The Stanford Legal Design Lab hosted its second annual AI & Access to Justice Summit as a gathering for leaders from legal aid organizations, technology companies, academia, philanthropists, and private practice. This diverse assembly of professionals gathered to discuss the potential of generative AI, and — most crucially at this moment of Autumn 2025 — to strategize about how to make AI work at scale to address the justice gap.

The summit’s mission was clear: to move beyond the hype cycle and forge a concrete path forward for a sustainable AI & A2J ecosystem across the US and beyond. The central question posed was how the legal community could work as an ecosystem to harness this technology, setting an agenda for 2, 5, and 10-year horizons to create applications, infrastructure, and new service/business models that can get more people access to justice.

The Arc of the Summit

The summit was structured over 2 days to help the diverse participants learn about AI tools, pilots, case studies, and lessons learned for legal teams — and then giving the participants the opportunity to design new interventions and strategies for a stronger AI R&D ecosystem.

Day 1 was dedicated to learning and inspiration, featuring a comprehensive slate of speakers who presented hands-on demonstrations of cutting-edge AI tools, shared detailed case studies of successful pilots, and offered insights from the front lines of legal tech innovation.

Day 2 was designed to shift the focus from listening to doing, challenging attendees to synthesize the previous day’s knowledge into strategic designs, collaborative agendas, and new partnerships. This structure was designed to build a shared foundation of knowledge before embarking on the collaborative work of building the future.

The Summit began by equipping attendees with a new arsenal of technological capabilities, showcasing the tools that serve as the building blocks for this new era in justice.

Our Key AI + A2J Ecosystem Moment

The key theme of this year’s AI+A2J Summit is building a strong, coordinated R&D ecosystem. This is because our community of legal help providers, researchers, public interest tech-builders, and strategists are at a key moment.

We are several years into the LLM era now — past the first wave of surprise, demos, and hype — and into the phase where real institutions are deciding what to do with these tools. People are already using AI to solve problems in their everyday lives, including legal problems, whether courts and legal aid organizations are ready or not. That means the “AI moment” is no longer hypothetical: it’s shaping expectations, workflows, and trust right now. But still many justice leaders are confused, overwhelmed, or unsure about how to get to positive impact in this new AI era.

This is exactly why an AI+A2J Summit like this matters. We’re at a pivot point where the field can either coordinate and build durable public-interest infrastructure — or fragment into disconnected experiments that don’t translate into meaningful service capacity. A2J leaders are balancing urgency with caution, and the choices made in the next year or two will set patterns that could last a decade: what gets adopted, what gets regulated, what gets trusted, and what gets abandoned.

What will 2030 look like for A2J?

Which of these possible near futures will we have in 2030 for access to justice?

A robust, accessible marketplace of services — where everyone having a problem with their landlord, debt collector, spouse, employer, neighbor, or government can easily get the help they need in the form they want?

Or will we have a hugely underserved public, that’s frustrated and angry, facing an ever-growing asymmetry of robo-filed lawsuits and relying on low-quality AI help?

What is stopping great innovation imapact?

Some of the key things that could stop our community from delivering great outcomes in the next five years include a few big trends:

• too much chilling regulation,
• under-performing and -safety tested solutions that lead to bad harms and headlines,
• not enough money flowing to get to solutions, everyone reinventing the wheel on their own and deliverting fragile and costly local solutions, and
• a lack of a building substantive, meaninful solutions — instead focusing on small, peripheral tasks.

The primary barriers are not just technical — they’re operational, institutional, and human. Legal organizations need tools that are reliable enough to use with real people, real deadlines, and real consequences. But today, many pilots struggle with consistency, integration into daily workflows, and the basic “plumbing” that makes technology usable at scale: identity management, knowledge management, access controls, and clear accountability when something goes wrong.

Trust is also fragile in high-stakes settings, and the cost of a failure is unusually high. A single under-tested tool can create public harm, undermine confidence internally, and trigger an overcorrection that chills innovation. In parallel, many organizations are already stretched thin and running on complex legacy systems. Without shared standards, shared evaluation, and shared implementation support, the burden of “doing AI responsibly” becomes too heavy for individual teams to carry alone.

At the Summit, we worked on 3 different strategy levels to try to prevent these blocks from pushing us to low impact or a continued status quo.

3 Levels of Strategic Work to Set us towards a Good Ecosystem

The goal of the Summit was to get leaders from across the A2J world to clearly define 3 levels of strategy. That means going beyond the usual strategic track — which is just defining the policies and tech agenda for their internal organization.

This meant focusing on both project mode (what are cool ideas and use cases) and also strategy mode — so we can shape where this goes, rather than react to whatever the market and technology delivers. We’re convening people who are already experimenting with AI in courts, legal aid, libraries, and community justice organizations, and we’re asking them to step back and make intentional choices about what they will build, buy, govern, and measure over the next 12–24 months. The point is to move from isolated pilots to durable capacity: tools that can be trusted, maintained, and integrated into real workflows, with clear guardrails for privacy, security, and quality.

To do that, the Summit is designed to push work at three linked levels of strategy.

Strategy Level 1: Internal Org Strategy around AI

First is internal, organizational strategy: what each institution needs to do internally — data governance, procurement standards, evaluation protocols, staff training, change management, and the operational “plumbing” that makes AI usable and safe.

Strategy 2: Ecosystem Strategy

Second is ecosystem strategy, that covers how different A2J organizations can collaborate to increase capacity and impact.

This can scope out what we should build together — shared playbooks, common evaluation and certification approaches, interoperable data and knowledge standards, and shared infrastructure that prevents every jurisdiction from reinventing fragile, costly solutions.

Strategy 3: Towards Big Tech & A2J

Third is strategy vis-à-vis big tech: how the justice community can engage major AI platform providers with clear expectations and leverage — so the next wave of product decisions, safety defaults, partnerships, and pricing structures actually support access to justice rather than widen gaps.

The Summit is ultimately about making a coordinated, public-interest plan now — so that by 2030 we have a legal help ecosystem that is more trustworthy, more usable, more interoperable, and able to serve far more people with far less friction.

The Modern A2J Toolbox: A Growing set of AI-Powered Solutions

Equipping justice professionals with the right technology is a cornerstone of modernizing access to justice. The Summit provided a tour of AI tools available to the community, ranging from comprehensive legal platforms designed for large-scale litigation to custom-built solutions tailored for specific legal aid workflows. This tour of the growing AI toolbox revealed an expanding arsenal of capabilities designed to augment legal work, streamline processes, and extend the reach of legal services.

Research & Case Management Assistants

Teams from many different AI and legal tech teams presented their solutions and explained how they can be used to expand access to justice.

• Notebook LM: The Notebook LM tool from Google empowers users to create intelligent digital notebooks from their case files and documents. Its capabilities have been significantly enhanced, featuring an expanded context window of up to 1 million tokens, allowing it to digest and analyze vast amounts of information. The platform is fully multilingual, supporting over 100 languages for both queries and content generation. This enables it to generate a wide range of work products, from infographics and slide decks to narrated video overviews, making it a versatile tool for both internal analysis and client communication.
• Harvey: Harvey is an AI platform built specifically for legal professionals, structured around three core components. The Assistant functions as a conversational interface for asking complex legal questions based on uploaded files and integrated research sources like LexisNexis. The Vault serves as a secure repository for case documents, enabling deep analysis across up to 10,000 different documents at once. Finally, Workflows provide one-click solutions for common, repeatable tasks like building case timelines or translating documents, with the ability for organizations to create and embed their own custom playbooks.
• Thomson Reuters’ CoCounsel: CoCounsel is designed to leverage an organization’s complete universe of information — from its own internal data and knowledge management systems to the primary law available through Westlaw. This comprehensive integration allows it to automate and assist with tasks across the entire client representation lifecycle, from initial intake and case assessment to legal research and discovery preparation. The platform is built to function like a human colleague, capable of pulling together disparate information sources to efficiently construct the building blocks of legal practice. TR also has an AI for Justice program that leverages CoCounsel and its team to help legal aid organizations.
• VLex’s Vincent AI: Vincent AI adopts a workflow-based approach to legal tasks, offering dedicated modules for legal research, contract analysis, complaint review, and large-scale document review. Its design is particularly user-friendly for those with “prompting anxiety,” as it can automatically analyze an uploaded document (such as a lease or complaint) and suggest relevant next steps and analyses. A key feature is its ability to process not just text but also audio and video content, opening up powerful applications for tasks like analyzing client intake calls or video interviews to rapidly identify key issues.

AI on Case Management & E-Discovery Platforms

• Legal Server: As a long-standing case management system, Legal Server has introduced an AI assistant named “Ellis.” The platform’s core approach to AI is rooted in data privacy and relevance. Rather than drawing on the open internet, Ellis is trained exclusively on an individual client organization’s own isolated data repository, including its help documentation, case notes, and internal documents. This ensures that answers are grounded in the organization’s specific context and expertise while maintaining strict client confidentiality.
• Relativity: Relativity’s e-discovery platform is made available to justice-focused organizations through its “Justice for Change” program. The platform includes powerful generative AI features like AIR for Review, which can analyze hundreds of thousands of documents to identify key people, terms, and events in an investigation. It also features integrated translation tools that support over 100 languages, including right-to-left languages like Hebrew, allowing legal teams to seamlessly work with multilingual case documents within a single, secure environment.

These tools represent a leap in technological capability. They all show the growing ability for AI to help legal teams synthesize info, work with documents, conduct research, produce key work product, and automate workflows. But how do we go from tech tools to real-world impact, solutions that are deployed at scale and get to high performance numbers? The Summit moved from tech demos to case studies to get to accounts of how to get to value and impact.

From Pilots to Impact: AI in Action Across the Justice Sector

In the second half of Day 1, the Summit moved beyond product demonstrations to showcase a series of compelling case studies from across the justice sector. These presentations offered proof points of how organizations are already leveraging AI to serve more people, improve service quality, and create new efficiencies, delivering concrete value to their clients and communities today.

• Legal Aid Society of Middle Tennessee & The Cumberlands — Automating Expungement Petitions: The “ExpungeMate” project was created to tackle the manual, time-consuming process of reviewing criminal records and preparing expungement petitions. By building a custom GPT to analyze records and an automated workflow to generate the necessary legal forms, the organization dramatically transformed its expungement clinics. At a single event, their output surged from 70 expungements to 751. This newfound efficiency freed up attorneys to provide holistic advice and enabled a more comprehensive service model that brought judges, district attorneys, and clerks on-site to reinstate driver’s licenses and waive court debt in real-time.
• Citizens Advice (UK) — Empowering Advisors with Caddy: Citizens Advice developed Caddy (Citizens Advice Digital Assistant), an internal chatbot designed to support its network of advisors, particularly new trainees. Caddy uses a Retrieval-Augmented Generation (RAG), a method that grounds the AI’s answers in a private, trusted knowledge base to ensure accuracy and prevent hallucination. A key feature is its “human-in-the-loop” workflow, where supervisors can quickly validate answers before they are given to clients. A six-week trial demonstrated significant impact, with the evaluation found that Caddy halved the response time for advisors seeking supervisory support, unlocking capacity to help thousands more people.
• Frontline Justice — Supercharging Community Justice Workers To support its network of non-lawyer “justice workers” in Alaska, Frontline Justice deployed an AI tool designed not just as a Q&A bot, but as a peer-to-peer knowledge hub. While the AI provides initial, reliable answers to legal questions, the system empowers senior justice workers to review, edit, and enrich these answers with practical, on-the-ground knowledge like local phone numbers or helpful infographics. This creates a dynamic, collaborative knowledge base where the expertise of one experienced worker in a remote village can be instantly shared with over 200 volunteers across the state.
• Lone Star Legal Aid — Building a Secure Chatbot Ecosystem Lone Star Legal Aid embarked on an ambitious in-house project to build three distinct chatbots on a secure RAG architecture to serve different user groups. One internal bot, LSLAsks, is for administrative information in their legal aid group. Their internal bot for legal staff, Juris, was designed to centralize legal knowledge and defeat the administrative burden of research. A core part of their strategy involved rigorous A/B testing of four different search models (cleverly named after the Ninja Turtles) to meticulously measure accuracy, relevancy, and speed, with the ultimate goal of eliminating hallucinations and building user trust in the system.
• People’s Law School (British Columbia) — Ensuring Quality in Public-Facing AI The team behind the public-facing Beagle+ chatbot shared their journey of ensuring high-quality, reliable answers for the public. Their development process involved intensive pre- and post-launch evaluation. Before launch, they used a 42-question dataset of real-world legal questions to test different models and prompts until they achieved 99% accuracy. After launch, a team of lawyers reviewed every single one of the first 5,400 conversations to score them for safety and value, using the findings to continuously refine the system and maintain its high standard of quality.

These successful implementations offered more than just inspiration; they surfaced a series of critical strategic debates that the entire access to justice community must now navigate.

Lessons Learned and Practical Strategies from the First Generation of AI+A2J Work

A consistent “lesson learned” from Day 1 was that legal aid AI only works when it’s treated as mission infrastructure, not as a cool add-on. Leaders emphasized values as practical guardrails: put people first (staff + clients), keep the main thing the main thing (serving clients), and plan for the long term — especially because large legal aid organizations are “big ships” that can’t pivot overnight.

Smart choice of projects: In practice, that means choosing projects that reduce friction in frontline work, don’t distract from service delivery, and can be sustained after the initial burst of experimentation.

An ecosystem of specific solutions: On the build side, teams stressed scoping and architecture choices that intentionally reduce risk. One practical pattern was a “one tool = one problem” approach, with different bots for different users and workflows (internal legal research, internal admin FAQs, and client-facing triage) rather than trying to make a single chatbot do everything.

Building for Security & Privacy forward solutions: Security and privacy were treated as design requirements, not compliance afterthoughts — e.g., selecting an enterprise cloud environment already inside the organization’s security perimeter and choosing retrieval-augmented generation (RAG) to keep answers grounded in verified sources.

Keeping Knowledge Fresh: Teams also described curating the knowledge base (black-letter law + SME guidance) and setting a maintenance cadence so the sources stay trustworthy over time.

Figure out What You’re Measuring & How: On evaluation, Day 1 emphasized that “accuracy” isn’t a vibe — you have to measure it, iterate, and keep monitoring after launch. Practical approaches included: (1) building a small but meaningful test set from real questions, (2) defining what an “ideal answer” must include, and (3) scoring outputs on safety and value across model/prompt/RAG variations.

Teams also used internal testing with non-developer legal staff to ask real workflow questions, paired with lightweight feedback mechanisms (thumbs up/down + reason codes) and operational metrics like citations used, speed, and cost per question. A key implementation insight was that some “AI errors” are actually content errors — post-launch quality improved by fixing source content (even single missing words) and tightening prompts, supported by ongoing monitoring.

Be Ready with Policies & Governance: On deployment governance, teams highlighted a bias toward containment, transparency, and safe failure modes. One practical RAG pattern: show citations down to the page/section, display the excerpt used, and if the system can’t answer from the verified corpus, it should say so — explicitly.

There was also a clear warning about emerging security risks (especially prompt injection and attack surfaces when tools start browsing or pulling from the open internet) and the need to think about cybersecurity as capability scales from pilots to broader use. Teams described practical access controls (like 2FA) and “shareable internal agents” as ways to grow adoption without losing governance.

Be Ready for Data Access Blocks: Several Day 1 discussions surfaced the external blockers that legal aid teams can’t solve alone — especially data access and interoperability with courts and other systems.

Even when internal workflows are ready, teams run into constraints like restrictions on scraping or fragmented, jurisdiction-specific data practices, which makes replication harder and increases costs for every new deployment. That’s one reason the “lessons learned” kept circling back to shared infrastructure: common patterns for grounded knowledge, testing protocols, security hardening, and the data pathways needed to make these tools reliable in day-to-day legal work.

Strategic Crossroads: Key Debates Shaping the Future of the AI+A2J Ecosystem

The proliferation of AI has brought the access to justice community to a strategic crossroads. The Summit revealed that organizations are grappling with fundamental decisions about how to acquire, build, and deploy this technology. The choices made in the coming years will define the technological landscape of the sector, determining the cost, accessibility, and control that legal aid organizations have over their digital futures.

The Build vs. Buy Dilemma

A central tension emerged between building custom solutions and purchasing sophisticated off-the-shelf platforms. We might end up with a ‘yes and’ approach, that involves both.

The Case for Building:

Organizations like Maryland Legal Aid and Lone Star Legal Aid are pursuing an in-house development path. This is not just a cost-and-security decision but a strategic choice about building organizational capacity.

The primary drivers are significantly lower long-term costs — Maryland Legal Aid reported running their custom platform for their entire staff for less than $100 per month — and enhanced data security and privacy, achieved through direct control over the tech stack and zero-data-retention agreements with API providers.

Building allows for the precise tailoring of tools to unique organizational workflows and empowers staff to become creators.

The Case for Buying:

Conversely, presentations from Relativity, Harvey, Thomson Reuters, vLex/Clio, and others showcased the immense power of professionally developed, pre-built platforms. The argument for buying centers on leveraging cutting-edge technology and complex features without the significant upfront investment in hiring and maintaining an in-house development team.

This path offers immediate access to powerful tools for organizations that lack the capacity or desire to become software developers themselves.

Centralized Expertise vs. Empowered End-Users

A parallel debate surfaced around who should be building AI applications. The traditional model, exemplified by Lone Star Legal Aid, involves a specialized technical team that designs and develops tools for the rest of the organization.

In contrast, Maryland Legal Aid presented a more democratized vision, empowering tech-curious attorneys and paralegals to engage in “vibe coding.”

This approach envisions non-technical staff becoming software creators themselves, using new, user-friendly AI development tools to rapidly build and deploy solutions. It transforms end-users into innovators, allowing legal aid organizations to “start solving their own problems” fast, cheaply, and in-house.

Navigating the Role of Big Tech in Justice Services

The summit highlighted the inescapable and growing role of major technology companies in the justice space. The debate here centers on the nature of the engagement.

One path involves close collaboration, such as licensing tools like Notebook LM from Google or leveraging APIs from OpenAI to power custom applications.

The alternative is a more cautious approach that prioritizes advocacy for regulation, taxation and licensing legal orgs’ knowledge and tools, and the implementation of robust public interest protections to ensure that the deployment of large-scale AI serves, rather than harms, the public good.

These strategic debates are shaping the immediate future of legal technology, but the summit also issued a more profound challenge: to use this moment not just to optimize existing processes, but to reimagine the very foundations of justice itself.

AI Beyond Automation: Reimagining the Fundamentals of the Justice System

The conversation at the summit elevated from simply making the existing justice system more efficient to fundamentally transforming it for a new era.

In a thought-provoking remote address, Professor Richard Susskind challenged attendees to look beyond the immediate applications of AI and consider how it could reshape the core principles of dispute resolution and legal help. This forward-looking perspective urged the community to avoid merely automating the past and instead use technology to design a more accessible, preventative, and outcome-focused system of justice.

The Automation Fallacy

Susskind warned against what he termed “technological myopia” — the tendency to view new technology only through the lens of automating existing tasks. He argued that simply replacing human lawyers with AI to perform the same work is an uninspired goal. Using a powerful analogy, he urged the legal community to avoid focusing on the equivalent of “robotic surgery” (perfecting an old process) and instead seek out the legal equivalents of “non-invasive therapy” and “preventative medicine” — entirely new, more effective ways to achieve just outcomes.

Focusing Upstream

This call to action was echoed in a broader directive to shift focus from downstream dispute resolution to upstream interventions. The goal is to leverage technology and data not just to manage conflicts once they arise, but to prevent them from escalating in the first place. This concept was vividly captured by Susskind’s metaphor of a society that is better served by “putting a fence at the top of the cliff rather than an ambulance at the bottom.”

The Future of Dispute Resolution

Susskind posed the provocative question, “Can AI replace judges?” but quickly reframed it to be more productive. Instead of asking if a machine can replicate a human judge, he argued the focus should be on outcomes: can AI systems generate reliable legal determinations with reasons?

He envisioned a future, perhaps by 2030, where citizens might prefer state-supported, AI-underpinned dispute services over traditional courts. In this vision, parties could submit their evidence and arguments to a “comfortingly branded” AI system that could cheaply, cheerfully, and immediately deliver a conclusion, transforming the speed and accessibility of justice.

Achieving such ambitious, long-term visions requires more than just technological breakthroughs; it demands the creation of a practical, collaborative infrastructure to build and sustain this new future.

Building Funding and Capacity for this Work

On the panel about building a National AI + A2J ecosystem, panelists discussed how to increase capacity and impact in this space.

The Need to Make this Space Legible as a Market

The panel framed the “economics” conversation as a market-making challenge: if we want new tech to actually scale in access to justice, we have to make the space legible — not just inspiring. There could be a clearer market for navigation tech in low-income “fork-in-the-road” moments. The panel highlighted that the nascent ecosystem needs three things to become investable and durable:

• clearly defined problems,
• shared infrastructure that makes building and scaling easier, and
• business models that sustain products over time.

A key through-line in the panel’s commentary was: we can’t pretend grant funding alone will carry the next decade of AI+A2J delivery. Panelists suggested we need experimentation to find new payers — for example, employer-funded benefits and EAP dollars, or insurer/health-adjacent funding tied to social determinants of health — paired with stronger evidence that tools improve outcomes. This is connected to the need for shared benchmarks and evaluation methods that can influence how developers build and how funders (and institutions) decide what to back.

A Warning Not to Build New Tech on Bad Processes

The panel also brought a grounding reality check: even the best tech will underperform — or do harm — if it’s layered onto broken processes. Tech projects where tech sat on top of high-default systems contributed to worse outcomes.

The economic implication was clear: funders and institutions should pay for process repair and procedural barrier removal as seriously as they pay for new tools, because the ROI of AI depends on the underlying system actually functioning.

The Role of Impact Investing as a new source of capital

Building this ecosystem requires a new approach to funding. Kate Fazio framed the justice gap as a fundamental “market failure” in the realm of “people law” — the everyday legal problems faced by individuals. She argued that the two traditional sources of capital are insufficient to solve this failure: traditional venture capital is misaligned, seeking massive returns that “people law” cannot generate, while philanthropy is vital but chronically resource-constrained.

The missing piece, Fazio argued, is impact investing: a form of patient, flexible capital that seeks to generate both a measurable social impact and a financial return. This provides a crucial middle ground for funding sustainable, scalable models that may not offer explosive growth but can create enormous social value. But she highlighted a stark reality: of the 17 UN Sustainable Development Goals, Goal 16 (Peace, Justice, and Strong Institutions) currently receives almost no impact investment capital. This presents both a monumental challenge and a massive opportunity for the A2J community to articulate its value and attract a new, powerful source of funding to build the future of justice.

This talk of new capital, market-making, and funding strategies started to point the group to a clear strategic imperative. To overcome the risk of fragmented pilots and siloed innovation, the A2J community must start coalescing into a coherent ecosystem. This means embracing collaborative infrastructure, which can be hand-in-hand with attracting new forms of capital.

By reframing the “market failure” in people law as a generational opportunity for impact investing, the sector can secure the sustainable funding needed to scale the transformative, preventative, and outcome-focused systems of justice envisioned throughout the summit.

Forging an AI+A2J Ecosystem: The Path to Sustainable Scale and Impact

On Day 2, we challenged groups to envision how to build a strong AI and A2J development, evaluation, and market ecosystem. They came up with so many ideas, and we try to capture them below. Much of it is about having common infrastructure, shared capacity, and better ways to strengthen and share organic DIY AI tools.

A significant risk facing the A2J community is fragmentation, a scenario where “a thousand pilots bloom” but ultimately fail to create lasting, widespread change because efforts are siloed and unsustainable. The summit issued a clear call to counter this risk by adopting a collaborative ecosystem approach.

The working groups on Day 2 highlighted some of the key things that our community can work on, to build a stronger and more successful A2J provider ecosystem. This infrastructure-centered strategy emphasizes sharing knowledge, resources, and infrastructure to ensure that innovations are not only successful in isolation but can be sustained, scaled, and adapted across the entire sector.

Throughout the summit, presenters and participants highlighted the essential capacities and infrastructure that individual organizations must develop to succeed with AI. Building these capabilities in every single organization is inefficient and unrealistic. An ecosystem approach recognizes the need for shared infrastructure, including the playbooks, knowledge/data standards, privacy and security tooling, evaluation and certification, and more.

Replicable Playbooks to Prevent Parallel Duplication

Many groups in the Summit called for replicable solutions playbooks, that go beyond sharing repositories on Github and making conference presentations, and getting to the teams and resources that can help more legal teams replicate successful AI solutions and localize them to their jurisdiction and organization.

A2J organizations don’t just need inspiration — they need proven patterns they can adopt with confidence. Replicable “how-tos” turn isolated success stories into field-level capability: how to scope a use case, how to choose a model approach, how to design a safe workflow, how to test and monitor performance, and how to roll out tools to staff without creating chaos. These playbooks reduce the cost of learning, lower risk, and help organizations move from pilots to sustained operations.

Replicable guidance also helps prevent duplication. Right now, too many teams are solving the same early-stage problems in parallel: procurement questions, privacy questions, evaluation questions, prompt and retrieval design, and governance questions. If the field can agree on shared building blocks and publish them in usable formats, innovation becomes cumulative — each new project building on the last instead of starting over.

A Common Agenda of What Tasks-Issues to Build Solutions for

Without a shared agenda, the field risks drifting into fragmentation: dozens of pilots, dozens of platforms, and no cumulative progress. A common agenda does not mean one centralized solution — it means alignment on what must be built together, what must be measured, and what must be stewarded over time. It creates shared language, shared priorities, and shared accountability across courts, legal aid, community organizations, researchers, funders, and vendors.

This is the core reason the Legal Design Lab held the Summit: to convene the people who can shape that shared agenda and to produce a practical roadmap that others can adopt. The goal is to protect this moment from predictable failure modes — over-chill, backlash, duplication, and under-maintained tools — and instead create an ecosystem where responsible innovation compounds, trust grows, and more people get real legal help when they need it.

Evaluation Protocols and Certifications

Groups also called for more, easier evaluation and certification. They want high-quality, standardized methods for evaluation, testing, and long-term maintenance.

In high-stakes legal settings, “seems good” is not good enough. The field needs clear definitions of quality and safety, and credible evaluation protocols that different organizations can use consistently. This doesn’t mean one rigid standard for every tool — but it does mean shared expectations: what must be tested, what must be logged, what harms must be monitored, and what “good enough” looks like for different risk levels.

Certification — or at least standard conformance levels — can also shift the market. If courts and legal aid groups can point to transparent evaluation and safety practices, then vendors and internal builders alike have a clear target. That reduces fear-driven overreaction and replaces it with evidence-driven decision-making. Over time, it supports responsible procurement, encourages better products, and protects the public by making safety and accountability visible.

In addition, creating legal benchmarks for the most common & significant legal tasks can push LLM developers to improve their foundational models for justice use cases

Practical, Clear Privacy Protections

A block for many of the possible solutions is the safe use of AI with highly confidential, risky data. Privacy is not a footnote in A2J — it is the precondition for using AI with real people. Many of the highest-value workflows involve sensitive information: housing instability, family safety, immigration status, disability, finances, or criminal history. If legal teams cannot confidently protect client data, they will either avoid the tools entirely or use them in risky ways that expose clients and organizations to harm.

What is needed is privacy-by-design infrastructure: clear rules for data handling, retention, and access; secure deployment patterns; strong vendor contract terms; and practical training for staff about what can and cannot be used in which tools. The Summit is a place to align on what “acceptable privacy posture” should look like across the ecosystem — so privacy does not become an innovation-killer, and innovation does not become a privacy risk.

More cybersecurity, testing, reliability engineering, and ongoing monitoring

Along with privacy risks, participants noted that many of the organic, DIY solutions are not prepared for cybersecurity risks. As AI tools become embedded in legal workflows, they become targets — both for accidental failures and deliberate attacks. Prompt injection, data leakage, insecure integrations, and overbroad permissions can turn a helpful tool into a security incident. And reliability matters just as much as brilliance: a tool that works 80% of the time may still be unusable in high-stakes practice if the failures are unpredictable.

The field needs a stronger norm of “safety engineering”: threat modeling, red-teaming, testing protocols, incident response plans, and ongoing monitoring after deployment. This is also where shared infrastructure helps most. Individual organizations should not each have to invent cybersecurity practices for AI from scratch. A common set of testing and security baselines would let innovators move faster while reducing systemic risk.

Inter-Agency/Court Data Connections

Many groups need to call up and work with data from other agencies — like court docket files and records, other legal aid groups’ data, and more — in order to get highly effective, AI-powered workflows

Participants called for more standards and data contracts that can facilitate systematic data access, collection, and preparation. Many of the biggest A2J bottlenecks are not about “knowing the law” — they’re about navigating fragmented systems. People have to repeat their story across multiple offices, programs, and portals. Providers can’t see what happened earlier in the journey. Courts don’t receive information in consistent, structured ways. The result is duplication, delay, and drop-off — exactly where AI could help, but only if the data ecosystem supports it.

Many of the biggest A2J bottlenecks are not about “knowing the law” — they’re about navigating fragmented systems. People have to repeat their story across multiple offices, programs, and portals. Providers can’t see what happened earlier in the journey. Courts don’t receive information in consistent, structured ways. The result is duplication, delay, and drop-off — exactly where AI could help, but only if the data ecosystem supports it.

Data Contracts for Interoperable Knowledge Bases

Many local innovators are starting to build out structured, authoritative knowledge on court procedure, forms and documents, strategies, legal authorities, service directories, and more. This knowledge data is built to power their local legal AI solutions, but right now it is stored and saved in unique local ways.

This investment in local authoritative legal knowledge bases makes sense. LLMs are powerful, but they are not a substitute for authoritative, maintainable legal knowledge. The most dependable AI systems in legal help will be grounded in structured knowledge: jurisdiction-specific procedures, deadlines, forms, filing rules, court locations, service directories, eligibility rules, and “what happens next” pathways.

But the worry among participants is that all of these highly localized knowledge bases will be one-off for a specific org or solution. Ideally, when teams are investing in building these local knowledge bases, it can follow some key standard rules so it can perform well and it can be updated, audited, and reused across tools and regions.

This is why knowledge bases and data exchanges are central to the ecosystem approach. Instead of each organization maintaining its own isolated universe of content, we can build shared registries and common schemas that allow local control while enabling cross-jurisdiction learning and reuse. The aim is not uniformity for its own sake — it’s reliability, maintainability, and the ability to scale help without scaling confusion.

More training and change management so legal teams are ready

Even the best tools fail if people don’t adopt them in real workflows. Legal organizations are human systems with deeply embedded habits, risk cultures, and informal processes. Training and change management are not “nice to have” — they determine whether AI becomes a daily capability or a novelty used by a handful of early adopters.

What’s needed is practical, role-based readiness support: training for leadership on governance and procurement, training for frontline staff on safe use and workflow integration, and support for managers who must redesign processes and measure outcomes. The Summit is a step toward building a shared approach to readiness — so the field can absorb change without burnout, fragmentation, or loss of trust.

Building Capability & Lowering Costs of Development/Use

One of the biggest barriers to AI-A2J impact is that the “real” version of these tools — secure deployments, quality evaluation, integration into existing systems, and sustained maintenance — can be unaffordable when each court or legal aid organization tries to do it alone. The result is a familiar pattern: a few well-resourced organizations build impressive pilots, while most teams remain stuck with limited access, short-term experiments, or tools that can’t safely touch real client data.

Coordination is the way out of this trap. When the field aligns on shared priorities and shared building blocks, we reduce duplication and shift spending away from reinventing the same foundational components toward improving what actually matters for service delivery.

Through coordination, the ecosystem can also change the economics of AI itself. Shared evaluation protocols, reference architectures, and standard data contracts mean vendors and platform providers can build once and serve many — lowering per-organization cost and making procurement less risky. Collective demand can also create better terms: pooled negotiation for pricing, clearer requirements for privacy/security, and shared expectations about model behavior and transparency.

Just as importantly, coordinated open infrastructure — structured knowledge bases, service directories, and interoperable intake/referral data — reduces reliance on expensive bespoke systems by making high-value components reusable across jurisdictions.

The goal is not uniformity, but a commons: a set of shared standards and assets that makes safe, high-quality AI deployment feasible for the median organization, not just the best-funded one.

Conclusion

The AI + Access to Justice Summit is designed as a yearly convening point — because this work can’t be finished in a single event. Each year, we’ll take stock of what’s changing in the technology, what’s working on the ground in courts and legal aid, and where the biggest gaps remain. More importantly, we’ll use the Summit to move from discussion to shared commitments: clearer priorities, stronger relationships across the ecosystem, and concrete next steps that participants can carry back into their organizations and collaborations.

We are also building the Summit as a launchpad for follow-through. In the months after convening, we will work with participants to continue progress on common infrastructure: evaluation and safety protocols, privacy and security patterns, interoperable knowledge and data standards, and practical implementation playbooks that make adoption feasible across diverse jurisdictions. The aim is to make innovation cumulative — so promising work does not remain isolated in a single pilot site, but becomes reusable and improvable across the field.

We are deeply grateful to the sponsors who made this convening possible, and to the speakers who shared lessons, hard-won insights, and real examples from the frontlines.

Most of all, thank you to the participants — justice leaders, technologists, researchers, funders, and community partners — who showed up ready to collaborate, challenge assumptions, and build something larger than any single organization can create alone. Your energy and seriousness are exactly what this moment demands, and we’re excited to keep working together toward a better 2030.