Why AI Struggles With Low-Resource Languages (Urdu, Punjabi, Pashto, Sindhi)

Artificial intelligence can write business proposals, summarize lengthy documents, generate software code, and respond to complicated questions in seconds. Yet ask the same technology to understand a casual Punjabi conversation, transcribe a Pashto voice message, or interpret a sentence written in everyday Roman Urdu, and the experience can become surprisingly inconsistent.

The problem is not that these languages are too complicated for machines. The problem is that AI has not received equal opportunities to learn them.

Languages such as Urdu, Punjabi, Pashto, and Sindhi have millions of speakers, rich literary traditions, established grammatical systems, and active communities. However, their representation in digital training resources remains considerably smaller than that of English and several other widely digitized languages.

This creates an interesting contradiction: a language can be widely spoken in everyday life while remaining poorly represented inside the technology people increasingly depend on.

For AI developers, this is a technical challenge. For businesses, it is a market limitation. For ordinary users, it can mean being excluded from digital services simply because they communicate in the language they are most comfortable using.

The Real Problem: AI Learns From What the Internet Makes Available

Large language models do not acquire language skills in the same way humans do. They learn statistical patterns from enormous collections of text, code, conversations, and other training material.

The quantity and quality of that material influence what a model can recognize, predict, and generate.

English has an enormous digital footprint. There are extensive collections of books, research papers, websites, educational resources, technical documentation, news articles, and online discussions.

The situation is different for many regional languages.

Urdu has a substantial literary and online presence, but high-quality, properly structured, publicly accessible datasets are more limited. Punjabi content is spread across different scripts and regional varieties. Pashto has considerable dialect diversity, while Sindhi presents its own script and text-processing requirements.

Consequently, AI models may recognize common words in these languages but struggle with less familiar expressions, sentence structures, regional vocabulary, and cultural references.

This is what makes a language low-resource in AI research. It does not describe the number of people who speak it. It describes the availability of suitable digital resources for developing and evaluating language technologies.

Four Languages, Four Different Sets of Challenges

Treating Urdu, Punjabi, Pashto, and Sindhi as one technical problem would be a mistake. They share some challenges, but each language has characteristics that require specific solutions.

1. Urdu: The Complexity of Script and Everyday Communication

Urdu uses a Perso-Arabic writing system, commonly presented in the Nastaliq style. Its visual structure introduces challenges for applications involving optical character recognition (OCR), document processing, and text extraction.

Letters can change shape depending on their position within a word. Diacritical marks are frequently omitted in everyday writing, and the same expression may appear in different spellings.

Then there is Roman Urdu.

Millions of people communicate using Latin characters rather than the traditional Urdu script, particularly in text messages, social media comments, and informal online discussions.

A simple sentence such as:

“Ap kal office aa rahay ho?”

could also appear as:

“Aap kal office arhe hain?”

The meaning remains broadly similar, but the spelling changes. An AI model trained primarily on standardized Urdu may not handle all these variations equally well.

Code-switching creates another complication. People frequently combine Urdu and English within a single sentence, especially when discussing technology, business, education, and professional subjects.

For a genuinely useful Urdu AI assistant, recognizing formal Urdu is not enough. It must also handle informal communication, Roman Urdu, English terminology, and natural sentence variations.

2. Punjabi: One Language, Different Writing Systems

Punjabi presents an interesting challenge because its writing systems vary across geographical and cultural communities.

In Pakistan, Punjabi is commonly written using Shahmukhi, a Perso-Arabic-based script. In Indian Punjab, Gurmukhi is widely used.

These writing systems are different even though they represent the same broader language.

An AI model trained extensively on Gurmukhi Punjabi may not automatically perform well with Shahmukhi Punjabi. The model needs appropriate examples, script-aware processing, and evaluation data.

Dialect differences add another layer.

The Punjabi spoken in Lahore may sound different from varieties used in Faisalabad, Multan, or other parts of Punjab. Vocabulary, pronunciation, expressions, and sentence patterns can change between communities.

A translation system that produces technically recognizable Punjabi but ignores how people actually communicate may struggle to gain users’ trust.

The solution requires more than collecting Punjabi words. Developers need representative examples from different regions, scripts, speakers, and communication settings.

3. Pashto: Dialect Diversity and Limited Digital Representation

Pashto has a strong oral tradition and a substantial population of native speakers. However, building reliable AI applications for the language requires considerably more than translating existing English resources.

Regional varieties can differ in pronunciation, vocabulary, and everyday expressions. Northern and Southern Pashto, for example, cannot always be treated as identical for speech recognition and conversational applications.

Speech technology faces additional difficulties.

Background noise, speaking speed, regional accents, and differences in pronunciation can affect transcription accuracy. A voice assistant that performs well with carefully recorded studio speech may produce considerably weaker results when used during an ordinary conversation.

Another issue is the availability of properly transcribed recordings.

AI speech systems need audio paired with accurate written transcripts. Without enough diverse examples, a model may misinterpret words, omit phrases, or replace unfamiliar expressions with more common alternatives.

For Pashto-speaking users, these limitations affect practical applications such as educational platforms, voice assistants, customer support, and automated translation.

4. Sindhi: A Script That Requires Specialized Processing

Sindhi has another distinctive technical challenge: its writing system contains an extended set of characters.

The Perso-Arabic-based Sindhi alphabet includes letters that are not handled adequately by systems designed primarily for standard Arabic or Urdu.

This matters when developers build OCR tools, document scanners, search engines, and text-processing applications.

A system may recognize a large portion of an ordinary Urdu document but fail when it encounters Sindhi-specific characters.

Vocabulary and regional variation also matter. Sindhi users communicate through different local varieties, and literary language does not always reflect everyday conversational usage.

For better performance, developers need dedicated Sindhi datasets, character-aware processing, native-speaker validation, and evaluation methods that reflect actual language usage.

Why More Data Alone Will Not Solve Everything

A common assumption is that low-resource language AI simply needs more training data.

More data certainly helps, but the quality of that data matters just as much.

Imagine collecting 100,000 sentences from websites, social media posts, and public documents. If those sentences contain spelling errors, duplicated content, incorrect translations, or poorly identified languages, the resulting dataset may introduce additional problems.

AI developers need to consider several factors.

Data accuracy: Sentences should be checked by people who genuinely understand the language.

Regional representation: Data collection should include speakers from different geographical areas and communities.

Natural communication: Datasets should include ordinary conversations rather than relying entirely on formal writing.

Script consistency: Multiple writing systems and transliteration practices need appropriate representation.

Context: Words should be collected in meaningful sentences so models can learn how they function in different situations.

Permission and privacy: Language data should be collected and used with appropriate consent, licensing, and privacy safeguards.

An especially important point is that translated English sentences cannot replace original language material. A sentence translated word for word may be grammatically understandable but still sound unnatural to a native speaker.

AI needs examples of how people actually communicate.

Speech Recognition Is a Particularly Difficult Area

Text generation receives considerable attention in discussions about language AI, but speech technology deserves equal consideration.

Automatic Speech Recognition (ASR) converts spoken language into written text. Text-to-Speech (TTS) performs the opposite task by generating spoken audio from written content.

Both technologies require specialized datasets.

Consider a woman running a small business in a rural area who prefers sending voice messages rather than typing long Urdu or Punjabi messages. A reliable speech recognition system could help her prepare customer responses, maintain records, communicate with suppliers, and manage administrative work.

If the system repeatedly misunderstands her accent or replaces regional words with incorrect alternatives, the technology becomes another obstacle rather than a productivity tool.

This also has economic consequences.

Women entrepreneurs, freelancers, teachers, and home-based business owners should not need advanced English skills to benefit from digital tools. Reliable regional-language voice technology could make online business participation more accessible.

Recent research presented at the 2026 Language Resources and Evaluation Conference highlights the importance of community-driven speech data for Pakistan’s underrepresented languages. Its Common Voice for Pakistan project reports work involving 39 indigenous languages, with locally authored material, everyday sentences, poetry, and folk songs included in the corpus.

The approach demonstrates why involving actual speakers is essential when building technology for linguistically diverse communities.

The Hidden Business Cost of Language Limitations

Low-resource language support is not simply an academic concern. It has direct commercial implications.

Businesses increasingly use AI for customer service, marketing, online transactions, employee training, and automated communication.

However, a company serving customers across Pakistan cannot assume that every customer prefers English or standardized Urdu.

A Punjabi-speaking customer might explain a product issue using regional expressions. A Pashto-speaking customer might prefer voice communication. A Sindhi-speaking customer may need product information in their native language.

If the AI system cannot handle these interactions accurately, businesses risk losing customers and creating frustrating service experiences.

This creates an opportunity for developers and entrepreneurs.

Regional-language AI applications could support:

• Local-language customer support systems.
• Urdu and Punjabi educational assistants.
• Speech-to-text applications for regional businesses.
• Sindhi document digitization.
• Pashto learning and translation platforms.
• Voice-based business management tools.
• Local-language accessibility services.

For independent developers, these areas may offer opportunities that are less crowded than building another general-purpose English chatbot.

The strongest commercial ideas will solve specific problems for clearly defined communities rather than attempting to build a universal language application immediately.

How Language AI Can Create More Economic Opportunities for Women

Language accessibility also connects with financial independence.

Many women have the skills to operate online businesses, offer freelance services, teach students, sell products, or manage professional projects. However, language barriers can make certain digital activities more difficult.

Consider a woman who makes handmade products and wants to sell them online. She may be comfortable explaining her products in Punjabi but less confident writing professional English descriptions.

A well-developed regional-language AI assistant could help convert her ideas into polished product descriptions, customer messages, marketing content, and business documentation.

Similarly, Urdu speech recognition could help a freelancer turn spoken notes into written proposals or organize client discussions.

These tools could reduce the time required for administrative work and make professional participation more accessible.

There is also a financial planning dimension.

Someone building a small business should evaluate AI tools based on the time they save, the additional customers they help reach, and the quality of the work they produce.

Paying for an expensive language AI subscription is not automatically a good investment. The technology should demonstrate measurable value.

For women entering digital entrepreneurship, practical skills such as online communication, financial recordkeeping, digital marketing, and AI-assisted research can complement one another.

Language technology becomes valuable when it helps people turn their existing abilities into sustainable income.

What Developers Can Do to Improve Low-Resource Language AI

Building better language models requires a coordinated effort involving researchers, developers, linguists, educational institutions, and native-speaking communities.

There are several practical approaches worth prioritizing.

Build Community-Driven Datasets

Native speakers should participate in recording, transcription, translation, and quality evaluation.

Instead of collecting only formal sentences, developers should include conversations, questions, local expressions, educational material, and everyday vocabulary.

Community participation also helps prevent datasets from reflecting only the language preferences of a small group.

Develop Better Language-Specific Benchmarks

A model should not receive a high performance rating simply because it can generate grammatically recognizable sentences.

Evaluation should examine whether it can:

• Answer questions accurately.
• Preserve meaning during translation.
• Recognize regional vocabulary.
• Transcribe natural speech.
• Handle code-switching.
• Interpret informal writing.
• Avoid inventing information when uncertain.

Separate benchmarks are important because a system that performs well in Urdu translation may still struggle with Punjabi speech recognition or Sindhi OCR.

Improve Romanized Language Support

Roman Urdu and informal transliteration practices deserve more attention.

Developers should collect naturally written examples and test different spelling variations rather than assuming that users will follow a single standardized format.

This applies to other regional languages when speakers use Latin characters for informal communication.

Involve Linguists and Native Reviewers

Automated evaluation cannot replace human judgment in every situation.

A sentence can be technically grammatical while sounding unnatural, disrespectful, or culturally inappropriate.

Native reviewers can identify these problems before applications reach large audiences.

Their participation should be compensated fairly, particularly when they contribute specialized linguistic knowledge or extensive annotation work.

Small Research Projects Are Already Addressing the Gap

The low-resource language problem is receiving attention from smaller research groups and emerging technology initiatives.

One example is MirasAI’s open language data initiative, which focuses on speech and text resources for underserved South Asian languages, including Urdu, Punjabi, and other regional languages.

Such projects are important because language technology requires more than powerful computing infrastructure. It needs accessible datasets, practical evaluation resources, and contributions from people who use these languages every day.

The wider research community also needs to make datasets easier to access, document their limitations, and establish responsible licensing practices.

A useful language resource should not remain trapped inside a private research environment when it could support educational tools, accessibility applications, and local innovation.

What Businesses Should Consider Before Adopting Regional-Language AI

Companies planning to introduce AI-powered services should avoid selecting a system based only on its advertised language list.

A product claiming Urdu or Punjabi support may still perform poorly in real customer interactions.

Before deploying such technology, businesses should test it using actual examples from their target audience.

For instance:

1. Collect representative customer questions.
2. Include different accents and regional expressions.
3. Test both written and spoken interactions.
4. Evaluate code-switched sentences.
5. Ask native speakers to review the results.
6. Record common errors.
7. Establish human support for uncertain or sensitive cases.

Businesses should also measure customer satisfaction, response accuracy, and the amount of manual correction required.

This process helps determine whether an AI product is genuinely useful or simply capable of demonstrating its language skills in controlled situations.

The Future of AI Should Not Depend on Which Language You Speak

The next stage of artificial intelligence will be shaped not only by more powerful models but also by the diversity of people those models can serve.

Urdu, Punjabi, Pashto, and Sindhi are not minor languages simply because their digital resources are limited. They represent communities, economies, educational systems, cultural knowledge, and everyday communication.

The technical challenges are real. Data scarcity, script variation, dialect differences, code-switching, speech recognition errors, and insufficient evaluation all require serious attention.

But these challenges are not permanent limitations.

Community-led datasets, language-specific benchmarks, better speech technology, responsible data collection, and meaningful participation from native speakers can gradually improve performance.

For businesses, this represents an opportunity to serve customers who are often overlooked by mainstream technology.

For developers, it opens the door to building useful products around real regional needs.

For women, students, freelancers, and small-business owners, better language support could reduce barriers to education, employment, and financial independence.

Ultimately, artificial intelligence should not require people to abandon their preferred language before they can benefit from technological progress.

A truly useful AI ecosystem will be one in which people can communicate naturally, access reliable information, and participate in the digital economy without having to translate their identities into English first.