Artificial Intelligence (AI) has rapidly moved from an emerging technology to a key aspect of business operations. Organisations across a variety of sectors use AI systems to automate and streamline processes, generate content, analyse data, and deliver services. As the rate of adoption continues to accelerate, AI is becoming increasingly embedded within supply chains, commercial relationships, and contractual arrangements.
This growth has inevitably been accompanied by an increase in disputes. Businesses are no longer simply experimenting with AI tools – they are making significant commercial decisions based upon them. This means, however, that the consequences arising when an AI system fails can be substantial. Increasingly, courts are being asked to address disputes involving the development, deployment, and governance of AI systems.
The inherent complexity of AI presents challenges that do not fit neatly within existing legal frameworks. While established principles impacting assessments of liability and quantum remain relevant, it is rarely straightforward to apply these principles to AI-related disputes. The complexity of the technology, alongside the speed at which it is evolving, means that legislation and case law must adapt in tandem.
The question facing courts in 2026 is therefore not whether existing legal principles can be applied to AI disputes, but whether they are sufficiently equipped to resolve them effectively.
Existing legal frameworks
There is often a perception that AI represents an entirely new legal frontier. Yet there are existing legal principles which provide a framework through which many AI-related disputes can be assessed.
Breach of contract will likely remain the primary mechanism through which commercial AI disputes are resolved. Most AI systems are provided under contracts that set out how they are expected to perform and what happens if they fail. When an AI system fails, the primary basis for assessing liability will typically derive from the underlying contract and will involve an examination of the standards to be applied, what the parties agreed to and whether those standards and obligations have been fulfilled. In addition, when organisations fail to take reasonable care when implementing or supervising AI systems, negligence claims can arise. Intellectual property law also remains relevant to disputes involving training data, copyright, confidential information, and ownership of AI-generated content.
However, the application of those principles becomes more complicated when contracting parties attempt to determine exactly how and why an AI system reached a particular outcome.
The evidential problem
Perhaps the most significant challenge facing future AI litigation concerns evidence.
Traditional technology disputes often involve software systems operating according to predetermined rules. This makes it easier to identify the source of an error by examining code, system logs, and testing records.
AI systems, however, often operate differently. Machine learning models can produce outputs through processes that are difficult to interpret, even for the organisations that developed them. Therefore, when a dispute arises, it will likely be difficult to determine who is responsible when an AI model produces incorrect results. The problem may lie in the data used to train the model, the training process itself, or the way the system was used.
Traditional legal principles can be used to allocate liability between multiple parties. However, doing so in the AI context can be more challenging because the interaction between the various participants is often highly complex. Courts will increasingly be required to determine not only whether loss has occurred, but also where responsibility should properly be assigned.
The role of contractual risk allocation
As AI-related disputes become more common, contractual drafting is likely to become even more important, with parties increasingly seeking to address potential liability issues before disputes arise. Commercial agreements relating to AI systems now increasingly contain provisions dealing with intellectual property, liability allocation, data usage rights, and compliance with emerging regulation.
However, many organisations are procuring AI systems without fully understanding the underlying technology. Suppliers may be reluctant to provide detailed information regarding training methodologies or model architecture, particularly where such information is commercially sensitive. As a result, customers may struggle to assess whether contractual protections adequately address the risks they face.
This information imbalance creates fertile ground for future disputes, particularly where expectations regarding system performance differ from reality.
Governance and oversight failures
As AI systems become increasingly autonomous, disputes are also likely to focus on governance and oversight.
The courts, regulators and legislators are consistently emphasising the importance of human oversight in AI deployment. Yet determining what constitutes adequate oversight remains difficult in practice. Organisations are under pressure to realise the efficiency gains offered by AI, often by reducing human involvement in routine processes. The greater the level of automation, however, the greater the risk that errors will go undetected.
Future disputes may therefore focus less on the technology itself and more on the decisions surrounding its deployment. For example, did an organisation conduct appropriate testing before implementation? Were known limitations properly documented? Were employees adequately trained? Were governance frameworks sufficient to monitor performance and identify emerging risks?
These questions are easier to assess and are likely to feature prominently in future litigation, particularly where businesses have relied heavily upon automated decision-making processes, with limited or no oversight as to the outputs.
The regulatory challenge
The pace of technological development presents an additional difficulty for legislators.
AI systems are evolving far more rapidly than most legislative frameworks. This creates a persistent risk that legislation will either become outdated quickly or fail to address the practical realities of how AI systems operate.
Legislators face a difficult balancing exercise. Overly prescriptive regulation may inhibit innovation and become ineffective within a short period. On the other hand, broad principles-based regulation may provide insufficient certainty for businesses seeking to understand their obligations.
This tension is already evident across multiple jurisdictions, where governments are pursuing different approaches to AI regulation. The result is an increasingly fragmented legal landscape that creates additional compliance and litigation risks for organisations operating internationally. As AI adoption continues to expand, courts may find themselves resolving disputes in areas where regulatory guidance remains incomplete or uncertain.
The development of case law
Given the limitations of legislation, case law is likely to play a significant role in shaping the future legal framework governing AI disputes.
Many of the most important legal questions concerning AI have yet to be definitively answered by the courts. Existing judicial decisions provide only limited guidance regarding issues such as responsibility for autonomous outputs, standards of reasonable oversight, or the extent to which AI developers should be expected to anticipate harmful outcomes.
As more disputes reach the courts, judges will inevitably begin applying established legal principles to increasingly novel factual scenarios. Over time, these decisions will provide greater clarity regarding (amongst other things) liability and evidential standards.
The difficulty is that judicial development tends to be incremental. Case law evolves dispute by dispute, often over many years. By contrast, AI technology can change dramatically within a matter of months. This creates the possibility that legal precedent may struggle to keep pace with the technological reality, particularly in areas where innovation is occurring most rapidly.
Conclusion
Current legal frameworks are not unequipped to deal with AI disputes. Existing causes of action in contract, negligence and intellectual property provide a foundation upon which many claims can be brought and resolved.
However, AI introduces a level of complexity that presents significant challenges for courts, legislators, and commercial parties. Determining how an AI system reached a particular outcome, identifying the source of an error, and allocating responsibility among multiple participants may prove considerably more difficult than in traditional technology disputes.
At the same time, the rapid evolution of AI means that legislation and case law are continually trying to catch up. Whilst regulatory frameworks will continue to develop, many of the most important questions concerning liability, causation, governance, and accountability are likely to be answered through litigation rather than legislation.
AI-related legal risk can no longer be treated as a future concern. Organisations developing, procuring, or deploying AI systems should carefully consider contractual protections, governance arrangements, testing procedures, and oversight mechanisms. As AI becomes more deeply embedded within commercial activity, disputes will become inevitable. The organisations best positioned to manage those disputes will be those that have considered these issues before problems arise rather than after they have reached the courtroom.
Andrew Woolsey, Associate, Cooke, Young & Keidan LLP