and predictable on an ongoing basis, but there are very few use cases where this is currently possible with LLM output. The situation is evolving, and many expect that the expert use of LLMs could have a dramatic and favorable impact on current translation production processes.

There are several areas in and around the machine translation task where LLMs can add considerable value to the overall language translation process. These include the following:

• LLM translations tend to be more fluent and acquire more contextual information, albeit in a smaller set of languages
• Source text can be improved and enhanced before translation to produce better-quality translations
• LLMs can carry out quality assessments on translated output and identify different types of errors
• LLMs can be trained to take corrective actions on translated output to raise overall quality
• LLM MT is easier to adapt dynamically and can avoid the large re-training that typical static NMT models require

At Translated, we have been carrying out extensive research and development over the past 18 months into these very areas, and the initial results are extremely promising, as outlined in our recent whitepaper.

The chart below shows some evidence of our progress with LLM MT. It compares Google (static), DeepL (static), Lara RAG-tuned LLM MT, GPT-4o (5-shot), and ModernMT (TM access) for nine high-resource languages. These results for Lara are expected to improve further. 

At Translated, we have been carrying out extensive research and development over the past 12 months into these very areas, and the initial results are extremely promising, as outlined in our recent whitepaper.

One approach involves using independent LLM modules to handle each category separately. The other approach is to integrate these modules into a unified workflow, allowing users to simply submit their content and receive the best possible translation. This integrated process includes MTQE as well as automated review and post-editing.

While managing these tasks separately can offer more control, most users prefer a streamlined workflow that focuses on delivering optimal results with minimal effort, with the different technology components working efficiently behind the scenes.

LLM-based machine translation will need to be secure, reliable, consistent, predictable, and efficient for it to be a serious contender to replace state-of-the-art (SOTA) NMT models.

This transition is underway but will need more time to evolve and mature.

Thus, SOTA Neural MT models may continue to dominate MT use in any enterprise production scenarios for the next 12-15 months, except where the highest quality automated translation is required. 

Currently, LLM MT makes the most sense in settings where high throughput, high volume, and a high degree of automation are not a requirement and where high quality can be achieved with reduced human review costs enabled by language AI.

Translators are already using LLMs for high-resource languages for all the translation-related tasks previously outlined. It is the author’s opinion that there is a transition period where it is quite plausible that both NMT and LLM MT might be used together or separately for different tasks in new LLM-enriched workflows. NMT will likely perform high-volume, time-critical production work as shown in the chart below.

In the scenario shown above, information triage is at work. High-volume content is initially processed by an adaptive NMT model, followed by an efficient MTQE process that sends a smaller subset to an LLM for cleanup and refinement. These corrections can be sent back to improve the MT model and increase the quality of the MTQE (not shown in the diagram above).

However, as LLMs get faster and it is easier to automate sequences of tasks, it may be possible to embed both an initial quality assessment and an automated post-editing step together for an LLM-based process to manage.

An emerging trend among LLM experts is the use of agents. Agentic AI and the use of agents in large language models (LLMs) represent a significant evolution in artificial intelligence, moving beyond simple text generation to create autonomous, goal-driven systems capable of complex reasoning and task execution. 

AI agents are systems that use LLMs as their core controller to autonomously pursue complex goals and workflows with minimal human supervision. 

They potentially combine several key components:

• An LLM core for language understanding and generation
• Memory modules for short-term and long-term information retention
• Planning capabilities for breaking down tasks and setting goals
• Some ability to iterate to a goal
• Tools for accessing external information and executing actions
• Interfaces for interacting with users or other systems

One approach involves using independent LLM agents to address each of the categories below as separate and discrete steps.

The other approach is to integrate these steps into a unified and robust workflow, allowing users to simply submit content and receive the best possible output through an AI-managed process. This integrated workflow would include source cleanup, MTQE, and automated post-editing. Translated is currently evaluating both approaches to identify the best path forward in different production scenarios.

Agentic AI systems are capable of several advanced capabilities that include:

• Autonomy: Ability to take goal-directed actions with minimal oversight
• Reasoning: Contextual decision-making and weighing tradeoffs
• Adaptive planning: Dynamically adjusting goals and plans as conditions change
• Natural language understanding: Comprehending and following complex instructions
• Workflow optimization: Efficiently moving between subtasks to complete processes

A thriving and vibrant open-source community will be a key requirement for ongoing progress. The open-source community has been continually improving the capabilities of smaller models and challenging the notion that scale is all you need. We see an increase in recent models that are smaller and more efficient but still capable and are thus often preferred for deployment.

All signs point to an exciting future where the capabilities of technology to enhance and improve human communication and understanding get better, and we are likely to see major advances in bringing an increasing portion of humanity into the digital sphere for productive, positive engagement and interaction.

The Evolution of AI Translation Technology

 Translated Srl is a pioneer in using MT in professional translation settings at a production scale. The company has a long history of innovation in the effective use of MT technology (an early form of AI) in production settings. It has deployed MT extensively across much of its professional translation workload for over 15 years and has acquired considerable expertise in doing this efficiently and reliably.

Machine Translation
IS
Artificial Intelligence

One of the main drivers behind language AI has been the ever-increasing content volumes needed in global enterprise settings to deliver exceptional global customer experience. The rationale behind the use of language AI in the translation context has always been to amplify the ability of stakeholders to produce higher volumes of multilingual content more efficiently and at increasingly higher quality levels. 

Consequently, we are witnessing a progressive human-machine partnership where an increasing portion of the production workload is being transferred to machines as technology advances.

Research analysts have pointed out that even as recently as 2022-23 LSPs and localization departments have struggled with using generic (static) MT systems in enterprises for the following reasons:

1. Inability to produce MT output at the required quality levels. Most often due to a lack of training data needed to see meaningful improvement.
2. Inability to properly estimate the effort and cost of deploying MT in production. 
3. The ever-changing needs and requirements of different projects with static MT that cannot adapt easily to new requirements create a mismatch of skills, data, and competencies.

The Adaptive MT Innovation

In contrast to much of the industry, Translated was the first mover in the production use of adaptive MT since the Statistical MT era. The adaptive MT approach is an agile and highly responsive way to deploy MT in enterprise settings as it is particularly well-suited to rapidly changing enterprise use case scenarios.

From the earliest days, ModernMT was designed to be a useful assistant to professional translators to reduce the tedium of the typical post-editing (MTPE) work process. This focus on building a productive and symbiotic human-machine relationship has resulted in a long-term trend of continued improvement and efficiency.

ModernMT is an adaptive MT technology solution designed from the ground up to enable and encourage immediate and continuous adaptation to changing business needs. It is designed to support and enhance the professional translator's work process and increase translation leverage and productivity beyond what translation memory alone can. It is a continuous learning system that improves with ongoing corrective feedback. This is the fundamental difference between an adaptive MT solution like ModernMT and static generic MT systems.

The ModernMT approach to MT model adaptation is to bring the encoding and decoding phases of model deployment much closer together, allowing dynamic and active human-in-the-loop corrective feedback, which is not so different from the in-context corrections and prompt modifications we are seeing being used with large language models today.

It is now common knowledge that machine learning-based AI systems are only as good as the data they use. One of the keys to long-term success with MT is to build a virtuous data collection system that refines MT performance and ensures continuous improvement. This high-value data collection effort has been underway at Translated for over 15 years and is a primary reason why ModernMT outperforms competitive alternatives.

This is also a reason why it makes sense to channel translation-related work through a single vendor so that an end-to-end monitoring system can be built and enhanced over time. This is much more challenging to implement and deploy in multi-vendor scenarios. 

The existence of such a system encourages more widespread adoption of automated translation and enables the enterprise to become efficiently multilingual at scale. The use of such a technological foundation allows the enterprise to break down the language as a barrier to global business success.

The MT Quality Estimation & Integrated Human-In-The-Loop Innovation

As MT content volumes rapidly increase in the enterprise, it becomes more important to make the quality management process more efficient, as human review methods do not scale easily. It is useful for any multilingual-at-scale initiative to rapidly identify the MT output that most need correction and focus critical corrective feedback primarily on these lower-quality outputs to enable the MT system to continually improve and ensure overall improved quality on a large content volume.

The basic idea is to enable the improvement process to be more efficient by immediately focusing 80% of the human corrective effort on the 20% lowest-scoring segments. Essentially, the 80:20 rule is a principle that helps individuals and companies prioritize their efforts to achieve maximum impact with the least amount of work. This leveraged approach allows overall MT quality, especially in very large-scale or real-time deployments, to improve rapidly.

Human review at a global content scale is unthinkable, costly, and probably a physical impossibility because of the ever-increasing volumes. As the use of MT expands across the enterprise to drive international business momentum and as more automated language technology is used, MTQE technology offers enterprises a way to identify and focus on the content that needs the least, and the most human review and attention, before it is released into the wild.

When a million sentences of customer-relevant content need to be published using MT, MTQE is a means to identify the ~10,000 sentences that most need human corrective attention to ensure that global customers receive acceptable quality across the board.

This informed identification of problems that need to be submitted for human attention is essential to allow for a more efficient allocation of resources and improved productivity. This process enables much more content to be published without risking brand reputation and ensuring that desired quality levels are achieved. In summary, MTQE is a useful risk management strategy as volumes climb.

Pairing content with lower MTQE scores into a workflow that connects a responsive, continuously learning adaptive MT system like ModernMT with expert human editors creates a powerful translation engine. This combination allows for handling large volumes of content while maintaining high translation quality.

When a responsive adaptive MT system is integrated with a robust MTQE system and a tightly connected human feedback loop, enterprises can significantly increase the volume of published multilingual content.

The conventional method, involving various vendors with different and distinct processes, is typically slow and prone to errors. However, this sluggish and inefficient method is frequently employed to enhance the quality of MT output, as shown below.

MTQE technology aims to pinpoint errors quickly and concentrate on minimizing the size of the data set requiring corrective feedback. The business goal centers on swiftly identifying and rectifying the most problematic segments.

Speed and guaranteed quality at scale are highly valued deliverables. Innovations that decrease the volume of data requiring review and reduce the risk of translation errors are crucial to the business mission.

The additional benefit of an adaptive rather than a generic MTQE process further extends the benefit of this technology by reducing the amount of content that needs careful review.

The traditional model of post-editing everything is now outdated.

The new approach entails translating everything and then only revising the worst and most erroneous parts to ensure an acceptable level of quality.

For example, if an initial review of 40% of the sentences with the lowest MTQE score using a generic MTQE model identifies 60% of the major problems in a corpus, using the adaptive QE model informed by customer data can result in the identification of 90% of the "major" translation problems in a corpus by focusing only on the 20% lowest scoring MTQE scores using the adaptive MTQE model. 

This innovation greatly enhances the overall efficiency. The chart below shows how a process that integrates adaptive MT, MTQE, and focused human-in-the-loop (HITL) work together to build a continuously improving translation production platform.

The capability to enhance the overall quality of translation in a large, published corpus by analyzing less data significantly boosts the efficiency and utility of automated translation. An improvement process based on Machine Translation Quality Estimation (MTQE) is a form of technological leverage that advantages extensive translation production.

The Evolving LLM Era and Potential Impact 

The emergence of Large Language Models (LLMs) has opened up thrilling new opportunities. However, there is also a significant number of vague and ill-defined claims of "using AI" by individuals with minimal experience in machine learning technologies and algorithms. The disparity between hype and reality is at an all-time high, with much of the excitement not living up to the practical requirements of real business use cases. Beyond concerns of data privacy, copyright, and the potential for misuse by malicious actors, issues of hallucinations and reliability persistently challenge the deployment of LLMs in production environments.

Enterprise users expect their IT infrastructure to consistently deliver reliable and predictable outcomes. However, this level of consistency is not currently easily achievable with LLM output. As the technology evolves, many believe that expert use of LLMs could significantly and positively impact current translation production processes.