AI Localization Think Tank Looking Forward to 2026 | Part 2

As we enter 2026, the AI Localization Think Tank went from reflection to anticipation. The year ahead promises to solidify changes already in motion and bring new ones. But what changes can those be?

To explore what lies ahead, we asked each member three questions:

• What major change do you expect to see in the field in 2026, and why?

• What emerging opportunity or risk should the industry prepare for in 2026?

• Which innovation, behavior shift, or market force do you believe will define 2026?

The volume and depth of perspectives once again exceeded a single publication. We’re therefore sharing the insights in two parts.

This is the second part. The first part is available here.

Bridget Hylak

Head of ATA Language Technology Division, Localization Consultant

Looking ahead to 2026… The Year of Reinvention

It is now undeniable that a seismic shift across industries is underway.

In nearly every boardroom and client-facing meeting I attend, one word dominates: reinvention. It hums beneath strategic plans, budget forecasts, and workforce discussions, often carrying equal parts urgency and uncertainty.

But reinvention is not a single act. It generally unfolds in one of two ways.

The first is refinement: honing, extending, or reshaping existing skills to remain relevant as tools, workflows, and expectations evolve.

The second is far more radical: a hard turn into something entirely new.

This distinction matters more than most discussions acknowledge. Changing a job is not the same as changing a profession. A job can often be learned relatively quickly. A profession, by contrast, is built over years, sometimes decades.

Linguists, translators, interpreters, and localization professionals do not simply “do language.” They develop layered expertise through sustained practice: cultural competence, subject-matter depth, ethical judgment, and hard-earned intuition. Walking away from that is not a casual pivot. It represents a significant recalibration of identity, value, and time invested.

And yet, many professionals today are being asked, implicitly or explicitly, to guess. Guess which skills will still matter. Guess which tools will survive. Guess which path is safest in a landscape shifting faster than formal guidance can keep up.

I find myself asking similar questions. Will CAT tools survive? If so, in what form, and for how long? How must they continue to evolve to remain relevant in a world increasingly shaped by large language models and automation?

And perhaps more critically, who will train the next generation of professionals in skills and processes that have yet to be fully defined?

There is no way to outrun this avalanche. But we can build small campfires along the way. These are moments to slow down, observe, train with intention, assemble competent cross-disciplinary teams, and shed light on unintended or undesirable consequences.

These pauses are not resistance. They are strategic. They allow us to refine how we harness the snowfall rather than being buried beneath it.

As we move into 2026, processes must be better defined and outcomes more rigorously measured. AI implementations will no longer be judged on ambition alone, but on demonstrable results. Inflated and speculative AI budgets will increasingly give way to sharper expectations around return on investment. Metrics are being built now so that emerging AI directors and departments can operate with clearer benchmarks and real accountability.

This may be one of the most important shifts ahead. Much of the present moment has been characterized by experimentation without sufficient guardrails. High spending, limited clarity, and little responsibility for downstream effects have been common. By 2026, that era begins to close. As the pace of new and shiny AI tools slows, organizations will gain traction in defining roles, responsibilities, and success criteria with far greater precision.

AI literacy, meanwhile, will no longer be a résumé bonus. It will be an expectation. Training programs, professional courses, and even university curricula are already pivoting or disappearing at unprecedented speed. Educational institutions understand what many professionals are only beginning to confront: relevance is survival. A university, after all, functions much like a corporate entity. It becomes increasingly difficult to sell a six-figure degree that leads nowhere beyond a parent’s basement.

For example, MIT recently launched a short but comprehensive course titled Artificial Intelligence in Health Care, recognizing that health care, like nearly every industry, is actively seeking to leverage AI. Yet this does not change a truth I have illustrated for more than a decade using my own “surgical robot” analogy. A seasoned professional and their technology must work in harmony.

Just imagine yourself on an operating table about to undergo a right radical nephrectomy at the hand of the professional in this picture. What would you demand? An intern or inexperienced practitioner, no matter how advanced the surgical robot or his/her understanding of technology, should not be performing the surgery. At the same time, even a top-tier doctor cannot deliver optimal outcomes managing clunky technology or lacking experience in its use.

In this scenario, one would demand three things at minimum: quality and experience of a professional in a given discipline, in this case, surgical nephrology; high-quality, tested technology; and professional training and experience using the technology, something I have often referred to as “training on the bridge.” 

All three require time, and time seems to be the one thing we are less and less willing to add to the Recipe for Success.

This principle applies across industries.

The best professionals will always have a seat at the table, provided their definition of “best” evolves. Ongoing formation in the core skill that made them professionals in the first place must be paired with solid, field-specific AI literacy and real-world experience managing these tools responsibly. That experience cannot be rushed. It will require all of 2026 and well beyond.

True reinvention is about discernment, not abandonment. Knowing when to refine existing skills, when to expand them, and when a deeper transformation is truly warranted. As we enter 2026, a small but growing group of professionals across industries is finally prepared to take the wheel. Not because they guessed correctly, but because they invested the time to learn, test, and adapt with intention.

The multilingual services industry is a multi-billion dollar hidden gem - more than twice the size of the music industry. There is enough pie for everyone. How will you grab your piece?

As we enter 2026, a small but growing group of professionals across industries is finally prepared to take the wheel because they invested the time to learn, test, and adapt with intention.

Libor Safar

VP of Growth

What major change do you expect to see in the field in 2026, and why?

As someone much smarter than me recently quipped, we're asking 2026 questions while living with 2010 infrastructure. I expect—and hope—that organizations will increasingly stop trying to bolt AI onto legacy systems and start making hard choices: rebuild from scratch or accept the limitations. The brittleness of "duct tape solutions" will become too expensive to ignore. Those willing to invest in modern architectures will pull ahead dramatically. Others will struggle with integration costs that often exceed their actual localization spend.

What emerging opportunity or risk should the industry prepare for in 2026?

The biggest risk: losing our seat at the table. There's a dangerous "invisibility syndrome" where executives increasingly believe AI has eliminated the need for localization expertise. Other departments are often adopting AI tools with "translation included," bypassing localization teams entirely, along with all their expertise and infrastructure.

Which innovation, behavior shift, or market force do you believe will define 2026?

Turning the tables (pun fully intended) on these rogue internal AI localization alternatives. Localization professionals can position themselves as the organization's multilingual AI implementation guides. Those who proactively lead AI adoption, rather than resist it, will raise their internal profile. Our expertise should become more critical, not less.

The biggest risk: losing our seat at the table. There's a dangerous "invisibility syndrome" where executives increasingly believe AI has eliminated the need for localization expertise. 

Marina Ilari

CEO

By 2026, I expect to see an even greater shift in how we define and measure localization success. Quality will move beyond purely linguistic accuracy toward global audience impact, how localized content performs with real users, protects brand reputation, and supports business goals. At the same time, AI experimentation will mature. The industry will have clearer conclusions about what works, what doesn’t, and where AI actually adds value in localization workflows.

A key opportunity for the industry (which can also be a risk) is the shift from viewing localization as “translation transactions” to treating it as global content management solutions. Localization is increasingly becoming a strategic function that helps organizations manage legal, financial, and reputational risks across international markets.

I believe that in 2026, rather than adopting technology wholesale, teams will focus on what can realistically be leveraged without compromising quality. This shift will elevate the role of language service providers from execution partners to consultative advisors, helping clients design sustainable, high-quality localization strategies. The winners will be those who balance innovation with expertise, integrating technology with purpose while preserving human judgment, cultural insight, and creative integrity.

Quality will move beyond purely linguistic accuracy toward global audience impact, how localized content performs with real users, protects brand reputation, and supports business goals.

Marina Pantcheva

Director Linguistic AI Services

I do not expect 2026 to bring a transformational shift for the translation and localization industry. Instead, the year will probably be characterized by incremental progress as AI models improve and extend their capability frontier. A change I hope to see is the closing of the long-standing disconnect between processes, tools, and remuneration. The traditional “translator from scratch” role is giving way to roles such as editor, quality auditor, and AI trainer. This role evolution is a natural response to technological advancement; however, industry processes and compensation models have not evolved accordingly which creates tensions.

Related to this is the risk of losing talent. Current workflows often fail to use human linguists in a way that brings out their real value. This misalignment accelerates skills erosion and talent exodus. To mitigate this, the industry must focus on upskilling linguists for new roles and redesigning processes so human expertise is applied where it adds the most value, rather than being used for broad and inefficient reviews of AI output. Education will be critical to this end, and leadership must come from academia and industry stakeholders.

In 2026, I hope to see a growing resistance to generic, low-quality AI-generated content. I believe stakeholders will increasingly reject AI slop and expect high-quality results. This will push Language Technology Providers and Global Content Providers to move beyond superficial implementation of AI to robust and nuanced solutions capable of handling the intricacies or various types of content.

In 2026, I hope to see a growing resistance to generic, low-quality AI-generated content. I believe stakeholders will increasingly reject AI slop and expect high-quality results. 

Marta Nieto Cayuela

Senior Localization Quality Manager

In 2026, AI in Localization will reach the late majority in the adoption curve. The hype has not magically disappeared. LLMs have become the new normal due to business pressures, budgets, and competition. Using AI does not feel like a bold choice anymore, how you use it (or choose not to use it) does.

After a few years of quick deployment and exponential growth, something is shifting. The industry is moving toward more use-case-specific applications. We are reclaiming the “intelligence” to deploy LLMs when they make sense. We have clearer limits, and understand the risks, the damage, and the cost. Innovators and early adopters have already shared the trade-offs and made them measurable. Improvements in general applications of AI in Localization seem incremental now. Still useful, but less disruptive than before.

That is why, in 2026, I hope that we will still be disruptive in a different way. That we remain critical, conscious, curious.

Does opting out of AI add value as a company statement, or does it make things easier for competitors? Does AI encode dominant languages at the expense of minority languages, or does it make knowledge more accessible? Is AI helping to bridge gaps, or widening them in the name of efficiency and progress? As with so many things in translation, the answer is often the same: it depends on the context.

So, in 2026, we will see a behavioural shift with AI being the new normal. Not because AI has been perfected; it will be because we have stopped pretending it is, and we question it, and leverage it as any another tool in the process. We will focus again on the outcome and it's quality, we will be user-centric and we will set limits, context, and responsibility.

 We are reclaiming the “intelligence” to deploy LLMs when they make sense. .

Miguel Sepulveda

Globalization Director

What major change do you expect to see in the field in 2026, and why?

In 2026 I expect to see companies/teams stop imposing the use of AI just because it is the “new” thing. Instead, I hope to see more conversation around what problem are we trying to solve by implementing AI in Localization? In 2026 I expect to see a more mature approach. I expect teams will start working with formal frameworks, clear workflows, clear guardrails, and clear ownership.

What emerging opportunity or risk should the industry prepare for in 2026?

A key opportunity is the rise of market-level personalization. With the help of AI, we can reach levels of hyper-culturalization and personalization that traditional localization workflows could never support because of budget limits. Now we can adjust tone, references, and style in a much more detailed way for each audience. But the risk is very similar. If companies try to use this level of personalization without the right cultural guidance, we will see more mistakes, more bias, and an inconsistent brand voice. So in 2026 localization teams need to prepare for both sides. We need to shape personalization in a safe and responsible way, and we also need to make sure the role of the localization professional is respected and not treated as something that can be replaced or commoditized.

Which innovation, behavior shift, or market force will define 2026?

I believe 2026 will be defined by the shift from experimentation to accountability. Until know many companies experimented with AI. In 2026 they will start asking for proof: Does this workflow actually save time? Does this AI setup improve quality? Does it support global growth or create friction? This will push localization teams to show results with data, and it will also push leaders to make more careful decisions about when AI helps and when it does not.

In 2026 I expect to see a more mature approach. I expect teams will start working with formal frameworks, clear workflows, clear guardrails, and clear ownership.

Monica Albini

Researcher in AI

What major change do you expect to see in the field in 2026, and why?

In 2026, I expect the most significant change to be the formalization of hybrid localization roles. It will be the year when the industry will move beyond experimentation and clearly distinguish between “simple” translation as a standardized service and the real value coming from professionals who combine language skills with industry knowledge, technology skills and strategic thinking.

What emerging opportunity or risk should the industry prepare for in 2026?

The localization industry should prepare for a growing polarization of the market, with low-cost, AI-driven solutions dominating high-volume and low-risk content. At the same time, I expect the demand to rise for culturally sensitive and business-critical localization. The risk I see lies in unclear positioning.

Which innovation, behavior shift, or market force do you believe will define 2026?

I expect, as a behavioral shift in 2026, that customers will become even more selective, more informed and consequently more demanding..

The localization industry should prepare for a growing polarization of the market, with low-cost, AI-driven solutions dominating high-volume and low-risk content. At the same time, I expect the demand to rise for culturally sensitive and business-critical localization.

Stavroula Sokoli

Senior Researcher

By 2026, we will be firmly in a situation where the same outputs and services are delivered by fewer people, with AI handling a bulk of the execution. This is often framed as progress, and economically, it often is, but it also leads to job loss. It is reasonable to assume that the demand will expand or the needs will change, leading to enough work to keep people in the system.

Even under that optimistic assumption, there is a quieter risk. Across the industry, whether it is post-editing, prompt-driven content generation, agentic workflows, or decision support at the management layer, automation is also sold as a cognitive accelerator: remove the boring and repetitive work so humans can focus on the "interesting", the "creative" parts. What rarely enters the conversation is that the "boring" work is precisely how skills are encoded.

Enter neuroscience research. Engrams (the brain's physical memory traces formed through repeated activation) accumulate over time, and schemata (the higher-level cognitive structures built by organizing those traces) form on top of them. Without doing the work from scratch, the brain does not form the internal structures that make higher-order judgment, interpretation or creativity possible. It is the effortful retrieval and application that forces the brain to strengthen its pathways. (For more on this, see The Memory Paradox: Why Our Brains Need Knowledge in an Age of AI.)

More than any single technical breakthrough, 2026 will be the year when we need to reflect: what have we automated and what should we not automate? Our abilities depend on internal brain structures that only form through practice. And if the kind of work we're doing doesn't let us build them, maybe it's time to find a cognition gym?

Disclaimer: The above is deliberately simplified. The full implications deserve book-length treatment.

More than any single technical breakthrough, 2026 will be the year when we need to reflect: what have we automated and what should we not automate?

Veronica Hylak

Product Innovation Strategist and AI Vlogger

Going into 2026, I will ensure to pair that fascination with caution. To be excited about what’s coming while also acknowledging that we’re building technologies powerful enough to define entire eras, and even all of humanity.