This guide is also available in Arabic. اقرأ النسخة العربية

AI can work very well in Arabic. It just needs to be planned for Arabic from the start.

This guide is for business owners and managers in the Arab world who are adding AI to their software, and for businesses elsewhere that serve Arabic-speaking customers. It covers:

  • why Arabic is harder for AI than English

  • what Arabic costs to process, and how to check it

  • how to choose and test an AI model for Arabic

  • making search and documents work properly in Arabic

  • building interfaces that handle right-to-left text

  • the data protection laws in Saudi Arabia, the UAE and Egypt

  • how to start

Why Arabic is harder for AI than English

Written Arabic varies considerably, and several things make it harder for AI than English.

People write in dialects. Formal documents use Modern Standard Arabic, but customers usually write the way they speak. Egyptian, Gulf, Levantine and Moroccan Arabic differ in vocabulary, grammar and spelling. A model that handles a news article well may misread a WhatsApp message from a customer in Cairo or Riyadh.

Not all Arabic is written in Arabic script. Many people, especially younger users, type Arabic in Latin letters and numbers, sometimes called Arabizi. “7abibi” uses the number 7 for the letter ح, and “3” stands for ع.

People mix languages. In business, it is normal to switch between Arabic and English in one sentence, such as “ممكن تبعتلي الـ invoice؟” (“Can you send me the invoice?” in Egyptian Arabic). Your system has to understand both, and display both correctly.

One word can carry a whole phrase. Arabic attaches prefixes and suffixes to words. وسيكتبونها is a single word meaning “and they will write it”. Words also grow from shared roots, so كتاب (book), كاتب (writer) and مكتبة (library) are closely related. This affects how AI models split, store and search text.

The same word can be spelt several ways. Short vowels (diacritics) are usually left out, and some letters are written inconsistently. أحمد and احمد are the same name; مدرسة often appears as مدرسه. A person reads these as identical. A computer, unless told otherwise, does not.

What Arabic costs to process

AI models process text in pieces called tokens. A token might be a whole word, part of a word or even a single character, and AI providers charge by the token. Many models split Arabic into more tokens than the same content in English.

A peer-reviewed study presented at NeurIPS 2023 measured this using the same text translated into many languages. Compared with English, Standard Arabic needed about:

Tokeniser tested in 2023

Tokens compared with English

GPT-4 tokeniser (OpenAI)

about 3 times as many

LLaMA and Llama 2 tokeniser (Meta)

about 3.4 times as many

Original Qwen tokeniser (Alibaba)

about 1.6 times as many

The dialects tested, including Egyptian, Najdi, Levantine and Moroccan Arabic, showed similar results.

On some models, processing the same content in Arabic can cost considerably more than in English. It may also take longer and use more of the space available for a conversation or document. The actual difference depends on the model, the balance between what you send and what the model writes back, and any discounts such as caching.

These figures come from models available in 2023, and newer models may split Arabic more efficiently. The safest approach is to measure it yourself. Take a sample of real messages and documents, count the tokens with your chosen provider’s tools, and compare the result with English. That number should go into your budget before you build, not after the first invoice.

Choosing an AI model for Arabic

There are two main options, and they are not mutually exclusive.

Global models from companies such as Anthropic, OpenAI and Google handle Arabic well for many tasks, and they are usually the quickest way to start. Their ability to handle different dialects varies, and differences in how they process Arabic can affect the cost.

Arabic-focused models have been built in the region with much more Arabic in their training data. Examples include Jais, developed in the UAE by G42’s Inception with Mohamed bin Zayed University of Artificial Intelligence and Cerebras Systems; Falcon-H1-Arabic from the Technology Innovation Institute in Abu Dhabi; and ALLaM from the Saudi Data and AI Authority (SDAIA). Some of these can run on servers you control, which can help where data needs to stay in a particular country.

Published rankings are a starting point, but they do not tell you how a model will handle your customers. The only reliable test is your own:

  1. Collect real examples. Gather a few hundred genuine messages, questions and documents, with personal details removed, in the dialects your customers actually use.

  2. Decide what a good answer looks like. For example: correct, polite, in the right dialect or in Modern Standard Arabic, and consistent with your policies.

  3. Have native speakers judge the results. Ideally they should come from the same countries as your customers. A Gulf speaker may miss problems in a Moroccan reply.

  4. Compare cost and speed as well as quality. A model that is slightly better but three times the price may not be the right choice.

A common decision is which form of Arabic the AI should reply in. Modern Standard Arabic is understood everywhere and suits formal communication. Replying in the customer’s dialect can feel more natural, but it is harder to get right. Decide this deliberately, and test it.

Making search and documents work in Arabic

Many useful AI features depend on finding the right information first: a customer’s order, a policy document, a previous conversation. In Arabic, this is where many systems quietly fail.

Normalise text for searching. Arabic words and names are not always written consistently, so a search system may need to treat certain spellings as equivalent. Common steps include removing diacritics, treating أ, إ and آ as ا, and removing the decorative stretching character (ـ) used in headings. Some systems also allow for common variations between ة and ه at the end of a word, so that a search for مدرسة also finds مدرسه.

Keep the original text unchanged. Normalisation should help the system find information, not silently change what customers have written or what is stored in business records. Keep the original Arabic as it was entered, and use a separate normalised copy for search and matching. This matters most for contracts, legal documents and personal names.

Be careful not to over-normalise. Some rules can merge words that are genuinely different. Treating ى and ي as the same letter, for example, makes على (“on”) and the name علي (Ali) identical. The same caution applies to ة and ه, which are distinct letters even though people sometimes swap them. The right rules depend on your data, so they need testing.

Check that AI search understands Arabic. Many AI features find relevant documents by meaning rather than exact words. The tools that do this vary a lot in how well they handle Arabic, so test them with real questions and check whether the right documents come back.

Test scanned documents early. If your AI needs to read scanned PDFs, such as contracts, invoices or government forms, check how well text extraction works on your actual documents. Joined-up Arabic letters, right-to-left text, and tables mixing Arabic and English are all common sources of errors. Check that the system reads tables and form fields in the correct order, not just that it recognises the words. A value attached to the wrong field produces an answer that looks plausible but is wrong, and a mistake at this stage passes straight through to every answer the AI gives.

Building interfaces for right-to-left text

Even a perfect AI answer is let down by a screen that displays it badly. Arabic interfaces need to be designed for, not translated into.

Adapt the layout, not just the text. In Arabic, people read from right to left, so menus, navigation, progress bars and arrows usually need to be reversed too. Some icons, such as play buttons and brand logos, should not be.

Handle mixed text carefully. AI replies often mix Arabic with English names, order numbers, email addresses and prices. Without proper handling, punctuation jumps to the wrong end of a line and numbers appear in the wrong order. This needs testing with real AI output, not sample text. Setting the whole page to right-to-left will not solve every problem on its own.

Check AI formatting. Many AI models format replies with lists, headings and tables. Make sure these display correctly from right to left, including while a reply is still appearing on screen.

Respect local conventions. Some users expect Arabic-Indic digits (١٢٣) and others expect Western digits (123); preferences vary by country and context. Some businesses, particularly in Saudi Arabia, may also need Hijri dates alongside Gregorian ones.

Choose fonts and spacing for Arabic. Arabic text needs fonts designed for it, and often more line height than English. Text length also changes between languages, so buttons and labels need room to grow or shrink.

Data protection in Saudi Arabia, the UAE and Egypt

Most AI features send some information to an AI provider, and many of those providers process data outside the region. Several countries in the Arab world now have data protection laws that apply to this, including to companies based abroad. Saudi Arabia, the UAE and Egypt are three examples. Other countries have their own rules, and businesses serving customers in several countries may need to meet more than one set of requirements.

Saudi Arabia. The Personal Data Protection Law is overseen by the Saudi Data and AI Authority (SDAIA). It applies to processing the personal data of individuals in the Kingdom, including by organisations outside it. People must be told whether their data will be transferred or processed outside the Kingdom, and a separate regulation sets the conditions for transfers. Enforcement is active: in January 2026 the Saudi Press Agency reported 48 decisions confirming violations during 2025, and SDAIA’s Data Protection page reports that about 422 decisions had been issued by its committees for reviewing violations as of October 2026. The rules are still developing too: in October 2026, SDAIA opened a consultation on proposed amendments to the implementing regulation, running until 5 November 2026.

United Arab Emirates. Federal Decree-Law No. 45 of 2021 came into force on 2 January 2022. It sets rules for collecting, using and transferring personal data, generally requiring consent unless a legal exception applies. However, its implementing regulations had still not been issued by October 2026, which leaves some practical requirements and enforcement arrangements unresolved. Businesses in the Dubai International Financial Centre (DIFC) and Abu Dhabi Global Market (ADGM) operate under their own separate data protection regimes.

Egypt. The Personal Data Protection Law (Law No. 151 of 2020) gained its long-awaited executive regulations in November 2025. These set detailed requirements covering licensing, processing and transfers outside the country. The one-year transition period has now ended, with the compliance deadline falling on 31 October or 1 November 2026, according to different legal interpretations. Businesses operating in Egypt, and some organisations abroad that process the personal data of people in Egypt, should now establish which requirements apply to them and whether they need a licence or permit.

For an AI project, this means checking where each AI provider processes data, whether a transfer abroad is allowed and on what terms, and what customers must be told. A locally hosted model can make transfer rules simpler, but it does not make a business compliant on its own: you still need a lawful basis, clear privacy information, good security and sensible retention. These laws are detailed and still developing, so take local legal advice before launching. Our guide to what happens to your customer data when you add AI to a business app covers the general questions to ask any AI provider.

How to start

A good Arabic AI project starts small and is tested properly before it reaches customers.

  1. Pick one task where Arabic matters. Answering common customer questions, summarising Arabic documents or sorting incoming messages are good first candidates.

  2. Decide which Arabic you need. Agree which dialects you must understand, and whether replies should be in Modern Standard Arabic or the customer’s dialect.

  3. Measure the cost. Count the tokens in a sample of real Arabic text with each model you are considering, and build that into your budget.

  4. Test models with native speakers. Use real messages, and include at least one Arabic-focused model in the comparison.

  5. Design for Arabic from the start. Build in text normalisation, right-to-left layouts and mixed-language handling from the first version.

  6. Check the law in each country you serve. Confirm where data will be processed and what customers must be told.

  7. Pilot before launch. Release to a small group, have Arabic speakers review real answers, and fix problems before rolling out more widely.

At Niletech, we build AI features into business software using .NET and Laravel. Our team speaks Arabic and has worked on projects in Egypt, so we can assess Arabic AI output directly rather than relying solely on translation. Where a project involves other regional dialects, we also recommend testing with native speakers from those markets. Our AI integration work covers model choice, search, interfaces and data protection together. If you want to test an idea first, our three-week AI discovery sprint can test an Arabic AI use case with your own data, assess the available model options and give you a written recommendation before you commit to a full build.

Summary

Arabic AI is not simply English AI with translated text. The language affects how a system understands customers, finds information, displays answers and what it costs to run. These challenges can be addressed, but they are much easier to deal with before development starts than after launch.

If you’re considering AI for Arabic-speaking customers, we can help you assess the options before you commit to a full build. Book a 30-minute call or send us a short brief. You’ll speak directly to the person who would do the work.

Sources

Checked on 1 November 2026. Laws and AI models change often, so check the current position before relying on them.