AI Adoption and Investment in India Gains Momentum, Yet Remains Constrained by Infrastructure, Data Management, and Cybersecurity Issues in Achieving Responsible, Widescale Deployment
India is witnessing a surge in the adoption of Agentic AI, with spending expected to skyrocket from USD 2.1 billion in 2023 to USD 10.4 billion by 2028. However, this growth comes with significant challenges that must be addressed to ensure responsible and scalable deployment.
DebDeep Sengupta, Area Vice President, South Asia, UiPath, and Tarun Dua, Founder and MD of E2E Cloud, have emphasized the importance of investing in high-quality, diverse datasets, implementing governance frameworks, and upskilling the workforce in AI/ML, automation, data engineering, and ethical AI practices.
Biased Datasets
One of the key challenges is the prevalence of biased and low-quality data, which undermines AI fairness and accuracy. To address this, enterprises must invest in high-quality, diverse datasets and implement strong data governance frameworks that continually monitor and mitigate bias while promoting ethical AI usage.
Complex Data Engineering
Processing and engineering complex and varied data for AI workloads demand skilled personnel and modern infrastructure. This requires upskilling the workforce in AI technologies and data management, alongside adopting flexible, cloud-native platforms that support scalable, real-time AI applications.
Outdated IT Infrastructure
Legacy systems hinder seamless AI integration. Modernizing IT landscapes by migrating to cloud-based, scalable architectures is critical to enable secure and transparent AI agent interactions with internal systems and third-party models.
Cybersecurity Threats
Concerns about data privacy breaches and malicious misuse of AI are high. Organizations need to strengthen cybersecurity measures, enforce data protection policies, and deploy observability tools that monitor AI agent behaviour for risks and compliance.
Ethical Risks and Regulatory Uncertainty
Issues such as lack of transparency in AI decision-making and ethical misuse deter adoption. Implementing robust governance frameworks, including clear ethical guidelines and regulatory compliance policies, can help build trust and accountability. Transparency and explainability should be foundational principles in agentic AI deployment.
Talent Shortage
More than half of Indian enterprises cite a lack of skilled IT and AI professionals as a barrier. Addressing this requires large-scale upskilling initiatives to build AI literacy and expertise, enabling better deployment and governance of AI systems.
By actively tackling these factors, Indian enterprises can responsibly scale agentic AI, realize its productivity benefits, and set a global example for ethical AI adoption. The robust compound annual growth rate (CAGR) for agentic AI adoption in India stands at 38%.
Security and transparency are key concerns for businesses adopting agentic AI, with a significant proportion worried about data privacy breaches, lack of transparency in AI decision-making, and potential misuse by malicious actors.
This article was published by Staff India, an international franchise of our brand name Media. A similar proportion believes that agentic AI enhances decision-making across business functions.
- To ensure the fairness and accuracy of AI systems, it's crucial for enterprises to invest in high-quality, diverse datasets and implement strong data governance frameworks.
- To support AI workloads, it's essential to upskill the workforce in AI technologies, data management, and cloud-native platforms that facilitate scalable, real-time AI applications.
- Addressing the talent shortage by implementing large-scale upskilling initiatives can help build AI literacy and expertise, which is essential for better AI system deployment and governance.
