Industrial Predictive Maintenance AI Lock-In Effects

Detailed Explanation With At Least 6 Case Laws

1. Introduction

Industrial predictive-maintenance AI lock-in arises when manufacturers, utilities, transport operators, energy companies, or other industrial customers become economically, technically, or operationally dependent on a particular AI-based predictive-maintenance ecosystem.

Predictive-maintenance systems use machine-learning models, sensor data, equipment telemetry, digital twins, failure histories, maintenance records, and cloud infrastructure to predict when industrial assets are likely to fail. Once such a system becomes deeply integrated into production operations, switching to another provider may become difficult because the customer has accumulated:

• proprietary maintenance datasets;
• model-specific data formats;
• historical failure records;
• calibrated AI models;
• APIs and software integrations;
• digital-twin configurations;
• trained personnel;
• supplier-specific sensors;
• cloud infrastructure dependencies;
• cybersecurity configurations; and
• compatibility dependencies with industrial control systems.

The competition-law problem is therefore not simply high market share. The more important issue is whether an AI provider can convert technological integration and accumulated data advantages into durable customer captivity, thereby weakening competition from rival predictive-maintenance providers.

2. Meaning of Predictive-Maintenance AI Lock-In

Predictive maintenance differs from conventional preventive maintenance.

Preventive maintenance

Equipment is serviced according to predetermined schedules:

“Replace the component every 10,000 operating hours.”

Predictive maintenance

AI analyses operational data and predicts the probability or timing of failure:

“Based on vibration, temperature, load and historical patterns, this component has a high probability of failure within the next 300 operating hours.”

The AI system may therefore become an important part of the customer's operational decision-making.

Lock-in occurs where moving to another provider causes sufficiently large:

Switching Costs + Data Migration Costs + Integration Costs + Model-Recalibration Costs + Operational Risks

that the customer effectively remains tied to the incumbent.

3. Sources of Lock-In

A. Historical Data Lock-In

Predictive-maintenance systems become more valuable as they accumulate historical data.

For example:

10 years of machine data → failure correlations → trained model → improved predictions → more customer reliance → more data → stronger model

A rival entering the market may therefore face a significant data disadvantage.

The incumbent's advantage can become self-reinforcing.

B. Model Lock-In

The customer's historical data may have been transformed into:

• proprietary feature representations;
• model weights;
• machine-specific thresholds;
• anomaly-detection parameters;
• digital twins;
• failure signatures; and
• maintenance recommendations.

Even if the raw data can technically be exported, the customer may not be able to transfer the trained intelligence embedded in the incumbent's system.

This creates an important distinction:

Data portability does not necessarily equal model portability.

C. Sensor and Hardware Lock-In

An AI maintenance platform may be designed around a provider's proprietary:

• sensors;
• gateways;
• edge devices;
• industrial protocols;
• telemetry architecture; or
• diagnostic equipment.

If the AI provider requires its own sensors or gateways, replacing the software provider may require substantial physical infrastructure replacement.

D. API and Software-Integration Lock-In

Predictive-maintenance AI frequently connects to:

• ERP systems;
• manufacturing-execution systems;
• SCADA;
• industrial IoT platforms;
• cloud services;
• inventory systems;
• work-order management systems; and
• enterprise cybersecurity systems.

A customer switching providers may therefore need to rebuild numerous integrations.

4. Economic Effects of Lock-In

The principal competitive concern is that switching costs can reduce contestability.

Suppose:

Although the rival offers a cheaper AI service, the customer rationally remains with the incumbent.

The incumbent can subsequently increase its price because the customer's outside option has deteriorated.

This creates a potential cycle:

Installed base → data accumulation → better AI → switching costs → customer captivity → higher market power → more installed base

5. Competition-Law Theory

Industrial predictive-maintenance lock-in can implicate several theories of harm.

5.1 Abuse of dominance

A dominant provider may potentially abuse its position through:

• tying;
• bundling;
• refusal to provide necessary data;
• discriminatory interoperability;
• excessive switching costs;
• exclusionary contracts;
• loyalty-inducing arrangements;
• technical restrictions on interoperability;
• degradation of rival access; or
• exploitative contractual conditions.

5.2 Foreclosure of competing AI providers

The incumbent may use its installed customer base to make competing predictive-maintenance systems commercially unviable.

For example:

“Customers using our industrial IoT platform receive predictive-maintenance functionality, but third-party AI providers cannot access the relevant telemetry.”

The result may be foreclosure of independent AI providers.

5.3 Data advantage

A particularly important issue is whether the incumbent's control over industrial data creates a strategic input advantage.

The relevant question is not simply:

“Does the incumbent possess data?”

but:

“Does control over this data materially prevent equally efficient competitors from competing?”

6. Essential-Facility-Type Concerns

In extreme circumstances, industrial maintenance data or interoperability infrastructure might become sufficiently indispensable to raise refusal-to-supply or essential-facility-type arguments.

However, competition law generally does not require dominant firms to share every proprietary asset.

The strongest case ordinarily requires factors such as:

1. indispensability;
2. substantial elimination of competition;
3. inability to reasonably duplicate the input;
4. lack of a legitimate justification; and
5. depending on the jurisdiction, additional doctrinal requirements.

Therefore:

Proprietary AI data ≠ automatically essential facility.

7. Tying and Bundling

A predictive-maintenance provider might bundle:

industrial sensors + cloud platform + predictive AI + maintenance marketplace.

The customer may be required to purchase all components together.

Competition concerns become stronger where:

• the provider is dominant in the primary product;
• the tied predictive-maintenance service is separately contestable;
• customers are effectively forced to purchase the bundle;
• rivals are excluded from important distribution channels; and
• the arrangement produces substantial foreclosure.

8. Interoperability as a Competition Remedy

One of the most important solutions is interoperability.

A competition authority could potentially require:

• standardised APIs;
• machine-readable data exports;
• access to historical maintenance records;
• portability of sensor data;
• documented interfaces;
• non-discriminatory API access;
• migration assistance; and
• compatibility with competing AI systems.

This can convert:

closed AI ecosystem → contestable AI ecosystem

without necessarily requiring the incumbent to disclose its source code or proprietary model weights.

9. Case Laws

The following cases provide important competition-law principles applicable to industrial predictive-maintenance AI lock-in, even though most pre-date modern predictive-maintenance AI.

1. United Brands v Commission

Case 27/76, Court of Justice of the European Union

The Court examined dominance and the concept of an undertaking's ability to behave independently of competitors, customers and consumers.

Relevance

For predictive-maintenance AI, a provider controlling a sufficiently important industrial ecosystem may acquire significant bargaining power over customers.

The case supports analysing:

• customer dependency;
• market power;
• switching possibilities;
• competitive constraints; and
• the ability to act independently.

Principle: Dominance is assessed through the undertaking's economic power and the competitive constraints operating upon it.

2. Hoffmann-La Roche v Commission

Case 85/76, CJEU

This is a foundational case concerning dominance and exclusionary conduct.

The Court emphasised that dominance involves a position of economic strength enabling an undertaking to impede effective competition and behave to an appreciable extent independently.

Relevance

AI predictive-maintenance lock-in may become problematic where the incumbent's technological ecosystem creates a sufficiently durable position of economic strength.

Exclusive or loyalty-inducing arrangements surrounding predictive-maintenance services could potentially reinforce that position.

Principle: Conduct by a dominant undertaking must not distort the competitive process by reinforcing exclusionary market power.

3. Commercial Solvents v Commission

Joined Cases 6/73 and 7/73, CJEU

The case concerned a dominant undertaking's refusal to supply an input to downstream competitors.

Relevance

Predictive-maintenance AI ecosystems frequently contain upstream/downstream relationships.

For example:

Industrial IoT data → AI analytics → maintenance services

If an incumbent controls a critical upstream input and uses that control to eliminate downstream competitors, Commercial Solvents provides an important doctrinal foundation for analysing the conduct.

Principle: A dominant undertaking controlling an indispensable input may infringe competition law when it uses that control to eliminate downstream competition.

4. Bronner v Mediaprint

Case C-7/97, CJEU

Bronner is one of the leading cases concerning refusal to provide access to infrastructure under Article 102 TFEU.

The Court applied a demanding test for compulsory access.

Relevance

A predictive-maintenance provider might argue that its:

• AI platform;
• proprietary data infrastructure;
• cloud architecture;
• industrial telemetry;
• digital-twin environment; or
• predictive model

should not be made available to competitors.

Bronner cautions that competition law does not automatically impose a duty to share infrastructure merely because access would help competitors.

Principle: Compulsory access to an infrastructure requires stringent conditions, particularly where the infrastructure is indispensable and duplication is impossible or economically unrealistic.

5. Microsoft v Commission

Case T-201/04, General Court of the European Union

The Microsoft interoperability case is particularly relevant to technology-driven lock-in.

Microsoft's control over important software interfaces and interoperability information was examined in the context of exclusionary conduct.

Relevance to predictive maintenance

An industrial AI provider may similarly control interfaces necessary for competitors to interact with its ecosystem.

Potential issues include:

• withholding APIs;
• restricting technical interoperability;
• making third-party integration unnecessarily difficult;
• degrading compatibility; and
• using platform control to protect an adjacent AI market.

Principle: Control over interoperability can become a competition concern where it is used to exclude rivals from an adjacent market.

6. Intel v Commission

Case C-413/14 P, CJEU

Intel concerned conditional rebates and exclusionary effects.

The Court clarified the importance of analysing the actual or potential capability of rebates to foreclose equally efficient competitors in appropriate circumstances.

Relevance

Predictive-maintenance providers could theoretically offer:

“Discounted industrial AI if the customer commits all maintenance analytics to our platform.”

Such arrangements may create significant switching barriers and foreclose competing AI providers.

Principle: Loyalty-inducing commercial arrangements by a dominant undertaking require careful assessment of their foreclosure effects.

7. Google Shopping

Case C-48/22 P, Google and Alphabet v Commission

The case concerned Google's conduct in relation to comparison-shopping services.

Relevance

The broader significance for industrial AI is the possibility that dominance in one digital ecosystem can be leveraged to favour an adjacent service.

For example:

industrial IoT platform dominance → preferential treatment for proprietary predictive-maintenance AI

could potentially raise analogous leveraging concerns.

Principle: Dominance in one digital ecosystem may create competitive risks where the undertaking uses that position to advantage an adjacent service.

8. Slovak Telekom v Commission

Joined Cases C-152/19 P and C-165/19 P, CJEU

The case concerned exclusionary conduct involving access to telecommunications infrastructure.

Relevance

The case is useful for analysing infrastructure-dependent digital ecosystems.

Industrial AI systems similarly operate through layered infrastructures:

machines → sensors → connectivity → cloud → analytics → AI → maintenance

Control at one layer can potentially be used to restrict competition at another.

Principle: Infrastructure control and discriminatory access conditions can form part of an exclusionary strategy where they impair downstream competition.

10. Application to Industrial Predictive-Maintenance AI

A hypothetical dominant industrial-AI provider could engage in the following conduct:

Scenario

Company A supplies:

• industrial sensors;
• IoT gateways;
• predictive-maintenance AI;
• cloud infrastructure; and
• maintenance-management software.

After five years, Company A possesses extensive historical data from thousands of industrial machines.

Company B develops superior predictive-maintenance AI.

However, Company A:

1. refuses interoperable access to machine telemetry;
2. exports only aggregated data;
3. prevents customers from exporting machine-specific histories;
4. makes its API technically restrictive;
5. requires customers to use Company A's sensors;
6. offers substantial discounts for exclusive use; and
7. imposes expensive migration fees.

The result is:

Rival AI → technically superior → unable to obtain necessary data → weak customer adoption → reduced scale → weaker AI → further incumbent dominance

That is a classic feedback-loop foreclosure theory.

11. Lock-In and the Data Network Effect

Predictive-maintenance AI can produce a particularly powerful network effect:

More machines monitored

↓

More operational data

↓

More failure events observed

↓

Better model training

↓

More accurate predictions

↓

More customers

↓

Still more machine data

This creates a data-feedback loop.

The competition-law concern becomes stronger when the incumbent's accumulated data cannot reasonably be replicated by rivals.

12. Switching Costs as a Barrier to Entry

Traditional entry barriers include:

• capital requirements;
• intellectual property;
• economies of scale;
• regulation.

AI predictive maintenance introduces another category:

Data-and-integration barriers

A new entrant may have an excellent algorithm but lack:

• historical machine data;
• machine-specific failure histories;
• integration with legacy equipment;
• customer-specific calibration;
• maintenance workflows;
• technician familiarity; and
• certification or safety validation.

Consequently:

Algorithmic superiority may not overcome ecosystem lock-in.

13. Competition-Neutrality Problem

The issue becomes particularly significant where an industrial platform simultaneously acts as:

1. infrastructure provider;
2. data controller;
3. AI developer;
4. maintenance-service provider; and
5. marketplace operator.

It may therefore compete against firms that depend upon infrastructure it controls.

This creates a structural conflict:

Platform owner + data gatekeeper + downstream AI competitor

The platform may have incentives to disadvantage rival predictive-maintenance providers.

14. Potential Competition-Law Theories of Harm

15. Legitimate Business Justifications

Not every lock-in effect is unlawful.

An incumbent may legitimately argue that restrictions are necessary for:

• cybersecurity;
• industrial safety;
• model integrity;
• intellectual-property protection;
• protection against corrupted data;
• reliability;
• regulatory compliance;
• preventing unauthorised machine commands; or
• protecting confidential customer information.

Competition authorities therefore need to distinguish:

legitimate technological integration

from

artificial foreclosure.

16. Appropriate Remedies

Possible remedies include:

A. Data portability

Require customers to obtain machine-readable historical data.

B. API interoperability

Require documented and non-discriminatory interfaces.

C. Migration rights

Customers should be able to migrate to competing AI providers without punitive charges.

D. Contractual restrictions

Limit excessively long exclusive arrangements.

E. Separation of infrastructure and AI

In serious cases, structural or behavioural separation could be considered between:

industrial platform infrastructure

and

downstream predictive-maintenance AI.

F. Non-discrimination

The platform should provide rival AI providers with access on objectively equivalent terms.

17. Important Distinction: Data Portability vs Source-Code Disclosure

A competition remedy should not automatically require disclosure of proprietary AI technology.

There is a substantial difference between:

Customer data

and

provider's proprietary algorithm.

A proportionate remedy might require:

export of historical sensor and maintenance data + interoperability APIs

without requiring:

disclosure of source code + model weights + trade secrets.

This distinction is crucial for maintaining incentives to innovate.

18. Competition Assessment Framework

A competition authority investigating predictive-maintenance AI lock-in could ask:

Step 1 — Define the market

Is the relevant market:

• industrial AI generally;
• predictive-maintenance software;
• machine-specific predictive analytics;
• industrial IoT platforms;
• cloud-based maintenance analytics; or
• an ecosystem encompassing several products?

Step 2 — Establish market power

Consider:

• market share;
• installed base;
• customer dependency;
• data advantages;
• switching costs;
• interoperability;
• multi-homing;
• entry barriers.

Step 3 — Identify lock-in

Determine whether customers face:

• technical;
• financial;
• contractual;
• operational; or
• data-related switching costs.

Step 4 — Examine conduct

Analyse:

• tying;
• exclusivity;
• refusal to supply;
• discrimination;
• self-preferencing;
• interoperability restrictions;
• rebates.

Step 5 — Assess foreclosure

Would equally efficient rivals be unable to compete effectively?

Step 6 — Consider efficiencies

Could the conduct improve:

• safety;
• cybersecurity;
• reliability;
• model accuracy;
• industrial performance?

Step 7 — Select proportionate remedies

Prefer targeted interoperability and portability remedies before more intrusive structural intervention where those remedies adequately restore competition.

19. Key Legal Principle Emerging From the Case Law

The combined lesson of United Brands, Hoffmann-La Roche, Commercial Solvents, Bronner, Microsoft, Intel, Google Shopping and Slovak Telekom is that competition law does not condemn technological success or customer integration as such.

The concern arises when:

a technologically integrated and data-rich incumbent converts legitimate innovation advantages into artificial barriers that prevent customers from switching and prevent rivals from competing effectively.

Thus, the central question is not:

“Does predictive-maintenance AI create lock-in?”

but:

“Has the undertaking used its market power, data control, infrastructure control or contractual arrangements to make that lock-in exclusionary rather than merely a natural consequence of superior technology?”

20. Conclusion

Industrial predictive-maintenance AI lock-in represents a significant emerging competition-law issue because AI systems become more valuable through accumulated operational data, machine-specific calibration and deep integration with industrial infrastructure.

The principal competitive danger is a self-reinforcing ecosystem:

Data accumulation → superior predictions → customer dependency → switching costs → rival exclusion → greater data accumulation.

Competition law can address the problem through established doctrines concerning dominance, refusal to supply, interoperability, tying, exclusivity, discriminatory access, rebates and leveraging, while remaining careful not to penalise legitimate innovation.

The most proportionate regulatory approach is likely to focus on data portability, interoperability, reasonable migration rights and non-discriminatory access, reserving stronger structural remedies for situations in which behavioural measures cannot restore effective competition.

Core takeaway: Industrial predictive-maintenance AI becomes a competition concern when data, software, sensors and infrastructure are deliberately integrated into a closed ecosystem that makes customers captive and rivals unable to compete, rather than merely because the AI system has achieved technological or commercial success.