Artificial Intelligence in India: Blending Modern Technology with Ancient Wisdom
By Amrit Kaur Saggu, Nipunika Shahid, Shikha Arora
Department of Sciences, Department of English and Cultural Studies, Department of Business and Management, CHRIST UNIVERSITY, Delhi NCR
Artificial Intelligence (AI) is transforming workplaces worldwide, reshaping job roles, improving efficiency, and setting new industry standards. In India, however, AI’s impact goes beyond automation—it’s becoming a powerful tool for preserving and enriching the Indian Knowledge System (IKS), which holds the nation’s deep cultural and intellectual heritage. This article explores how AI is driving workforce transformation, helping to sustain traditional knowledge, and navigating the ethical and social challenges that come with this shift.
AI is changing how we work, requiring constant upskilling and adaptation. At the same time, it’s helping to safeguard India’s vast knowledge traditions, ensuring that technological progress doesn’t erase cultural identity. Let’s explore how AI is shaping India’s future while honoring its rich past.
AI in Indian Workplaces: A New Era
The Need for Upskilling
The Indian job market is undergoing a major shift as AI-driven automation streamlines tasks and changes skill requirements. According to a recent survey, Indian knowledge workers are adopting AI faster than their global counterparts—over 80% of Indian professionals use AI tools regularly, compared to 68% globally (Economic Times). Indian engineers are not only embracing AI but also contributing to codifying abstract knowledge into structured AI models (Times of India).
Boosting Efficiency at Work
Indian businesses are already seeing the benefits of AI, from predictive analytics in finance to robotic process automation (RPA) in manufacturing. AI adoption is helping firms reduce operational costs and increase decision-making efficiency. The rise of generative AI tools, including ChatGPT and GitHub Copilot, is accelerating coding, writing, and customer service processes. However, AI-driven changes require businesses to implement structured governance policies to avoid ethical and security pitfalls.
AI and the Indian Knowledge System (IKS)
Preserving Traditional Knowledge
AI is playing a crucial role in preserving India’s intellectual and cultural heritage. AI tools are being used to digitize ancient manuscripts, translate indigenous texts, and catalog traditional medicinal knowledge. According to Bairangula (2024), AI-driven archival systems are making India’s cultural wealth accessible to future generations, ensuring that this rich history isn’t lost to time .
AI-powered platforms are also helping to preserve India’s linguistic diversity. Real-time translation and speech recognition tools are enabling more accessible communication across India’s 22 official languages and numerous dialects. However, over-reliance on AI-based translation could erode linguistic nuances, highlighting the need for culturally sensitive AI models.
Integrating IKS into Education
AI-driven learning platforms are making traditional knowledge more accessible by integrating IKS into modern education. According to recent research, adaptive learning systems use AI to personalize content delivery, allowing students to engage with ancient texts and traditional knowledge systems interactively (CXO Today). For AI to effectively support IKS, educators need proper training in AI tools to ensure they’re used correctly and respectfully.
Challenges and Ethical Considerations
Balancing Cultural Authenticity and AI Moderation
AI moderation systems, often trained on Western-centric data, may misrepresent or dilute Indian cultural narratives. Ensuring that AI reflects India’s diverse cultural landscape is essential for maintaining authenticity. AI models must be trained using localized data to capture the depth and diversity of Indian traditions accurately.
Example Cases:
YouTube Content Moderation: Indian classical dance videos have faced demonetization or removal due to AI moderation systems flagging traditional attire as “inappropriate” based on Western standards. Bharatanatyam dancers reported their videos being mistakenly labeled as “adult content” because AI algorithms failed to interpret the cultural context behind the attire and movements.
Facebook’s AI Misinterpretation: In 2021, posts about Indian festivals such as Holi and Diwali were mistakenly flagged as “promoting violence” due to AI’s misreading of traditional terminologies and festive imagery. This highlighted the need for AI training using culturally specific datasets.
Google Translate’s Cultural Bias: When translating from Hindi or Tamil to English, traditional phrases or religious terminologies are often misrepresented, losing their intended meaning. For example, “शुभ विवाह” (Shubh Vivah) was translated as “Good Wedding,” stripping the term of its sacred and spiritual connotation.
Linguistic Diversity and AI Bias
India’s rich linguistic diversity presents another challenge. While AI has enabled real-time translations and speech recognition for regional languages, over-reliance on AI-based translations could erode linguistic nuances and lead to a loss of cultural identity. Developing AI models that respect linguistic diversity is crucial to ensuring the preservation of India’s complex linguistic heritage.
Example Cases:
Microsoft’s Speech Recognition Failure: Microsoft’s speech-to-text system struggled to accurately transcribe Hindi and Tamil words due to the system’s bias toward English phonetics. During a corporate event in Chennai, a speaker’s Tamil phrase “Vanakkam” (a respectful greeting) was transcribed as “Banakam,” changing the word’s meaning entirely.
Amazon Alexa’s Regional Language Barrier: While Alexa supports Hindi, it struggles with dialects such as Awadhi and Bhojpuri. Users reported that Alexa misinterprets common phrases, leading to incorrect responses. For instance, the Bhojpuri word “Ka ho” (What happened?) was misunderstood as “Car home.”
Google’s Regional Language Mismatch: Google’s AI misinterpreted a Marathi folk song as a Punjabi bhangra track due to a lack of distinct training data for regional folk music. This reflects AI’s difficulty in distinguishing between regional dialects and musical traditions.
Reducing Dependence on Foreign AI Models
Much of India’s AI development relies on Western technology. Experts recommend building indigenous AI models to reduce dependency and protect India’s data sovereignty. Developing local AI frameworks will empower India to chart its technological future while safeguarding against geopolitical risks.
Example Cases:
TikTok Ban and Data Sovereignty: After the Indian government banned TikTok in 2020 due to data privacy concerns, local startups like Chingari and Moj emerged to fill the gap. These platforms adopted AI-based content recommendation engines tailored to Indian user behavior, reducing reliance on Chinese AI models.
Aadhaar and Indigenous AI: India’s Aadhaar biometric system uses an indigenous AI framework developed by the Unique Identification Authority of India (UIDAI). This system processes over 1 billion identities while ensuring data security and sovereignty, without relying on foreign technology.
Bhashini AI Project: The Indian government launched the Bhashini project to create AI models for regional languages using Indian data sets. This initiative aims to reduce India’s dependence on platforms like Google and Microsoft for language processing.
AI’s Impact on Industry: The Numbers Tell the Story
Generative AI: A Game-Changer
Generative AI tools like ChatGPT, DALL•E, and GitHub Copilot are redefining how work gets done. A 2024 McKinsey report revealed that 58% of employees use AI tools, often without formal approval from their organizations. While these tools enhance productivity, they also introduce compliance risks, giving rise to Shadow AI—the unauthorized use of AI in the workplace.
Example Cases:
Wipro’s Use of ChatGPT: In 2023, Wipro discovered that employees were using ChatGPT to automate code generation and customer support responses without company approval. While it boosted productivity, it exposed proprietary data to potential leaks.
Infosys and Code Vulnerability: Engineers at Infosys used GitHub Copilot to speed up coding but unknowingly introduced vulnerabilities into the software, leading to a security breach. The AI-generated code was not fully compatible with Infosys’ proprietary security protocols.
The Rise of Shadow AI
Shadow AI is becoming a growing concern for businesses. A 2024 Deloitte study found that 40% of organizations experienced data leaks due to unauthorized AI use. Shadow AI manifests in several ways:
Content Creation: Marketers using AI-generated copy without verifying brand authenticity.
Example: An Indian fashion brand used AI to create promotional content that misrepresented traditional attire, sparking backlash on social media.
Software Development: Engineers relying on AI-assisted coding tools that expose proprietary code.
Example: Tata Consultancy Services (TCS) faced a potential intellectual property leak when engineers uploaded proprietary code to OpenAI’s Codex, unaware that it could be accessed publicly.
Legal and Compliance Risks: AI-powered document review tools being used without proper oversight, potentially compromising confidentiality.
Example: Cyril Amarchand Mangaldas became the first Indian law firm to introduce AI-based contract review tools through a partnership with Kira Systems in 2017. While it improved contract analysis speed, human oversight was essential to avoid misinterpretation of Indian legal terms. Similar concerns have been raised by Shardul Amarchand Mangaldas and Khaitan & Co about AI-based legal research tools misinterpreting Indian statutes.
Governance Strategies
To address these risks, businesses need to establish strong AI governance policies. According to the Harvard Business Review, companies with AI governance frameworks report 30% fewer security breaches than those without such policies.
Example Cases:
Tata Group created an AI Ethics Committee to oversee AI-based decisions and data security.
HDFC Bank implemented an AI literacy program to educate employees about responsible AI use, reducing unauthorized AI usage by 35%.
Indian IT Ministry proposed guidelines for AI adoption that require companies to disclose AI usage in customer interactions and secure customer data with local encryption standards.
Ethical AI: Moving Forward Responsibly
Strategies for Responsible AI Adoption
AI Literacy Programs
To ensure the responsible use of AI, Indian companies and government institutions have started implementing AI literacy and training programs:
Wipro’s AI Upskilling Initiative – Wipro has launched an AI training program under its ‘School of Innovation’ to equip employees with AI-related skills, focusing on responsible AI use and compliance.
HDFC Bank’s AI Training for Employees – HDFC Bank has initiated AI literacy programs to help employees understand AI-based financial decision-making and customer service protocols.
Government of India’s AI Training for Bureaucrats – In 2023, the Indian government, in collaboration with NITI Aayog, introduced AI training modules for civil servants to enhance their understanding of AI in governance.
AI Ethics Committees
Indian institutions are setting up dedicated oversight bodies to ensure ethical AI deployment:
NITI Aayog’s AI Ethics Framework – NITI Aayog has developed guidelines for ethical AI use, focusing on transparency, fairness, and accountability.
Reserve Bank of India (RBI) AI Oversight – RBI has established guidelines for AI-based decision-making in financial services, particularly for loan approvals and fraud detection.
Tata Consultancy Services (TCS) Responsible AI Program – TCS has implemented internal AI governance frameworks to monitor AI-generated outputs and ensure they align with ethical standard
Indigenous AI Development
India is reducing its dependence on foreign AI models by developing local frameworks and infrastructure:
Bhashini AI Project – Launched by the Indian government, Bhashini aims to develop AI models for Indian languages, enabling more accurate speech recognition and translation services.
Aadhaar Biometric System – The Aadhaar system, managed by UIDAI, is an indigenous AI-powered framework handling over 1 billion identities while ensuring data security and sovereignty.
Indian Language Models by Microsoft and Google – Both Microsoft and Google have introduced AI models tailored for Indian languages like Hindi, Tamil, and Marathi to improve localization.
Regulatory Compliance
India’s regulatory bodies have introduced structured AI governance policies to manage risks and ensure compliance:
SEBI’s Algorithmic Trading Guidelines – The Securities and Exchange Board of India (SEBI) has set guidelines for AI-driven high-frequency trading to prevent market manipulation and ensure transparency.
Personal Data Protection Act (PDP) – The Indian government passed the PDP Act in 2023, requiring companies to disclose AI use in data processing and ensure secure handling of customer data.
MeitY’s Draft AI Guidelines – The Ministry of Electronics and Information Technology (MeitY) has proposed guidelines for AI development, focusing on accountability, fairness, and data security.
A Vision for Sustainable AI Development
India’s approach to AI adoption must be rooted in sustainability and inclusivity. By encouraging indigenous AI development, India can reduce dependence on foreign technology and ensure that AI models are tailored to its unique cultural and social context. The Government of India’s investment in AI-driven infrastructure through initiatives like Digital India and Make in India reflects this long-term vision.
AI also has the potential to bridge socioeconomic gaps. Real-time translation tools can empower rural communities to access government services and educational content in their native languages. AI-based agricultural advisory platforms are helping farmers optimize crop yields and reduce environmental impact through data-driven insights.
India’s AI-driven future hinges on its ability to strike a delicate balance between technological progress and cultural preservation. AI must be viewed not just as a tool for automation and efficiency but as a means to enhance and protect India’s rich heritage and social diversity. By embedding ethical considerations and cultural sensitivity into AI frameworks, India can position itself as a global leader in responsible AI development—one that reflects the values and identity of its people while embracing the transformative power of technology.
