C&M E-Alert: AI, COMPETITION, AND COMPLIANCE IN INDIA: INSIGHTS FROM THE CCI’S MARKET STUDY

 WHAT HAS HAPPENED?

On October 6, 2025, the Competition Commission of India (CCI) released its market study report on ‘Artificial Intelligence and Competition’ (Report), undertaken through the Management Development Institute, Gurgaon.

 WHY DOES THIS MATTER?

The CCI’s market studies are research projects meant to identify enforcement priorities. They do not make any legal determinations or lead to binding directives. However, the Report provides the clearest guidance till date on the CCI’s likely approach on competition issues relating to artificial intelligence (AI). It also recommends a self-audit of AI systems to ensure competition compliance.

The Report arrives against a broader policy backdrop in which the Government of India has explored the need to address digital-era competition challenges. The August 2025 Report of the Parliamentary Standing Committee on Finance noted that the CCI’s market studies, including on AI, would be crucial for developing an evidence-based foundation for ex-ante regulation.

ASSESSMENT OF THE AI ECOSYSTEM

The Report looks at AI as something transformative rather than ‘just another technology’, that is increasingly seeing mainstream deployment across sectors. It underscores strong growth in India, with market size more than doubling since 2020 and expected to further quadruple by 2031.

In relation to the competitiveness of the AI ecosystem, the Report notes the following:

UPSTREAM CONCENTRATION	DOWNSTREAM VIBRANCY
A small number of big-tech firms (“hyperscalers”) hold pivotal positions in ‘upstream layers’ that create or prepare AI technologies.	Indian startups have a vibrant and growing presence in downstream layers, which entail the use of AI technologies supplied by upstream players in specific sectors.
STRUCTURAL RISKS	OTHER COMPETITIVE FACTORS
Dependence on a small set of providers for essential upstream inputs, ecosystem lock‑ins and switching costs, lead to lower bargaining power for downstream startups.	Open‑source technologies fuel startup growth and Government’s ‘IndiaAI Mission’ initiatives widen access to compute, datasets, and skills.

The Report notes the steady but uneven adoption of AI across various sectors, leading to operational gains most visible in AI‑enabled prediction and planning. It cautions, however, that adoption gaps may yield competitive asymmetries between early adopters and those that lag behind.

 COMPETITION RISKS FROM AI

The CCI identifies seven potential competition concerns arising from the rapid adoption of AI as set out below:

1. Algorithmic collusion: AI‑facilitated collusion was identified as a risk by the highest proportion of stakeholders (~37%). New-age algorithms can monitor competitor actions and coordinate conduct without human involvement even where that is not the intended design, through self-learning or signalling. Detection can be difficult due to these algorithms operating in ‘black boxes’, as well as due to rapid repricing, and multi‑market use.

2. Algorithmic unilateral conduct: A dominant enterprise can use algorithms to engage in exploitative and exclusionary conduct such as:

3. Pricing practices: AI can be used to tailor prices based on estimated willingness to pay, preferences, brand choices and purchasing behaviours. While potentially increasing output and consumer welfare, it can raise issues if competitors lack access to comparable data or if personalised pricing occurs without consumer knowledge.

4. Entry barriers: Startups and small companies in the AI industry highlighted entry barriers including quality and quantity of data, cost of cloud services, talent availability, computing facilities, funding and compute capability/ hardware.

5. Reduced transparency and choice: Lack of transparency in how foundational models, infrastructure, and platforms operate, may hinder innovation and complicate competition law compliance.

6. Network effects: The continuous data collection by AI reinforces data network effects (whereby a product becomes more valuable to users as more data is collected), which consolidates market positions of enterprises.

7. Strategic transactions: Mergers, acquisitions and partnerships in the AI industry can raise competition concerns if it can lead to anti-competitive tying strategies, is done in markets that are already concentrated, and/ or reduces number of competitors or results in exclusive partnerships.

 COMPETITIVE ADVANTAGES OF AI

The Report recognises that the above issues need to be balanced against AI’s role in fostering competition and innovation that includes:

1. speeding up product cycles, enabling real-time responsiveness and supporting dynamic pricing, thereby increasing competitive pressures and compelling innovation;

2. operational efficiency and cost reduction through automation, predictive analytics, and real-time data use;

3. enhancing supply chain management through better forecasting, inventory optimisation and predictive maintenance; and

4. improving customer engagement through personalised experiences and targeted advertising.

Cumulatively, 86% of AI user industries reported substantial to moderate improvements in competitiveness through adoption of AI. 55% of respondents believe that AI adoption in AI user industries reduces market concentration.

LEGAL FRAMEWORK

The Report does not make any suggestions on an ex-ante framework for digital markets or comment on how AI impacts it and simply notes developments in relation to the Digital Competition Bill. It further states that:

• certain 2023 amendments to address technology-driven markets in the Competition Act, 2002 are now fully operational; and

• legislative initiatives such as the Digital Personal Data Protection Act, 2023, and forthcoming AI governance guidelines by the Ministry of Electronics and Information Technology, promise to further strengthen the institutional and legal framework for AI.

 HOW TO TACKLE COMPETITION RISKS FROM AI

To promote a competitive AI ecosystem, the CCI recommends the following measures for enterprises:

1. Self-audit of AI systems: With the intent of facilitating early identification and mitigation, the Report recommends a self-audit framework comprising:

Governance and oversight	Establish clear responsibilities, involve senior management and implement approval processes for high-risk. Periodically review and report issues, and maintain documentation.
Algorithm design and development	Review objectives, programming inputs and training data. Evaluate how competitor information is processed, and document design choices and their effects.
Testing and validation	Test for unintended anti-competitive effects, employ controlled experiments and validate performance across scenarios.
Monitoring and control	Implement continuous monitoring, establish human review triggers, and maintain audit logs.
Transparency	Maintain explanations to key algorithm functions with the ability to disclose key parameters to relevant stakeholders. Create mechanisms for stakeholders to report concerns.
Compliance	Align AI development with competition compliance frameworks, train technical teams, involve legal and compliance functions in review, and integrate competition considerations into AI risk assessments.

To implement these, the Report recommends the following:

Preparation	Form a cross functional team to identify AI systems that may affect competition and establish timelines and resource requirements.
System mapping	Document working of each AI system (including its role, data inputs, design logic), map its relationship with other AI systems and determine how AI systems interact with market players.
Risk assessment	Evaluate competition risk factors and high risk-areas, assess the potential impact and consider impact from market-based factors.
Detailed audit	Apply a self-audit checklist (provided in the Report) to each prioritised system, test system performance in various scenarios, and interview system developers and users. Review documentation, code, and system behaviour.
Findings and recommendations	Document compliance strengths and weaknesses, develop specific recommendations for addressing issues, prioritise remediation actions based on risk level, and create implementation plans.
Implementation and follow-up	Execute remediation actions, verify the effectiveness of changes, document improvements and residual risks, and plan for periodic reassessment.

2. Improving transparency and reducing information asymmetry: To foster trust in AI-powered systems, the Report recommends that enterprises using AI for decision-making should clearly communicate to stakeholders the AI’s purpose, usage and its key foundational parameters.

   
   

   Further, the CCI highlights the following focus areas for the government and itself:

3. Removing entry barriers: To level the playing field, the Report reiterates the following suggestions made by stakeholders:

   • Continued focus of Government departments on expanding national AI computing infrastructure.

   • Promotion of open-source AI frameworks to allow wider access, portability and platform neutrality.

   • Development of data repositories to provide seamless access to high quality, non-personal data.

   • Equipping India’s workforce with cutting edge, industry-relevant AI expertise.

   • International collaborations for joint research and development, meaningful technology transfer provisions and cross-border data governance.

   
   

WAY FORWARD

The CCI’s granular recommendations for self-regulation balance the need to provide clarity without inciting alarm. Given that AI technology is still developing, the CCI expects enterprises to design their AI systems for competition compliance, instead of retrofitting it after issues arise. Early, well‑documented compliance will likely materially reduce regulatory and litigation risk as India’s AI governance architecture matures.

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