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The Business Software Alliance (BSA) has laid out a powerful, three-part policy agenda—the Enterprise AI Adoption Agenda for India—urging the government to act now to secure a future where AI adds over USD 500 billion to the economy. This blueprint is not just about adopting AI; it's about making AI the engine of India's inclusive growth and global competitiveness.
Pillar 1: Forge an AI-Ready Workforce
The success of AI hinges entirely on human capital. The agenda calls for a massive, scaled-up investment in upskilling that will future-proof India's workforce.
National AI Academies: Establish and scale AI training academies nationwide at premier institutions like theIndian Institutes of Technology (IITs) and National Institutes of Technology (NITs).
Sector-Specific Training: Expand the existingIndiaAI FutureSkills program to incorporate focused, sector-specific training across vital industries like healthcare, manufacturing, and agriculture.
Global Innovation Hubs: Collaborate withglobal software companies to build AI innovation hubs, facilitating critical knowledge transfers to local enterprises and deepening the talent pipeline.
Pillar 2: Build a Data Superhighway
AI systems starve without data, and the current infrastructure and data policies are bottlenecks. The agenda demands modernization to create a seamless digital environment:
Remove Data Barriers: Urgentlyremove restrictions on international data transfers and data localization requirements to enable the adoption of cloud services, which are a necessary prerequisite for many AI tools.
Open Government Data: Expand access to non-sensitive, high-value government datasets in machine-readable formats to fuel AI research, training, and innovation in enterprise applications.
Copyright Modernization: Implement a crucialText and Data Mining (TDM) exception in India's copyright law to permit the use of publicly available data for AI training.
Lead by Example: The government mustlead by example by adopting cloud computing and AI tools in its own e-governance and Smart Cities programs.
Pillar 3: The Light-Touch Governance Framework
Clarity and confidence are essential for mass adoption. The agenda advocates for a smart, modern regulatory structure:
Risk-Based Regulation: Champion a risk-based approach to AI governance, focusing regulations on high-risk uses while avoiding unnecessary restrictions on low-risk, high-value use cases.
Harmonize Data Policy: Ensure the Digital Personal Data Protection Act (DPDP Act) explicitly supports the processing of personal data for AI training, incentivizing responsible development.
Fight Deepfakes: Work with international groups to deploy anopen industry standard for reliable content authentication and provenance mechanisms, allowing users to identify the origin and modification of AI-generated content.
Whole-of-Government Approach: Adopt a unified,whole-of-government approach to AI policy to eliminate the risk of a confusing patchwork of state, municipal, and central rules.
Comparative AI Adoption Agendas
The Enterprise AI Adoption Agenda for India is part of a global movement, yet it has unique points of focus compared to the agendas of other major economies:
Country/Region | Governance Approach | Data/Infrastructure Focus | Skilling Strategy |
India | Risk-Based approach, explicitly avoiding restrictions on low-risk/high-value AI uses. Requires a whole-of-government coordinating body. | Prioritizes Text and Data Mining (TDM) exception in copyright law. Seeks to align DPDP Act to support AI training data processing. Calls for removal of data localization requirements. | Scaling national AI academies (IITs/NITs) and expanding sector-specific training through IndiaAI FutureSkills |
United States | Focuses on building public trust by addressing bias and discrimination risks. Pushes for government efficiency by accelerating federal AI adoption. | Emphasizes modernizing IT infrastructure and increasing public access to non-sensitive government data. Advocates for strong digital trade policies to enable cross-border data flows. | Focuses on training a skilled workforce for AI infrastructure and aligning with the White House's national plan. |
Japan | Adopts an "Innovation-First" and "soft-law" approach (AI Promotion Act), aiming to be the world's "most AI-friendly country". Its primary goal is to promote R&D. | Focused on streamlined permitting for data centers and investing in world-class scientific datasets. Emphasizes trusted data use in collaboration with the US. | Focuses on digital workforce development and promoting R&D for practical applications. |
ASEAN Member States | Aims for a regionally concerted approach to responsible AI, recognizing a need for alignment due to varying degrees of AI readiness among member states. | Priorities include advancing digital infrastructure and developing secure and trusted data-sharing platforms for interoperable AI systems. | Focuses on developing an AI-ready workforce through a comprehensive skilling roadmap and tailored programs for MSMEs and youth. |
The core difference lies in the regulatory philosophy: India and Japan favor a less restrictive, innovation-focused approach to spur adoption, while the US focuses heavily on federal leadership and global dominance. Meanwhile, India's specific policy asks regarding the DPDP Act and the TDM exception highlight unique legislative reforms needed to unlock its vast data potential for enterprise AI training, which are not front-and-center in the same way in the other agendas.