Artificial Intelligence and the Competition Act, 2002: CCI’s Market Study and the Architecture of Digital Competition

Aniket Ghosh, Partner, King Stubb & Kasiva

Introduction

The Competition Commission of India (CCI) has released its Market Study on Artificial Intelligence and Competition, India’s first comprehensive analysis of how Artificial Intelligence (AI) affects market structures, competitive behaviour, and enforcement priorities. Conducted by the Management Development Institute, Gurgaon, the study combines empirical surveys, stakeholder consultations, and global case law to assess whether India’s competition framework is equipped to address the challenges posed by AI-driven markets.

The report comes at a critical policy juncture. With the IndiaAI Mission (₹10,300 crore allocation), the Digital Personal Data Protection Act, 2023 (DPDPA), and the proposed Digital Competition Bill, 2024, India’s digital economy is entering a phase that requires alignment between AI innovation, data governance, and competition policy. The study outlines a framework for managing these intersections in a balanced and forward-looking manner.

The Expanding AI Market in India

The global AI market has grown from USD 93 billion in 2020 to USD 186 billion in 2024, and is projected to exceed USD 1 trillion by 2031. In India, the AI market expanded from USD 3.2 billion in 2020 to USD 6.05 billion in 2024 and is expected to reach USD 31.9 billion by 2031, implying a compound annual growth rate of over 40 percent.

AI adoption in India is concentrated in banking and financial services, healthcare, retail, e-commerce, logistics, and marketing, where algorithms are used for dynamic pricing, personalised recommendations, risk assessment, fraud detection, and automated decision-making. According to the CCI’s stakeholder survey of 106 entities, 52 percent of respondents in banking, healthcare, IT, and manufacturing reported adopting AI-based systems, 24 percent in retail, 14 percent in e-commerce, and 10 percent in logistics and marketing. These figures represent the adoption rates by sector within the study’s sample, illustrating deeper penetration in capital and data-intensive industries, such as banking and manufacturing.

Examples of use cases include: personalisation and demand forecasting in retail and e-commerce (Swiggy, Myntra, Reliance Retail), route optimisation and warehouse automation in logistics (Flipkart, Amazon), content generation and sentiment analysis in marketing (Nykaa, Coca-Cola), fraud detection and credit scoring in banking, and diagnostics and drug discovery in healthcare.

Technologically, Machine Learning forms the backbone for 76 percent of surveyed firms, followed by Natural Language Processing at 48 percent and Large Language Models at 45 percent. Approximately 76 percent of startups reported using open-source AI tools, and 43 percent indicated a reliance on hybrid architectures that combine proprietary and open-source systems. These figures highlight the industry’s preference for adaptable, cost-efficient frameworks over closed, vendor-dependent models.

AI Stack and Market Structure

The study conceptualises the AI ecosystem as a layered stack comprising upstream layers, such as data, computing infrastructure, development frameworks, and foundational models, and downstream layers, such as model fine-tuning, deployment, user interfaces, and governance mechanisms. Control over upstream layers, particularly data, compute power, and foundational models, provides a significant strategic advantage. Global hyperscalers, such as AWS, Microsoft Azure, Google Cloud, and NVIDIA, dominate these inputs. In contrast, 67 percent of Indian firms operate at the application layer, while only 3 percent are engaged in foundational model development. This asymmetry creates high switching costs and limits interoperability and bargaining power for smaller firms, reinforcing dependency on large platform providers and constraining domestic innovation.

Competitive Risks Identified by the CCI

The study identifies six primary concerns about competition in AI markets.

1. Algorithmic Collusion

AI pricing and recommendation systems can facilitate tacit coordination among competitors, even without human involvement. The study categorises algorithms as monitoring algorithms that track competitor prices, hub-and-spoke algorithms that coordinate through shared service providers, signalling algorithms that respond to market cues, and self-learning algorithms that autonomously optimise for profit. International precedents such as Topkins (US), Trod Ltd. (UK), and E-TURAS (EU) demonstrate how algorithmic pricing can distort competition. The CCI notes that similar use of AI in India could give rise to horizontal collusive conduct between competitors or abusive behaviour through unfair or discriminatory conditions in digital markets.

CCI’s Recommendations: The CCI recommends algorithmic self-audit frameworks, enabling firms to document their design logic, data inputs, and testing protocols to detect coordination risks. It also advocates for transparency measures that require firms to explain in plain terms how algorithms influence pricing and recommendations.

Implications for Businesses: Companies that use algorithmic tools for pricing, marketing, or credit risk should implement internal AI compliance reviews and retain documentation of explainability. Firms are encouraged to train legal and technical teams jointly to identify unintentional collusive outcomes and maintain a defensible record of good-faith compliance.

2. Price Discrimination and Dynamic Pricing

AI enables real-time personalised pricing based on consumer data. While this can improve efficiency, it may also create opaque and exclusionary pricing patterns that disadvantage certain consumer groups, particularly when algorithms self-adjust without transparency or oversight.

CCI’s Recommendations: The CCI urges businesses to develop explainable AI systems and provide consumer-facing disclosures where algorithmic pricing or recommendations materially affect choice. It recommends enhanced monitoring of self-learning models that evolve beyond initial training parameters.

Implications for Businesses: Firms should ensure that their algorithmic pricing models comply with the principles of fairness and non-discrimination. Establishing internal policies to review outcomes by consumer segment and maintaining logs of algorithmic changes will reduce the risk of enforcement or reputational harm.

3. Entry Barriers and Market Concentration

Structural entry barriers stem from unequal access to data, computing infrastructure, skilled talent, and financing. The CCI found that smaller firms are heavily dependent on proprietary infrastructure, particularly for cloud and data access. This dependency reinforces concentration and limits the ability of startups to scale competitively against global incumbents.

CCI’s Recommendations: The CCI recommends expanding access to computing resources and high-quality datasets, encouraging the use of open-source frameworks, and promoting collaborative innovation hubs that reduce cost barriers for startups.

Implications for Businesses: Startups and Micro, Small, and Medium Enterprises (MSMEs) can benefit from open-data initiatives such as the IndiaAI Datasets Platform and partnerships with public innovation centres. Larger firms should anticipate greater scrutiny over exclusive data arrangements or restrictive platform terms.

4. Reduced Transparency and Choice

Proprietary AI systems often lack explainability. This opacity restricts user understanding and oversight, reducing consumer choice and complicating both regulatory and market-based accountability.

CCI’s Recommendations: The CCI advocates for algorithmic transparency through disclosure of decision parameters and a shift toward interpretable model design. It also emphasises cross-regulatory engagement with the Ministry of Electronics and Information Technology (MeitY) and the Data Protection Board to develop common standards of explainability.

Implications for Businesses: Firms should invest in model interpretability tools and ensure product teams can articulate decision logic to regulators and consumers. Transparency will be viewed as a compliance differentiator and a reputational advantage.

5. Network Effects

AI-driven platforms benefit from feedback loops, where more users generate more data, thereby improving algorithms and attracting further users. These network effects can entrench incumbents, reinforce market dominance, and deter new entrants from entering the market.

CCI’s Recommendations: The CCI proposes interoperability measures and data portability frameworks to prevent lock-in effects. It also highlights the importance of promoting open technical standards to ensure market contestability.

Implications for Businesses: Dominant digital platforms should prepare for future obligations around interoperability and non-discriminatory data sharing. Smaller firms should align with open standards to improve ecosystem compatibility and avoid dependence on proprietary ecosystems.

6. Mergers, Acquisitions, and Partnerships

The study highlights an increasing trend of vertical and data-driven transactions among AI firms seeking access to datasets, compute resources, or model infrastructure. Even smaller or low-revenue acquisitions can influence market structure when they involve control over critical data assets or algorithmic pipelines. The UK Competition and Markets Authority’s review of the Google-Anthropic partnership is cited as an example of growing regulatory scrutiny of such transactions, reflecting a shift toward evaluating the competitive implications of data and AI capability consolidation rather than traditional turnover-based thresholds.

CCI’s Recommendations: The CCI advises heightened monitoring of AI-driven and data-centric M&A transactions, including minority acquisitions and partnerships. It also calls for the use of deal-value thresholds introduced under the 2023 amendments to capture data-rich but low-turnover deals.

Implications for Businesses: Firms engaging in acquisitions or strategic partnerships involving AI assets should conduct AI-specific competition due diligence. They should evaluate data access, interoperability restrictions, and potential foreclosure effects to ensure regulatory readiness.

Pro-Competitive Opportunities

Despite these risks, AI can enhance market efficiency and consumer welfare by lowering transaction costs, improving service quality, and fostering innovation. The study highlights the potential for AI to empower smaller enterprises and MSMEs through automation, predictive analytics, and cost-effective scaling. The CCI recommends targeted advocacy and policy support to increase SME participation in AI ecosystems while maintaining competitive neutrality.

Regulatory Architecture: India and Beyond

India’s AI governance framework is evolving through complementary instruments such as the Competition (Amendment) Act, 2023, which expands enforcement tools for digital markets, the Digital Competition Bill, 2024, which introduces proactive obligations for large digital enterprises, the DPDPA, 2023, which strengthens accountability for data processing, the IndiaAI Mission (2024-29), focused on compute and datasets, and the MeitY AI Governance Guidelines (2025), which emphasise transparency and harm-based oversight. The National Strategy for AI (NITI Aayog, 2018) continues to serve as a guiding policy anchor.

Globally, regulators are converging on similar principles. The European Union’s AI Act and Digital Markets Act impose ex-ante obligations on dominant platforms. The United Kingdom’s Digital Markets, Competition and Consumers Act prioritises explainability and accountability. The United States Federal Trade Commission and the Department of Justice focus on algorithmic accountability and collusion risks. At the same time, Japan, Canada, and Australia emphasise fairness and transparency in AI systems.

Regulatory Convergence: The Reserve Bank of India’s FREE-AI Framework and the CCI Market Study

The Reserve Bank of India (RBI) issued its Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) in August 2025, anticipating several themes later reflected in the CCI’s market study. While the RBI’s focus was on financial stability, data ethics, and systemic risk, its framework complements the CCI’s competition-policy perspective. The RBI identified risks of algorithmic collusion, model convergence, and concentration in financial systems, and recognised that control over data and computing power could distort markets.

FREE-AI introduced practical measures such as a Financial Sector Data Infrastructure linked to MeitY’s AI Kosh, AI innovation sandboxes, and shared compute facilities to improve access for smaller entities. It also mandated board-level AI governance and graded accountability for AI deployment. Together, the RBI and CCI frameworks create a coordinated regulatory foundation for AI in India, combining prudential, ethical, and competition oversight to ensure transparent and non-exclusionary innovation.

Strategic Outlook (2025-2027)

The CCI is expected to transition from advocacy to active enforcement. Likely developments include investigations into AI-based pricing and recommendation systems, reviews of AI-related mergers and partnerships, and a growing reliance on self-audit records as indicators of compliance. Early adopters of transparency and governance frameworks are likely to benefit from regulatory goodwill and market confidence.

KSK Commentary

“The CCI’s market study represents a significant evolution in India’s approach to competition in digital markets. Its focus on algorithmic accountability, interoperability, and coordinated regulation reflects the realities of AI-driven economies. For enterprises, AI compliance is now a strategic function integrating legal, technical, and ethical oversight.”
— King Stubb & Kasiva Competition & Technology Law Team

Conclusion

The Market Study on Artificial Intelligence and Competition establishes the foundation for India’s digital competition regime. It identifies structural risks such as collusion, concentration, and opacity while outlining measures for transparency, fairness, and inclusion. Alongside the RBI’s FREE-AI framework, the study signals the emergence of a coordinated, cross-sector approach to AI oversight that balances innovation, accountability, and market integrity.

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