This blog discusses the importance of digitizing local languages towards advancing more equitable Language AI tools. The insights are based on ‘Scaling Language Data Ecosystems to Drive Industrial Development Growth’ – a discussion paper recently released as part of the AI Hub for Sustainable Development.  

Artificial Intelligence (AI) is transforming industries and communities on a massive scale, with many of its most promising applications centred on language. Research estimates that AI will contribute US$15.7 trillion to the global economy by 2030, with natural language processing technologies and skills expected to be a key driver of growth. 

Despite this economic potential, there is an existing global inequality that needs to be addressed – AI innovations are mostly concentrated in a handful of high-resource languages. This significantly limits non-native speakers of these languages from not only accessing the benefits of the innovations but informing their development over time with local contexts and realities embedded.

The AI language gap

Today’s patterns in AI development risk widening the digital divide. This is particularly evident with respect to language, as only a handful of widely spoken languages – such as English, Spanish and Mandarin – are considered sufficiently resourced to support effective AI development. This excludes more than 7000 languages and numerous dialects spoken around the world that are largely not accounted for.  

This disparity persists despite growing demand for multilingual systems in both research and entrepreneurship. Across many communities, innovators and researchers seeking to build responsive local infrastructure and integrate diverse languages into AI systems often face unreliable outputs, higher costs and insufficient safeguards. Some of the world’s most linguistically diverse regions – particularly in Africa, Asia and Latin America – are also systematically under-represented in natural language processing research.  

Significant gaps also exist in our current understanding of multilingual and multi-modal systems. This is not only a missed opportunity to advance equity, it is a fundamental barrier to development. When AI systems cannot reliably serve local populations in their preferred languages – or reflect the rich linguistic diversity of the contexts they are deployed in – their potential to drive innovation in agriculture, healthcare, education or public governance remains largely untapped.  

Ensuring that linguistic diversity is embedded throughout AI systems can unlock high-impact use cases across multiple domains of countries’ long-term development, while also building trust and strengthening local ecosystem and community agency in the digital economy.

Addressing the challenges: Four paths, lasting gains

The AI language gap and the challenges it presents are multifaceted and thus best addressed through coordinated, collective action. As part of the AI Hub for Sustainable Development’s Local Language Partnership Accelerator Pilot, we underwent a three-month learning journey with 70 innovators from 17 countries on the African continent and beyond that helped to four critical action areas for building more equitable, productive and sustainable language AI ecosystems – both locally and globally. Below are four actionable recommendations that illustrate how innovative partnerships can drive meaningful progress towards inclusive Language AI ecosystems.

1. Build awareness and amplify momentum

Governments and international organizations, public stakeholders, language communities and infrastructure providers must prioritize linguistic inclusion in their AI strategies. Countries such as Nigeria or South Africa are already leading by example, embedding multilingual AI in national policies and establishing institutional frameworks to preserve language diversity in the digital age.

2. Foster collaboration among language AI innovators

Fragmented efforts and a lack of accessible spaces for coordination and knowledge exchange limits impact and delays the uptake of successful approaches. Initiatives such as Masakhane or Mozilla Common Voice illustrate the potential of cross-border collaboration to democratize language technology.

3. Advance inclusive data collection

New data collection methods – from crowdsourced voice recordings to interactive platforms and community-led digitization – are making it possible to capture the richness of human language at scale. African organizations such as ToumAI Analytics or Kenya’s Kytabu are pioneering applications that place communities at the centre of data creation and stewardship.

4. Scale community and rights-based data governance

Frameworks that ensure that communities retain control and decision-making power over their data and linguistic heritage are crucial. The development and deployment of community-led data licenses and transparent sharing mechanisms marks a critical shift from extractive to participatory models of AI development.

A call to develop responsible AI that speaks every language  

No single actor can close the AI language gap. Governments, philanthropic organizations, funders, companies, academics and local innovators must work together to build systems that serve real people in real contexts. With concerted action, local communities can lead – rather than passively follow – in shaping inclusive AI systems.

The vision is compelling: AI leveraged responsibly can help diagnose crop diseases in Twi, facilitate teaching in rural classrooms in Kenya and enable governments to deliver public services to all citizens, regardless of their linguistic background. But without action, the digital divide will deepen – and with it, inequality.  

The AI Hub for Sustainable Development’s recently-released discussion paper titled, Scaling Language Data Ecosystems to Drive Industrial Development Growth articulates a way forward, offering diverse examples and guidance to build AI futures that include everyone from the start.

The question is no longer whether it is possible to create AI that serves the full breadth of humanity's linguistic diversity – but whether there is sufficient collective will to realize this goal.  

Read the full discussion paper here and explore how to join and contribute to the global movement for linguistic inclusion in AI: www.aihubfordevelopment.org.