If there is one thing organisations know well, it is that disputes are expensive. The expense extends beyond legal fees, to management time, damaged relationships, and strategic distraction. A supplier dispute that should take weeks can consume months of senior attention, and for multinationals juggling cross-jurisdictional matters, the logistical burden alone can dwarf the value of the underlying claim.    

This is the problem Online Dispute Resolution (ODR), powered by artificial intelligence, is increasingly being asked to solve. The promise is compelling: faster resolution, lower cost, greater consistency, and a process that scales. But before any corporate legal team rests its case in favour of AI-enabled ODR, it is worth understanding exactly what it is, where it works, and where it can go wrong.

Meet ODR - The smarter way to settle a fight

ODR is not simply the relocation of a hearing onto a video call. It is a structured ecosystem of processes that applies established methods of alternative dispute resolution, including negotiation, mediation, and arbitration, in an online environment, using digital platforms, automated workflows, and AI tools to manage disputes from first contact through to final resolution.

The concept has been around since the late 1990s, emerging primarily to handle e-commerce disputes. eBay's Resolution Centre was one of the earliest examples. At its peak, it processed approximately 60-million disputes a year, more than the United States' civil court system, and was arguably the largest dispute resolution system in the world by volume. Since then, ODR has expanded to cover commercial contracts, employment matters, financial services, insurance, intellectual property, and court proceedings.

There are three core modes:

• Automated negotiation is the most transactional, where parties submit confidential financial positions through a platform, and an algorithm identifies where their ranges overlap without either side ever seeing the other's actual figure.
• Online mediation brings in a human neutral who facilitates dialogue digitally, via video, messaging, or asynchronous written exchange, while AI handles administrative work in the background.
• Online arbitration goes further still: a neutral decision-maker issues a binding or non-binding ruling based on documents and, where necessary, a video hearing.

ODR in the wild: How smart corporates are already using it

The applications across a typical corporate are broader than most legal teams initially appreciate. In straightforward commercial disputes, contract disagreements, delivery failures, fee disputes, ODR platforms can resolve matters in a fraction of the time litigation would require, without the relationship damage that comes with adversarial proceedings. Algorithmic negotiation tools are particularly useful here: both sides can move toward settlement without tipping their hand.

For corporates with consumer-facing operations, retail, financial services, travel, telecoms, utilities, the volume argument is compelling. ODR allows complaints to be triaged, processed, and resolved at scale. In supply chain management, ODR platforms embedded directly into supplier contracts provide a structured escalation pathway that resolves friction quickly, before it disrupts operations.

In financial services and insurance, AI plays a meaningful role in how firms categorise, prioritise, and prepare responses, reducing cost per case and improving consistency across high volumes. On the claims side, AI-driven ODR is increasingly used to assess and resolve straightforward insurance claims without human adjuster involvement.

Intellectual property is another well-established area. The WIPO UDRP process has been resolving domain name disputes entirely online since 1999, typically within 60 days, at a fraction of the cost of litigation.

AI's specific role spans intake and triage, document analysis, outcome modelling, multilingual support, fraud detection, settlement generation, and case summarisation. Lower-risk functions such as translation, triage, summarising submissions for a mediator are largely uncontroversial and genuinely useful. Higher-risk ones, such as predicting outcomes, scoring credibility, and generating automated decisions, are where considerably more scrutiny is warranted.

Cut the cost. Cut the Time. The argument for ODR is compelling

The cost case is real. For corporates handling high volumes of lower-value disputes, per-case savings from AI-supported ODR compound quickly, freeing up legal budget for matters that warrant it. Speed has direct commercial consequences: disputes that drag on create uncertainty in financial reporting, relationships, and forward planning. ODR compresses timelines from months to days or weeks, enabling cleaner balance sheets and preserving relationships that litigation would likely have destroyed.

Consistency matters as both a legal and fairness value. Inconsistent settlements on similar claims can be used against an organisation in future proceedings. AI-supported ODR enforces consistent criteria and generates documented decision trails, reducing the risk of outcomes that appear arbitrary or discriminatory, while producing the audit trail regulators increasingly expect.

The data angle is underappreciated. Every ODR-resolved dispute generates structured intelligence: where disputes concentrate, which contract terms create friction, and which suppliers repeatedly underperform, information that is entirely invisible from the unstructured record of traditional litigation.

Watch the bias. watch the data. The other side of the ODR argument

AI systems trained on historical dispute data can embed and replicate biases in that data, producing outcomes skewed across protected characteristics. For a corporate, that is not just an ethical problem; it is a source of regulatory and legal exposure. Discriminatory outcomes at scale attract regulatory scrutiny and invite group litigation.

Process fairness carries its own liability risk. A settlement obtained through a process that failed to allow adequate response, compressed timelines unreasonably, or gave no basis for its decision may be voidable. Employment outcomes derived from opaque automated tools have already attracted tribunal challenge in multiple jurisdictions, including the Amsterdam "robo-firing" matter involving Uber's algorithmic management of workers and the United States' litigation concerning alleged discrimination in AI-driven job recruitment screening systems. Auditing ODR processes for procedural adequacy is a legal risk management exercise, not an optional one.

There is also an authority laundering problem. When an AI tool presents a settlement range as the statistically likely outcome, parties anchor to it, including, at, times the corporate's own team. If the prediction is wrong or miscalibrated, the organisation may accept poor terms or push terms that later invite challenge. An AI-generated number is not a legal conclusion and should be interrogated with the same rigour as any other piece of evidence.

Data security deserves sustained attention. Dispute-related data is among the most sensitive a corporate holds: contractual exposure, financial records, HR files, and privileged advice. ODR platforms centralise this information in third-party environments. Legal teams must conduct rigorous vendor due diligence, covering data residency, encryption, access controls, retention, and whether dispute data is used to train underlying model. That last point has direct implications for privilege and trade secrets that standard vendor terms rarely address adequately. Emerging AI governance frameworks, including the EU AI Act and similar regulatory initiatives, are likely to increase scrutiny of AI systems used in dispute resolution and decision-making.

Repeat-player dynamics cut both ways. Corporates are usually the experienced, institutional side of an ODR process, which is an advantage, but patterns across resolved disputes can be aggregated and deployed against the organisation in class actions, regulatory investigations, or media coverage. The question is not only whether each dispute is handled well, but what the full portfolio looks like in the aggregate.

Finally, governance gaps remain a persistent problem. Many corporates have adopted ODR tools without answering basic questions: who owns the process; who reviews AI outputs before a decision is made; when a matter escalate to human legal review; what happens when the ODR system and a lawyer's judgement diverge. Without clear answers embedded in process design, those gaps become operational and liability risks.

ODR without governance is just risk with a faster turnaround

None of this means corporate legal teams should avoid AI-enabled ODR. It means they should deploy it properly, and that requires considerably more than purchasing a platform. A sound framework covers five areas:

• Dispute mapping - Categorise disputes by volume, value, complexity, and regulatory sensitivity. Deploy ODR where it has the most impact first, in high-volume, lower-complexity matters.
• Human review thresholds - Set clear triggers, by value, category, jurisdiction, or counterparty, for when a matter requires human legal sign-off before resolution. These must be embedded in the process, not left to individual judgement.
• Procurement discipline - Require explainability as a contractual term: the AI must be able to account for its outputs in plain language. Negotiate data handling provisions covering residency, encryption, retention, model training, and liability allocation. Standard vendor terms are rarely adequate.
• Ongoing auditing - Review ODR outcomes regularly across protected characteristics, counterparty types, and geographies. This belongs in the compliance calendar, not reserved as a response to a problem that has already emerged.
• Genuine training - Every team member interacting with ODR systems needs a proper understanding of what the AI can and cannot do, and when to override it - not merely a user guide.

(A)I rest my case - Enormous promise. Zero room for complacency

AI-enabled ODR is not primarily a technology question. It is a strategic, legal, and governance question, and the corporate legal teams that approach it that way will get considerably more value from it, and considerably less liability.

During a recent interview with Steven Bartlett, host of The Diary of CEO, Dr John Lennox compared artificial intelligence to a sharp knife. He explains that, like a knife, AI can be used for immense good, such as life-saving surgery, or destructive evil, such as murder, depending entirely on the hands that wield it. This is true of AI-enabled ODR.

Used well, it can genuinely transform a dispute management function - lower cost leading to greater consistency, preserve relationships, and create a data trail that generates real business intelligence. Used without proper governance, it produces the opposite, discriminatory outcomes, voidable settlements, data exposure, and an over-reliance on AI outputs that replaces sound legal judgement with automated convenience.

The organisations that get this right will not necessarily be those with the most sophisticated tools. They will be those that combine capable technology with clear governance, meaningful human oversight, and an honest understanding of where the process must serve fairness, not just efficiency. And on that basis, (A)I rest my case.

Written by Garth Duncan, Partner, Brittany Leroni, Senior Associate & Razina Mahomed, Candidate Attorney at Webber Wentzel