The future of dispute resolution is no longer about improving existing processes—it is about rethinking the system itself.
That shift was front and center at the 2026 LawDroid AI Conference, where a clear theme emerged: AI is not simply enhancing how disputes are resolved, it is redefining the underlying infrastructure.
In her keynote, AAA President and CEO Bridget McCormack framed this theme by describing AI as a platform shift that introduces an entirely new set of rules to design and deliver dispute resolution.
Maya Markovich’s Tech Talk built directly on that premise, exploring what those new rules look like in practice and why the convergence of AI and ADR can expand access to justice.
The System Wasn’t Built for This
Pressure on the legal system is not new, but incremental change is no longer sufficient.
Most civil disputes today unfold in a system that assumes representation, time, and resources that many parties do not have. With 75% of cases involving at least one self-represented party, access is determined less by claim merits than by the ability to navigate the process.
McCormack’s keynote positioned this as a structural challenge rooted not in doctrine, but in design. Markovich’s session extended that framing by illustrating how this mismatch plays out for individuals and small businesses: the process itself is the barrier to resolution. The system is not failing at the margins—it is operating beyond its intended design.
ADR as the Bridge
Arbitration and mediation have long provided a more flexible, efficient alternative to litigation. They reduce procedural friction and allow for more tailored, party-driven outcomes.
But even ADR, in its current form, reflects the constraints of a process-driven model.
McCormack’s keynote highlighted that dispute resolution should be defined not by the tools historically used to deliver it—courts, lawyers, arbitrators—but by the outcomes it is meant to achieve: fair, consistent, and accessible resolution.
Markovich’s Tech Talk positioned ADR as the foundation, and AI as the force that allows it to scale. Where ADR reduces friction, AI reduces structural limitations. Where ADR enables flexibility, AI enables reach.
The convergence resolves a long-standing tension: how to deliver quality at scale.
From Process to System
If ADR provides the framework, AI changes the architecture.
Traditional dispute resolution is process-driven—linear, manual, and dependent on human capacity, inherently limiting efficiency, whereas AI introduces a system-driven model. As McCormack emphasized, it is rapidly moving from experimentation into core infrastructure across the legal ecosystem.
Markovich’s session translated that shift into operational terms. AI does not simply accelerate existing steps; it reorganizes them. It structures intake, organizes claims, and directs disputes along appropriate pathways from the outset, reducing friction before it compounds.
The effect is subtle but profound: instead of asking users to navigate the system, the system begins to guide the user.
Human Judgment, Repositioned
This shift inevitably raises questions about the role of human decision-makers. Both presentations converged on the same conclusion: AI does not displace human judgment, but enables more precise application.
AI excels at structure—organizing information, identifying gaps, and ensuring consistency. Human neutrals remain essential where judgment, discretion, and legal reasoning are required.
As Markovich emphasized, this is not an incidental feature but a deliberate design choice. Human oversight is integrated at the points where it matters most, allowing systems to scale without compromising fairness.
The result is not a diminished role for practitioners, but a more focused one.
Trust as the Connector
Across both sessions, one theme emerges as foundational: trust.
Dispute resolution systems derive legitimacy not only from outcomes, but from the experience of the process. McCormack underscored that when parties feel heard, they are more likely to accept outcomes and maintain confidence in the system.
Markovich reinforced that insight in the context of design. If access to justice is constrained by the ability to navigate the process, then expanding access requires more than efficiency. It requires clarity, transparency, and the ability for parties to see themselves reflected in the system.
Scaling Access and Responsibility
The convergence of AI and ADR creates the potential to resolve disputes at a scale that was previously unattainable. But that capability introduces a parallel responsibility.
Both McCormack and Markovich emphasized that scaling resolution without scaling safeguards risks amplifying inequities rather than alleviating them.
Governance, transparency, and accountability are not secondary considerations. They are integral to whether these systems earn and sustain public trust.
The Role of Practitioners
If AI is reshaping the infrastructure of dispute resolution, practitioners remain central to how that infrastructure is defined.
The shift to system-driven models creates new points of influence: how disputes are structured, how fairness is operationalized, and how oversight is embedded.
These are not technical decisions alone. They are legal, ethical, and institutional choices, and engagement is not optional.
Practical Takeaway
What emerged from the LawDroid conference is not just a vision of the future, but a reframing of the present. AI does not change the purpose of dispute resolution, or guarantee a more accessible system, but it makes one achievable.
The move from process to system raises a fundamental question: not whether the system can adapt to technology, but whether it can use that technology to become even more accessible, scalable, and trusted.