The Governance Imperative: Why 83% of Indian Executives Say AI Infrastructure Needs Strict Oversight

India stands at a critical inflection point in its artificial intelligence journey. The country has emerged as a global powerhouse in AI development, ranking among the top four nations in AI skills and capabilities, and serving as the second-largest contributor to AI projects on GitHub[1]. Yet beneath this impressive facade lies a troubling paradox: while Indian organizations are investing heavily in AI infrastructure—with budgets rising by an average of 19% in 2025—the governance frameworks needed to manage this expansion remain dangerously underdeveloped.

A striking finding from recent industry research underscores this tension: 83% of Indian executives acknowledge that effective governance is essential for successful AI infrastructure deployment. However, only 4% of organizations currently have robust frameworks in place to manage AI-related risks and ensure ethical AI practices[2]. This massive governance gap represents one of the most pressing challenges facing India’s emergence as a global AI leader.

The Investment Surge and the Governance Gap

India’s commitment to AI development is undeniable. The government approved the IndiaAI Mission in March 2024 with a budget outlay of ₹10,371.92 crore over five years, marking a defining step toward making India a global leader in artificial intelligence[1]. Private sector investment has followed suit, with 58% of Indian organizations reporting increased infrastructure investment due to growing AI demand[2].

This momentum extends to data center infrastructure. In March 2024, the central government approved INR 107.3 billion (US$1.24 billion) for AI-specific data center infrastructure, designed to support high-density workloads with cutting-edge technologies like liquid cooling and energy-efficient architecture[3]. States like Andhra Pradesh and Madhya Pradesh have launched their own initiatives, with Andhra Pradesh signing an MoU with Google for an AI data center in Visakhapatnam, while Madhya Pradesh established India’s first purpose-built AI data center[3].

The scale is impressive. Over 38,000 GPUs have been onboarded under government initiatives, available at subsidized rates of just ₹65 per hour[1]. By 2027, India’s AI market is expected to reach US$20–22 billion, posting a compound annual growth rate of 30 percent[4]. Generative AI alone is forecast to contribute US$400 billion to India’s GDP by 2030 at a CAGR of 28 percent[3].

Yet this explosive growth in infrastructure investment has not been matched by corresponding advances in governance architecture. The disconnect is stark: while organizations are rapidly scaling their AI capabilities, the institutional frameworks needed to ensure responsible, transparent, and trustworthy deployment remain nascent. This creates a precarious situation where rapid innovation outpaces oversight mechanisms.

Why Executives Recognize the Governance Imperative

The 83% figure is not merely a statistic—it reflects a hard-earned realization among India’s business leadership. As AI systems become increasingly embedded in critical sectors like healthcare, agriculture, finance, and governance, the risks of inadequate oversight become impossible to ignore.

Several factors drive this executive consensus on governance necessity:

Risk Management at Scale: As AI applications expand across sectors, the potential for systemic failures, bias propagation, and unintended consequences grows exponentially. Executives recognize that without robust governance frameworks, organizations face regulatory penalties, reputational damage, and loss of stakeholder trust.

Regulatory Uncertainty: India’s AI governance landscape remains in flux. The 2025 AI Governance Guidelines represent a forward-looking, techno-legal, whole-of-government approach, yet the operational architecture required to ensure continuity across implementation phases remains undefined[5]. This ambiguity creates pressure on organizations to establish their own governance standards preemptively.

Global Competitive Positioning: As India positions itself as a democratic alternative to U.S. and Chinese AI models, governance excellence becomes a competitive advantage. Executives understand that India’s ability to demonstrate responsible AI stewardship directly influences the country’s attractiveness to global partners and investors.

Ethical and Social Accountability: With 8.65 lakh candidates enrolled in emerging technology courses, including 3.20 lakh in AI, India is building a massive AI workforce[1]. Executives recognize that this talent must be deployed within frameworks that ensure ethical practice and societal benefit.

The Critical 4% Problem: Where Governance Lags

The most alarming finding in the recent research is the governance implementation gap. While 83% of executives agree that effective AI governance is essential, only 4% currently have robust frameworks for managing AI-related risks and ensuring ethical AI practices[2]. This 79-percentage-point gap represents a fundamental vulnerability in India’s AI infrastructure.

Why is implementation so far behind recognition? Several factors contribute:

Lack of Standardized Frameworks: Unlike data protection or financial regulation, AI governance lacks mature, widely-adopted standards. Organizations struggle to determine what “robust governance” actually means in practice. The proposed governance operating system approach, while promising, remains largely theoretical[5].

Resource Constraints: Building comprehensive governance infrastructure requires significant investment in talent, technology, and processes. Many organizations, particularly mid-market companies, lack the resources to develop governance capabilities while simultaneously scaling AI deployment.

Rapid Technology Evolution: AI capabilities evolve faster than governance frameworks can be developed and implemented. By the time an organization establishes governance protocols for one generation of AI tools, new capabilities have emerged that fall outside existing frameworks.

Organizational Maturity Gaps: 75% of Indian organizations remain in the early phases of workforce maturity for AI roles[2]. When workforce capabilities are still developing, establishing sophisticated governance structures becomes exponentially more difficult.

The Three Non-Negotiables for AI Readiness

Recent research identifies three critical pillars that organizations must address to achieve genuine AI readiness, with governance forming the foundation:

Hybrid Infrastructure for AI Performance: 65% of organizations agree that implementing a ‘fit-for-purpose’ strategy for AI infrastructure has helped optimize costs and performance[2]. However, infrastructure alone is insufficient. Effective governance must determine which use cases warrant which infrastructure investments, ensuring alignment with organizational risk tolerance and ethical standards.

Trust-by-Design for AI at Scale: This represents perhaps the most critical governance imperative. Trust cannot be retrofitted into AI systems; it must be embedded from inception. This requires governance frameworks that address data provenance, model transparency, bias detection, and ethical alignment before systems are deployed at scale.

Talent and Skills for AI Impact: 83% of organizations are investing in training and recruitment for AI-related roles to support infrastructure modernization[2]. Yet governance training remains conspicuously absent from most AI talent development programs. Organizations must ensure that AI professionals understand not only how to build and deploy systems, but also how to do so responsibly within established governance frameworks.

Government Initiatives: Building the Governance Foundation

India’s government has recognized the governance imperative and is taking concrete steps to establish institutional frameworks. The IndiaAI Mission includes multiple pillars designed to support responsible AI development, including the IndiaAI Application Development Initiative, which develops AI applications for India-specific challenges in healthcare, agriculture, climate change, governance, and assistive learning[1].

Additionally, the government has established three Centres of Excellence (CoEs) in Healthcare, Agriculture, and Sustainable Cities, with a fourth announced for Education in Budget 2025[1]. These centers serve as collaborative spaces where academia, industry, and government institutions develop scalable AI solutions within governance frameworks.

The proposed implementation roadmap spans four phases extending through 2035, with Phase 1 (2025–2026) focused on mission orientation and stakeholder engagement, and subsequent phases addressing institutional setup, pilot programs, and nationwide rollout[1]. This long-term approach acknowledges that governance infrastructure cannot be rushed.

However, as governance experts note, the challenge remains translating policy blueprints into operational architecture. The gap between principles and infrastructure persists—policies define intent, but architecture defines capability[5].

The Path Forward: From Recognition to Implementation

Closing the governance gap requires coordinated action across multiple stakeholders:

For Organizations: The 4% with robust governance frameworks should become models for the broader industry. Organizations must move beyond governance recognition to implementation, establishing clear accountability structures, risk management protocols, and ethical guidelines before scaling AI deployment.

For Government: The phased implementation roadmap must accelerate institutional setup and governance design. Regulatory clarity on AI safety, liability, and transparency will enable organizations to align their governance frameworks with national standards.

For Industry Bodies: Professional associations and industry groups must develop standardized governance frameworks, best practices, and certification mechanisms that allow organizations to benchmark their governance maturity.

For Talent Development: AI governance must become a core component of AI education and professional development programs, ensuring that the next generation of AI professionals understands governance as integral to their discipline.

Conclusion: Governance as Competitive Advantage

The 83% of Indian executives who recognize the governance imperative are correct. As India invests billions in AI infrastructure and positions itself as a global leader in artificial intelligence, governance excellence will determine whether this investment yields sustainable, trustworthy, and beneficial outcomes.

The current 4% implementation rate is not a failure—it is a starting point. India has the opportunity to build governance infrastructure that balances innovation with accountability, growth with responsibility. By moving rapidly from governance recognition to implementation, India can establish itself not only as a technological leader but as a governance leader—demonstrating that advanced AI capabilities and robust oversight can advance together.

The governance imperative is not a constraint on India’s AI ambitions. It is the foundation upon which those ambitions can be realized sustainably, equitably, and at scale.