Beyond the Chip: Why Language and Localization Will Define Robotics and AI in the Middle East
Kurdish, Arabic, Turkish, Persian and Hebrew will be part of the infrastructure that makes physical AI truly usable across the region.
The next major transformation in artificial intelligence will not happen only on a computer screen.
AI is beginning to move into the physical world.
Robots are becoming more autonomous. Machines are learning to interpret images, understand natural-language instructions, make decisions and perform actions in factories, hospitals, warehouses, farms, airports, energy facilities and eventually everyday public environments.
This development is sometimes described as physical AI or embodied AI.
For the Middle East, it represents an enormous opportunity.
But it also creates a challenge that is still underestimated.
A robot developed in California, Shenzhen, Tokyo or Munich cannot simply be shipped to the Middle East and be expected to function perfectly because its hardware works.
The machine must also understand the people and environment around it.
And in a region as linguistically complex as the Middle East, that means thinking far beyond English — and far beyond Arabic alone.
For Ziman Agency, five language ecosystems are particularly important when thinking about the future of AI and robotics in the region:
Kurdish, Arabic, Turkish, Persian and Hebrew.
Each has its own linguistic structure, terminology, speech characteristics, writing system, regional varieties and cultural context.
That matters because as robots become more intelligent, language itself increasingly becomes part of the robot's operating system.
Robotics is entering a new phase
Global robotics adoption is already substantial.
The International Federation of Robotics reported that 542,000 industrial robots were installed worldwide in 2024, while the total number operating globally reached approximately 4.66 million. Professional service robot sales approached 200,000 units, and medical robot sales increased by 91% to approximately 16,700 units.
But the more significant change may be what is happening inside the robots.
According to the International Federation of Robotics' Top 5 Global Robotics Trends 2026, generative and agentic AI are helping robotics move away from rigid rule-based automation toward systems capable of greater autonomy. IFR specifically identifies natural-language and vision-based commands as an emerging form of human-robot interaction.
Stanford's 2026 AI Index describes a similar transition through vision-language-action models, or VLAs. These systems attempt to connect what a robot sees with language instructions and physical movement, allowing a single AI system to move from camera input and human instructions toward motor control.
That fundamentally changes the role of localization.
In traditional machinery, localization might mean translating a user manual, warning label or control panel.
In physical AI, localization can influence what the machine hears, what it understands, how it responds and eventually what physical action it performs.
That is a very different responsibility.
After the hardware comes the human environment
A robot still needs processors, sensors, motors, actuators, cameras, power systems and safety engineering.
Language cannot replace any of those components.
So it would be technically inaccurate to say that language is literally the second component of every robot after its chip.
But from a market deployment perspective, the idea becomes much more important.
Once the hardware and core AI work, one of the next questions is:
Can this machine actually operate with the people who will use it?
A processor gives the robot computational power.
Sensors allow it to observe the environment.
AI helps it reason.
Motors allow it to move.
But language and localization connect that technology to the humans around it.
The more robots move from isolated factory cages into hospitals, hotels, public buildings, warehouses and workplaces, the more important that human layer becomes.
The Middle East is not one language market
One of the biggest mistakes a robotics or AI company can make when entering the Middle East is treating the entire region as a single localization market.
Even saying that a product “supports Arabic” is not enough.
The region includes several major language ecosystems, sometimes overlapping within the same city, workplace or family.
A hospital in Iraq may encounter Kurdish and Arabic.
A technology deployment in the Kurdistan Region may require Central Kurdish, Kurmanji and Arabic.
A system entering Türkiye must function naturally in Turkish rather than translated English.
A platform serving Persian-speaking users has to account for Persian language structures, terminology and speech.
Products operating in Israel may require Hebrew as well as Arabic and English.
The result is not simply multilingualism.
It is a multilingual physical environment.
And a physical machine has far less room for linguistic mistakes than a website.
Kurdish: one language name does not mean one AI problem
Kurdish provides perhaps one of the clearest examples of why robotics companies cannot depend entirely on generic multilingual AI.
Kurdish includes major varieties such as Central Kurdish (Sorani), Northern Kurdish (Kurmanji) and Southern Kurdish, along with significant regional and subdialect variation.
Research presented at LREC-COLING 2024 warned that Kurdish language technology has frequently treated Kurdish too much like a single monolithic macro-language, even though its varieties can differ significantly. Researchers found that state-of-the-art systems still produced suboptimal results on dialectal Central Kurdish data.
The situation is improving rapidly.
The 2025 Kuvost project produced an English-to-Central-Kurdish speech-translation dataset containing approximately 1,003 hours of translated speech and 786,000 utterances, with contributions from 230 volunteers.
In 2026, researchers also introduced a dedicated Southern Kurdish speech-recognition dataset, providing 30 hours of validated speech and dedicated ASR benchmarks.
These projects are important progress.
But they simultaneously demonstrate the problem.
If Kurdish were already fully solved by general-purpose AI, researchers would not need to create dedicated resources for its individual varieties.
For robotics, this distinction could become essential.
A robot working in Erbil, Sulaymaniyah, Duhok or another Kurdish-speaking environment may need to recognize different vocabulary, pronunciation and language conventions.
A system that merely has “Kurdish” in its language menu is not necessarily localized.
Arabic: dialects, code-switching and regional speech
Arabic presents a different scale of linguistic complexity.
Modern Standard Arabic remains important in formal communication, but people generally speak regional varieties in daily life.
Gulf Arabic is not identical to Iraqi Arabic.
Iraqi Arabic is not Levantine Arabic.
Levantine Arabic is not Egyptian Arabic.
And even within those groups, substantial variation exists.
A major ACL 2025 study on Arabic automatic speech recognition reported that many existing systems concentrate heavily on Modern Standard Arabic and a small number of higher-resource dialects.
Researchers therefore developed models using data from 17 Arabic-speaking countries and at least 11 spoken variants, while specifically addressing code-switching with other languages.
Code-switching is especially relevant to robotics.
A doctor might speak Arabic but use an English medical term.
An engineer may give most of an instruction in Arabic and name a component in English.
A worker might alternate between two languages depending on the person being addressed.
Humans handle this naturally.
Robots need to be trained to handle it.
Turkish: translation from English is not enough
Turkish creates a different technical challenge because of its grammatical and morphological structure.
Turkish can build substantial amounts of meaning through suffixes attached to a word. Its word order is also more flexible than English.
Recent research shows that even advanced language models do not automatically master these characteristics.
The 2025 TurBLiMP benchmark found that cutting-edge language models still struggle with Turkish grammatical phenomena that are comparatively straightforward for human speakers.
Another 2025 study evaluated 17 Turkish benchmark datasets and found significant quality problems, particularly where resources had been translated or adapted from English or multilingual datasets rather than being created around Turkish linguistic and cultural requirements.
That lesson is highly relevant to robotics:
Localization cannot simply mean translating the English training data.
Türkiye itself is also expanding its robotics ambitions.
Its official 2030 Industry and Technology Strategy identifies robotics and collaborative robots as important parts of future industrial automation and notes that annual industrial robot installations in Türkiye increased from approximately 2,800 in 2022 to 4,400 in 2023.
The country's industrial robot investment programme is also designed to strengthen domestic robot production, critical components and R&D capabilities.
As this ecosystem develops, Turkish linguistic capability will become increasingly relevant to the software and AI layers surrounding those machines.
Persian: speech AI needs language-specific engineering
Persian, or Farsi, presents another major Middle Eastern AI language environment.
It shares parts of its writing system with Arabic but is a different Indo-European language with its own grammar, pronunciation, vocabulary and computational challenges.
That distinction matters.
A machine cannot infer that because two languages use visually related scripts, the underlying speech and language problems are the same.
Recent speech-technology research continues to develop dedicated Persian resources.
The 2025 ManaTTS Persian project introduced approximately 86 hours of high-quality speech and described the work as addressing challenges faced by lower-resource speech technologies.
Other 2025 research examining Persian, Arabic and Urdu automatic speech recognition emphasized the importance of language-specific cleaning, tokenization, phonemization and expert linguistic validation for Perso-Arabic scripts.
A further study found that strategically selected Persian speech data could allow a smaller specialized ASR model to outperform the much larger Whisper Large v3 model on Persian.
That finding illustrates a principle that should matter enormously to robotics companies:
A larger global AI model is not automatically better than relevant local data.
For physical AI, local linguistic relevance can sometimes matter more than raw model size.
Hebrew: dedicated speech data remains necessary
Hebrew has a strong technology ecosystem, but that does not mean the language problem is finished.
Hebrew has rich morphology and speech-processing characteristics that continue to require dedicated resources.
The HebDB project assembled approximately 2,500 hours of natural Hebrew speech to improve spoken-language processing research.
At Interspeech 2025, researchers described Hebrew ASR as continuing to face significant challenges because of limited resources and rich morphology. Using a crowdsourced dataset of 314 transcribed hours, they developed an open-source Hebrew speech-recognition model that reduced errors by as much as 29% compared with evaluated Whisper alternatives.
Meanwhile, Israel's 2026 health-technology export portfolio already includes AI systems that coordinate autonomous robots, hospital logistics, staff and healthcare workflows.
As these systems become more conversational and autonomous, Hebrew language quality becomes part of their usability rather than simply part of their interface.
Healthcare shows why language can become a safety issue
Healthcare may be the strongest example of why robotics localization deserves much more attention.
Medical robotics is expanding quickly globally. IFR recorded a 91% increase in medical robot sales in 2024, with strong growth across rehabilitation, surgery and laboratory automation.
The Middle East is participating directly in this development.
In August 2026, Saudi Arabia's Seha Virtual Hospital announced 11 consecutive remote robotic surgeries, connecting specialist surgical teams with patients across different locations.
The programme operates under a Saudi national remote-surgery protocol, and the robotic systems underwent clinical, technical, training and simulation testing before implementation.
Today, a surgical robot is primarily a tool controlled by trained medical professionals.
But healthcare robotics is much broader than surgery.
Future systems may transport medications, guide patients, assist rehabilitation, monitor environments, support elderly people, coordinate laboratory samples or communicate basic instructions.
Imagine a hospital robot hearing:
“Take this medication to Room 214.”
“Call the cardiology team.”
“This patient speaks Kurdish.”
“Do not move this sample.”
“Bring the equipment from the second floor.”
A misunderstanding in an entertainment chatbot may be annoying.
A misunderstanding in healthcare may become a safety problem.
That is why medical robotics localization should eventually involve not only translation, but terminology validation, speech testing, multilingual QA, human evaluation and carefully defined confidence and escalation systems.
Robotaxis and care robots show what localization looks like in everyday life
The same challenge becomes even easier to see when robotics moves into everyday services.
Consider a future robotaxi operating in the Middle East.
The vehicle may already be capable of navigating roads, detecting traffic and driving safely. But the passenger experience introduces another layer of complexity.
A passenger may speak Kurdish, Arabic, Turkish, Persian or Hebrew. They may use a local accent or dialect, pronounce a street or neighbourhood name differently from the system's training data, mix English with their local language, or change languages during the same journey.
A passenger might say:
“Take me to the old market.”
“Stop after the next traffic light.”
“Not this entrance — the one behind the hospital.”
“I changed my mind. Take me home.”
The vehicle does not only need to hear the words.
It has to correctly understand the place, intention and local context behind them.
A robotaxi that can drive perfectly but repeatedly misunderstands its passengers is not fully localized for the market.
Elderly-care robots create an even more human challenge
The same principle applies to robots designed to support elderly people at home, in hospitals or in care facilities.
A care robot may eventually help with reminders, communication, mobility support, basic monitoring, contacting family members or healthcare staff, and everyday assistance.
But elderly users may communicate very differently from the language found in standard AI datasets.
They may use a strong regional dialect, older vocabulary, informal expressions or local names for medicines and everyday objects. Some may be much more comfortable speaking Kurdish, Arabic, Turkish, Persian or Hebrew than English.
Imagine an elderly person telling a robot:
“Please call my daughter.”
“Remind me about my medicine after dinner.”
“I don't feel well.”
“Bring me my walking stick.”
“I need help getting up.”
In these situations, understanding language correctly is not simply about creating a smoother user experience.
It can affect trust, accessibility and safety.
This is why the future of robotics localization will require more than translating buttons and menus.
Robots that interact directly with people will need to understand how people actually speak in the places where those robots are deployed.
The same principle applies across almost every sector
Healthcare is only one part of the opportunity.
In energy, robots can inspect hazardous infrastructure and reduce human exposure to dangerous areas.
In manufacturing, robots and cobots increasingly operate alongside employees.
In logistics, autonomous mobile robots can transport goods through warehouses and distribution centres.
In hospitality, robots can provide guidance, delivery and customer-facing services.
In agriculture, robots can perform repetitive or difficult physical tasks.
In airports and public services, conversational robots may eventually answer questions and physically guide users.
Dubai's Robotics and Automation Program explicitly identifies manufacturing, tourism and customer services, logistics and transportation, workplace improvement and healthcare among its priority areas, with a long-term objective involving 200,000 robots.
The UAE is also increasingly using the word localization at the technology-development level itself. In June 2026, the UAE Ministry of Industry and Advanced Technology and NYU Abu Dhabi announced cooperation intended to co-develop, localize and scale advanced manufacturing technologies including robotics and automation.
This is an important shift.
The region is moving from simply consuming advanced technology toward testing, adapting and developing it locally.
Language should be part of that same localization process.
Robotics localization is much bigger than translation
For robotics companies, localization should begin before a finished machine reaches the market.
It can include:
user interfaces and operating instructions; natural-language commands and responses; automatic speech recognition testing; text-to-speech and voice quality; Kurdish, Arabic, Turkish, Persian and Hebrew terminology; dialect and accent coverage; multilingual and code-switched conversations; AI training and evaluation data; safety warnings and emergency language; cultural interaction patterns; linguistic red teaming; human evaluation and LQA; and validation of the robot inside the environment where it will actually operate.
It also extends beyond language.
Robots may have to be adapted to regional temperatures, dust, network conditions, local maps, building layouts, regulations, medical procedures, privacy requirements and workplace practices.
So the correct concept is not simply robot translation.
It is robotics localization.
Why the five-language layer matters strategically
Kurdish, Arabic, Turkish, Persian and Hebrew do not represent one homogeneous market.
That is precisely why they matter.
Together they represent very different linguistic architectures and very different levels of available AI data.
Kurdish demonstrates the challenge of dialect diversity and under-resourced AI.
Arabic demonstrates enormous dialect variation and code-switching.
Turkish demonstrates how morphology and local cultural data can challenge models trained primarily around English.
Persian demonstrates why similar scripts do not mean similar language-processing requirements.
Hebrew demonstrates that even technologically advanced markets still require dedicated language datasets and evaluation.
The lesson for robotics companies is simple:
Do not wait until the robot is finished to think about language.
If natural language will eventually control the machine, then language data, terminology, linguistic testing and local human evaluation should become part of the development process itself.
The next robotics supply chain will include language
The robotics supply chain traditionally includes semiconductor companies, sensor manufacturers, motor manufacturers, software engineers, system integrators and industrial specialists.
Physical AI is likely to add another layer.
Language specialists.
As robots become conversational, localization providers may increasingly help technology companies collect and validate speech, build terminology resources, test commands, evaluate AI responses, identify cultural problems and perform multilingual quality assurance before deployment.
This is where the language industry and robotics industry begin to meet.
For organizations entering the Middle East, that intersection is particularly important because a single deployment may encounter several languages at once.
A system designed for the region may need Kurdish in one market, Arabic in another, Turkish in another, Persian in another and Hebrew in another — while still supporting English as a common technical language.
That is not ordinary translation.
It is multilingual AI infrastructure.
From global robot to local robot
The Middle East is unlikely to adopt robotics in exactly the same way as Europe, North America or East Asia.
Its industries are different.
Its climate is different.
Its population is multilingual.
Its healthcare systems and regulatory environments are different.
And its languages are different.
Advanced robotics companies therefore face two challenges when entering the region.
The first is making the robot technologically capable.
The second is making the robot locally capable.
The first requires chips, sensors, AI and engineering.
The second requires language, data, culture, terminology, testing and human understanding.
Both matter.
Because the future question will not simply be:
“Can this robot move?”
It will be:
“Can this robot understand the people around it well enough to be trusted?”
For the Middle East, answering that question will require much more than adding Arabic to a language menu.
It will require serious localization across Kurdish, Arabic, Turkish, Persian, Hebrew and the many varieties and multilingual environments surrounding them.
And as physical AI continues to develop, that language layer will become increasingly difficult to separate from the intelligence of the machine itself.
Ziman Agency perspective
At Ziman Agency, we believe the next generation of localization will extend beyond websites, software and documentation.
As AI moves into machines that can hear, speak, see and act, language quality will become part of how technology interacts with the physical world.
Our focus on Kurdish, Arabic, Turkish, Persian and Hebrew places us close to one of the most linguistically complex technology markets in the world.
For robotics and AI companies entering the Middle East, successful localization will not simply be about making the technology understandable.
It will be about making the technology understand us.
The chip makes the robot possible.
AI makes it intelligent.
Localization makes it ready for the world it is entering.