Disputes in the AI era: the challenge for in‑house legal teams

Artificial intelligence (AI) has rapidly moved from an experimental tool to an intrinsic resource for many businesses. With this, AI‑related disputes have surged, overtaking more traditional risk areas such as intellectual property infringement and breach of contract.

AI may bring significant opportunities and create efficiencies for businesses affecting HR, procurement, data governance, product development and customer engagement, but its widespread use increases risk exposure.

In‑house legal teams are already seeing this impact. AI is increasingly featuring in commercial, employment and regulatory disputes and this trend is expected to accelerate. All of this is occurring against the backdrop of constrained budgets, stricter regulatory frameworks (including the implementation of the EU Artificial Intelligence Act) and organisational pressure to move faster with digital developments.

Organisations that succeed in using AI will not be those that eliminate risk, but those that understand, allocate and manage it strategically.

Where are AI-related claims emerging?

Contracts for AI services

Drafting must now anticipate failure modes, not just functionality. Contractual disputes are rising as organisations face:

o Variable or unpredictable outputs

o Model drift and hallucinations

o Dependency on third‑party model providers.

Privacy and data protection

Automated decision-making and cross-border transfers are driving complaints and investigations. Employees entering confidential or personal data into public tools is becoming a hidden but significant driver of disputes. Complaints and investigations are being driven by:

• Unclear lawful basis for AI processing
• Automated decision‑making without sufficient safeguards
• Cross‑border data transfers
• ‘Shadow AI’ use across the workforce – employees entering confidential or personal data into public tools.

Employment and discrimination claims

Employment disputes represent one of the most pressing AI-related risks. Where employers can’t explain or justify AI‑influenced decisions, liability risks increase sharply. AI‑driven hiring, performance management and workplace monitoring tools are flashpoints. Courts and tribunals will expect transparency and evidence of human oversight.

Key risks include:

• Algorithmic bias in recruitment or promotion tools
• Opaque AI decisions
• AI‑scored performance assessments used in redundancy or dismissal
• Employee AI‑drafted grievances containing inaccurate or irrelevant legal analysis.

Liability attribution

As AI systems become more autonomous, questions of accountability will intensify:

• Who is liable when AI overrides or influences human judgement?
• Can negligence be established where the decision-making logic is opaque?
• How is causation proven when outputs are probabilistic rather than deterministic?

AI disputes will require an explanation of how a particular output or decision was generated. In‑house legal teams must be prepared to evidence:

• Version histories
• Human review and escalation records
• Bias and fairness testing
• Governance documentation, transparency and audit trails.

What should in-house legal teams do now?

Upskilling on AI procurement, bias testing, data protection and incident response will be key to managing risk. Businesses prepared for AI-related disputes will be those able to demonstrate governance, transparency and control.

1. AI governance frameworks

Implement restrictions on generative AI use. Policies need to be embedded across the workplace with practical governance. Effective frameworks may include:

• Clear acceptable use policies
• Approved-tool registers
• Restrictions on uploading confidential data to public models
• Prompt reporting and transparent incident-escalation pathways
• Defined board-level accountability.

2. Contractual protections

Supplier contracts will need to evolve as fast as the technology. They should be regularly reviewed and renegotiated as necessary, to include:

• Warranties on training data provenance, driven by the need to verify data origins, ensure IP safety and mitigate biases
• Liability allocation for hallucinations and model drift
• Audit and transparency rights
• Indemnities aligned with risk exposure
• Testing requirements – for example, is a circuit breaker trigger necessary in case the AI system becomes prejudicial to the business?
• Training requirements
• Risk allocation frameworks
• Dispute resolution mechanisms and choice of forum. AI disputes can involve confidential data, proprietary models and sensitive algorithms. Arbitration can offer privacy and specialist decision‑makers. Alternative dispute resolution (ADR) such as mediation should be incorporated into contracts, as courts increasingly expect ADR engagement prior to proceedings.

3. Human oversight

Human review can be a strong defence in AI disputes. Regulators and courts have criticised excessive reliance on AI without verification. All AI‑assisted decisions, particularly in HR, risk and customer‑facing functions, should be subject to documented human oversight.

Training is key – in-house teams, HR teams and line managers should be trained to recognise potential AI bias and correctly interpret AI‑generated reports.

4. Dispute preparedness reviews

Many organisations are now mapping AI use across the business and assessing where disputes are most likely to arise. Reviews should include:

• Evidence preservation for prompts, datasets and model versions
• Insurance coverage assessments
• Bias and performance audit records
• Dispute scenario planning and early-stage identification of technical experts.

5. Strengthened cyber security

AI has accelerated the scale and sophistication of cyber threats. Investment in cyber resilience and cyber insurance is now fundamental to litigation risk mitigation.

Competitive advantage

AI‑related disputes are rising and will continue to rise. Businesses will need to ensure they can withstand scrutiny when AI use is challenged. Those that invest in governance, transparency and evidential readiness now will be able to minimise risk.

In-house legal teams must develop an understanding of how to govern AI before it governs their organisation’s litigation exposure. A thorough review and the implementation of clear policies will help manage AI risks. If an AI dispute arises, these measures – together with advice at an early stage from expert external dispute resolution counsel – will assist in achieving a faster and more cost-effective resolution.

Practical checklist for in‑house legal teams

□ Map AI usage throughout the business: catalogue systems and maintain a live register

□ Assess employment risk: run bias and impact assessments, update hiring, monitoring and automated‑decision policies

□ Review AI contracts: require data provenance warranties, audit rights, model transparency, SLAs and indemnities

□ Tighten privacy controls: minimise data, set retention rules and log automated decisions in line with ICO guidance

□ Improve governance and documentation: implement procurement approvals, policies for employee use (including shadow AI), human‑in‑the‑loop criteria and a model risk taxonomy

□ Dispute readiness: preserve model and dataset evidence, maintain version control and review forum clauses

□ Review cyber response measure: strengthen third‑party oversight and clarify insurer requirements

□ Board reporting: prepare AI risk heatmap, track actions and owners and tie funding requests to measurable risk reduction.