Competition Law and Cognitive Automation Market Power

1. Introduction

Cognitive automation refers to technologies that combine artificial intelligence, machine learning, natural-language processing, reasoning systems, data analytics, and automated decision-making to perform tasks that previously required significant human judgment. Examples include automated customer support, document analysis, fraud detection, workflow management, intelligent software agents, enterprise decision systems, and AI-assisted business processes.

From a competition-law perspective, cognitive automation becomes particularly important when a small number of firms control critical inputs such as AI models, computing infrastructure, proprietary datasets, enterprise software ecosystems, APIs, distribution channels, or interoperability standards.

Market power itself is generally not unlawful. Competition law becomes relevant when a powerful undertaking obtains or maintains its position through anticompetitive agreements, exclusionary conduct, abusive tying, discriminatory access, predatory strategies, or acquisitions that substantially weaken competition.

There is not yet a large body of decided cases specifically labelled “cognitive automation market power.” Therefore, existing competition cases involving software platforms, operating systems, search services, interoperability, digital infrastructure, and technological ecosystems provide the main legal analogies.

2. Meaning of Market Power in Cognitive Automation

Market power is the ability of an undertaking to behave to an appreciable extent independently of competitors, customers, or market pressures.

In cognitive automation, authorities may investigate whether a company controls an important layer of the automation stack, such as:

• foundation models or specialised AI models;
• enterprise automation platforms;
• proprietary business datasets;
• cloud and computing infrastructure;
• APIs and developer interfaces;
• enterprise operating environments;
• AI-agent marketplaces;
• workflow-management software;
• identity and authentication systems;
• distribution channels.

The important question is therefore not simply whether a company has a large share of “AI.” Competition authorities normally need to identify the relevant product and geographic market and examine competitive constraints within that market.

3. Sources of Cognitive Automation Market Power

Data advantages

Cognitive automation systems can improve when they obtain large quantities of useful data. A company operating a widely used enterprise platform may consequently possess information unavailable to smaller competitors.

A feedback mechanism can develop:

More customers → more useful data → better automation → greater customer adoption → more data.

However, possession of large datasets does not automatically establish dominance. Authorities would also consider whether competing datasets can be obtained, purchased, generated, licensed, or substituted.

Computing infrastructure

Training and operating sophisticated AI systems can require substantial computing resources. Control over scarce or strategically important computing infrastructure may therefore create an entry barrier.

Competition concerns become stronger where an infrastructure provider also competes with companies that depend on that infrastructure.

Network effects

An automation platform can become increasingly valuable as more developers, businesses, integrations, and complementary applications participate.

Large ecosystems may therefore enjoy indirect network effects:

More users → more developers → more integrations → better platform → more users.

This can make entry difficult even where technically competing software can be developed.

Switching costs

Businesses may integrate automation systems deeply into databases, customer-management software, accounting systems, communications platforms, and internal workflows.

Changing provider can consequently involve retraining employees, rewriting integrations, migrating data and rebuilding automated workflows.

High switching costs may strengthen existing market power.

4. Interoperability and Market Power

Interoperability is particularly significant in cognitive automation.

Imagine that Firm A controls an important enterprise platform while Firm B develops a competing cognitive automation service. If Firm B requires technical information to communicate effectively with Firm A's platform, restrictions on interoperability could potentially disadvantage Firm B.

This issue resembles earlier competition disputes involving software interoperability.

In Microsoft v Commission (T-201/04), Microsoft was found dominant in relevant operating-system markets. One important issue concerned Microsoft's refusal to provide interoperability information needed by competing work-group server operating systems. The General Court largely upheld the Commission's competition findings.

The principle can be relevant to AI automation where control over APIs, technical interfaces, authentication systems or compatibility information prevents competing automation providers from functioning effectively.

5. Tying and Bundling

A powerful technology company may provide several products simultaneously:

cloud infrastructure + enterprise software + AI assistant + automation engine + analytics + data storage.

Bundling can benefit customers through lower integration costs and greater convenience. It can nevertheless raise competition concerns when market power in one product is used to disadvantage competitors in another.

For example, a dominant enterprise-software provider might make its own cognitive automation assistant the default or bundle it with another indispensable service.

Microsoft v Commission (T-201/04) is again instructive. Microsoft supplied Windows together with Windows Media Player. The General Court found that the bundling could give Microsoft's media player a distribution advantage that competing media players could not readily reproduce.

The analogy for cognitive automation is important: an incumbent could potentially use control over an established software environment to accelerate distribution of its own AI automation service.

6. Default Placement and Pre-installation

Defaults can matter greatly because many customers never change the technology initially presented to them.

Suppose a major enterprise platform automatically activates its own cognitive assistant whenever customers purchase its core software. Independent automation providers might technically remain available but face substantially higher customer-acquisition costs.

A major precedent is Google and Alphabet v Commission (Google Android), T-604/18 and C-738/22 P.

The dispute concerned contractual arrangements involving Android, Google Search, Chrome and the Play Store. The Commission challenged practices including certain pre-installation requirements and anti-fragmentation obligations. The General Court largely confirmed the infringement in 2022, and on 2 July 2026 the Court of Justice dismissed Google's appeal, confirming the revised penalty of €4.125 billion.

For cognitive automation, similar reasoning could become relevant where access to an essential or highly important platform is conditioned on installation, promotion or preferential treatment of the platform owner's automation service.

7. Self-Preferencing

Vertical integration can allow an automation-platform operator to participate simultaneously at several levels of the market.

For example:

Infrastructure → AI model → automation platform → agent marketplace → customer interface.

The company could potentially favour its own services in rankings, recommendations, integrations or access conditions.

An important comparison is Google Shopping (Google and Alphabet v Commission, T-612/17; C-48/22 P). The litigation concerned Google's treatment of its own comparison-shopping service relative to competing comparison-shopping services. The Court of Justice's 2024 judgment forms an important part of EU case law concerning dominant digital platforms and preferential treatment.

Applied to cognitive automation, authorities could examine whether an AI-agent marketplace gives the platform's own agents preferential visibility or access while systematically disadvantaging competing agents.

8. Access to Essential Inputs

An automation provider might depend upon access to:

• specialised datasets;
• computing infrastructure;
• enterprise APIs;
• software interfaces;
• identity infrastructure;
• model-distribution channels.

Competition law does not normally require every dominant company to share assets with competitors.

The leading EU authority is Oscar Bronner GmbH v Mediaprint (C-7/97). The Court established demanding conditions for treating refusal of access to infrastructure developed for the dominant undertaking's own business as abusive, including the importance of indispensability and the absence of realistic substitutes. Later EU case law continues to distinguish a straightforward refusal of access from other forms of exclusionary conduct.

This distinction would matter where a cognitive automation company claims that access to another company's data, API or infrastructure is indispensable.

9. Loyalty Rebates and Exclusivity

A powerful automation provider could offer discounts conditional on customers purchasing most or all automation requirements from that provider.

For example:

An enterprise receives substantially cheaper cloud computing only if it exclusively deploys the provider's cognitive automation platform.

Discounts themselves can represent legitimate price competition. The competition question concerns their structure, market context and capacity to exclude competitors.

Intel v Commission provides important guidance concerning dominant-firm exclusivity rebates. EU case law requires consideration of whether challenged rebate arrangements are capable of restricting competition where the undertaking contests their foreclosure capability.

The principle could apply to AI markets where cloud, computing, models and automation services are sold through interconnected discount structures.

10. Leveraging Market Power Between Markets

A company can possess substantial power in one technological layer without initially being powerful in another.

Consider:

Dominant enterprise platform → automation product → AI-agent marketplace.

Competition concerns can arise if control over the first market is used through exclusionary practices to protect or expand the company's position in an adjacent market.

The Microsoft and Google Android cases demonstrate why competition authorities examine relationships among technologically connected products rather than analysing each digital product entirely in isolation.

This approach is particularly significant for cognitive automation because AI ecosystems increasingly consist of interconnected products rather than isolated applications.

11. Barriers to Entry

Authorities investigating cognitive automation market power may examine several barriers simultaneously.

Technical barriers include specialised engineering expertise, model development and computing requirements.

Data barriers arise where useful proprietary information cannot readily be replicated.

Contractual barriers may include exclusivity agreements or restrictive platform conditions.

Ecosystem barriers arise from network effects, integrations and complementary applications.

Financial barriers include the capital required to develop and operate sophisticated systems.

Switching barriers arise from data migration, employee training and workflow redesign.

No individual factor automatically establishes dominance. Their cumulative effect can nevertheless make successful entry substantially harder.

Important Case Laws

1. Microsoft Corp. v Commission — T-201/04

This is one of the most important technology-sector abuse-of-dominance cases.

Microsoft held dominant positions in relevant operating-system markets. The proceedings concerned both Microsoft's refusal to provide certain interoperability information and the tying of Windows Media Player with Windows. The General Court largely upheld the Commission's findings.

Cognitive automation relevance: interoperability restrictions and bundling can potentially allow an established software provider to protect or extend market power into automation markets.

2. Google and Alphabet v Commission (Google Android) — T-604/18 / C-738/22 P

The case involved restrictions associated with Android, including product bundling, pre-installation arrangements and anti-fragmentation obligations. In July 2026, the Court of Justice dismissed Google's appeal against the General Court judgment and confirmed the revised €4.125 billion penalty.

Cognitive automation relevance: control of an ecosystem or distribution gateway may create opportunities to favour an incumbent's AI or automation products.

3. Google and Alphabet v Commission (Google Shopping) — T-612/17 / C-48/22 P

The litigation concerned Google's conduct involving its comparison-shopping service and competing comparison-shopping services.

The case is particularly important for understanding competition concerns surrounding dominant digital platforms that operate an important access channel while simultaneously offering downstream services.

Cognitive automation relevance: operators of AI-agent stores, enterprise marketplaces or automation-discovery systems may face scrutiny if platform design systematically advantages their own downstream services.

4. Oscar Bronner GmbH v Mediaprint — C-7/97

Bronner concerned access to a newspaper home-delivery system.

The judgment established demanding requirements for compelling a dominant undertaking to provide competitors with access to infrastructure developed for its own activities, particularly around indispensability and the availability of substitutes.

Cognitive automation relevance: not every valuable dataset, API, model or computing system automatically becomes infrastructure that competitors are legally entitled to use.

5. Intel Corp. v Commission — C-413/14 P and subsequent proceedings

The Intel litigation concerned rebates provided by a dominant microprocessor supplier and their potential exclusionary effects.

The case became an important authority for analysing whether rebate practices by dominant firms are capable of foreclosing competitors, including consideration of economic evidence where appropriate.

Cognitive automation relevance: cloud credits, model discounts, enterprise licensing rebates or automation-platform discounts may require competition analysis when structured around exclusivity or loyalty.

6. Deutsche Telekom v Commission — C-280/08 P

The Deutsche Telekom litigation is an important EU authority concerning exclusionary conduct by a dominant undertaking in vertically connected markets, particularly margin-squeeze principles.

Cognitive automation relevance: similar economic questions could arise if a vertically integrated AI infrastructure provider charges downstream competitors input prices that make effective downstream competition difficult while simultaneously competing against them.

7. Post Danmark A/S v Konkurrencerådet — C-209/10

This judgment provides important guidance concerning selective pricing by dominant undertakings.

The Court emphasized that competition law protects the competitive process rather than individual competitors and that pricing conduct must be assessed within its economic and legal context.

Cognitive automation relevance: aggressive pricing by a powerful AI automation provider is not automatically unlawful simply because smaller competitors find it difficult to match.

8. IMS Health GmbH v NDC Health — C-418/01

This case addressed refusal to license intellectual property and the exceptional circumstances under which such refusal can constitute abuse of dominance.

Cognitive automation relevance: the principles may become important where proprietary data structures, software interfaces or intellectual-property rights are claimed to be indispensable for developing competing automation services.

Competition-Law Framework for Cognitive Automation

A competition authority examining a cognitive automation company would generally proceed through several connected questions.

First, define the relevant market. The authority determines whether products such as enterprise automation software, AI assistants, AI agents, specialised models or cloud-based automation belong to the same or different markets.

Second, determine market power or dominance. Market share is relevant but normally not sufficient alone. Entry barriers, switching costs, network effects, data advantages, ecosystem control and buyer power may also matter.

Third, identify the challenged conduct. This could include tying, exclusive dealing, discriminatory API access, interoperability restrictions, loyalty rebates, self-preferencing or refusal of access.

Fourth, determine competitive effects. Authorities examine whether the conduct can materially restrict rivals' ability to compete, increase entry barriers or weaken innovation.

Fifth, consider objective justification and efficiencies. Integration of AI products can produce legitimate benefits such as cybersecurity improvements, lower transaction costs, technical compatibility and better performance.

Finally, consider remedies. Depending on the jurisdiction and infringement, possible remedies can include ending restrictive agreements, changing contractual practices, interoperability obligations, access remedies, behavioural commitments, fines or, in exceptional circumstances, structural measures.

Innovation Versus Market Power

This area requires an important distinction.

A cognitive automation company may become highly successful because its technology is simply better. Competition law generally does not punish firms merely for innovation, efficiency or commercial success.

The legal concern is instead whether established market power is maintained or extended through conduct that falls outside legitimate competition on the merits.

Therefore:

Superior AI technology → normally lawful competitive success.

Large market share → not automatically unlawful.

Network effects → not automatically unlawful.

But:

Dominance + exclusionary tying → potential competition concern.

Dominance + anticompetitive exclusivity → potential concern.

Dominance + discriminatory ecosystem control → potential concern.

Dominance + abusive restrictions on interoperability → potential concern.

Conclusion

Cognitive automation can create unusual concentrations of economic power because competitive advantage may arise simultaneously from data, computing capacity, AI models, software ecosystems, distribution, APIs and network effects.

Competition law therefore focuses not simply on whether an automation provider has become large, but on how that market position was obtained, how it is maintained, and whether the firm's conduct prevents effective competition on the merits.

Cases such as Microsoft v Commission, Google Android, Google Shopping, Bronner, Intel, Deutsche Telekom, Post Danmark and IMS Health provide the existing doctrinal foundation for analysing these emerging markets. Although most predate today's cognitive automation systems, their principles concerning interoperability, tying, ecosystem leverage, access, exclusivity, pricing and technological foreclosure can be adapted to competition disputes involving AI-driven automation.