AI Adoption in the Access to Justice Sector.
Artificial intelligence is often discussed in the legal sector as a discrete product: a chatbot, a drafting assistant, or a research engine.
But data from the access-to-justice ecosystem suggests something different.
In practice, AI adoption is rarely about a single tool. It is increasingly about the architecture of the platform on which legal services are built.
For Community Legal Centres and other front-line legal service providers there is already a wide technology gap. COVID shutdowns revealed uneven technological sophistication across the sector as some services continued to operate throughout lockdowns uninterupted, while others struggled to operate without adequate systems providing staff with remote access.
The technology sophistication gap is now widening further, as the choice of technology platforms has become determinative of access to integrated AI capabilities, and therefore the cost and quality of services that can be delivered.
This dynamic is becoming particularly visible amongst small law firms, legal aid providers, and the community legal sector, where margins and funding increases are thin and efficiency gains translate directly into improved access for clients.
Wallumatta Legal + Smokeball
A useful illustration is Wallumatta Legal, a new low-cost legal service operating in Australia.
Wallumatta serves the missing middle - people on low incomes who can’t afford to pay for legal advice. The financial model delivers high quality legal services at fixed fees which are 40-60% lower than the market. This has been acheived in part through successful deployment of powerful technology tools, many of which are integrated into the Smokeball case management platform.
Smokeball is not just a case management tool. It integrates a range of features that increasingly define the modern legal workflow:
AI-assisted document drafting
automated time capture and workflow management
pricing insights
data analytics and practice intelligence
document automation and precedent systems
client self-service portals, including file access and document review/editing
For a small or low-cost legal practice, adopting a platform like this effectively bundles AI capability into the operational infrastructure of the firm.
The legal service does not need to separately identify, procure, integrate, and maintain multiple AI tools. Instead, those capabilities arrive as part of the platform ecosystem.
For services focused on affordability, this model significantly reduces administrative overhead and increases lawyer productivity.
AI adoption is “stack-based”
Data emerging from the A2J-technology ecosystem reinforces this pattern.
Community legal centres and legal aid organisations rarely adopt a single “AI tool.” Instead, they assemble technology stacks that together form an end-to-end service delivery workflow.
Typical stacks include:
Operational systems
Case management platforms (e.g. CLASS, Actionstep, Salesforce, AIIM)
Productivity tools
AI-enabled productivity software (e.g. Microsoft 365 Copilot)
transcription and summarisation tools (e.g. Otter.ai)
Client-facing access tools
legal information portals (e.g. LawHelp.org)
guided interviews and document assembly (e.g. Legal Help Interactive, A2J Guided Interviews)
online dispute resolution systems (e.g. Modria or Tyler ODR)
Viewed together, these tools function as an integrated workflow rather than isolated technologies.
The most effective systems connect the entry point and vestibule (intake, triage, and guided self-help) to the back-end case management environment where lawyers manage matters.
Without that integration, self-help tools risk creating drop-offs where users begin a process but cannot complete it.
Where AI is delivering value
The data also suggests the most immediate impact of AI in community legal services is not automated legal advice.
Instead, AI is currently delivering the most value in internal productivity and administrative capacity, including:
drafting and summarising
transcription and meeting notes
document assembly
workflow automation
Tools like Microsoft 365 Copilot and transcription platforms reduce administrative burden and free up staff time.
For organisations operating under severe resource constraints, that shift can be transformative.
In this sense, AI is expanding access to justice indirectly by allowing legal services to help more people.
The most mature access-to-justice technology
Interestingly, the most successful technology in the access-to-justice ecosystem is not necessarily cutting-edge AI.
Some of the most effective systems are long-running civic platforms designed for self-represented litigants, including:
LawHelp.org (legal information and referrals)
Legal Help Interactive (form generation)
A2J Guided Interviews (step-by-step process navigation)
These tools rely on structured workflows and guided decision-making, rather than open-ended generative AI.
For vulnerable users, particularly those facing literacy, language, or mobility barriers, this design approach can be far more effective.
The overlooked driver of AI adoption
One implication of these patterns is that platform choice is increasingly determinative of AI adoption.
Case-management systems and productivity platforms are becoming AI delivery infrastructure.
Once an organisation commits to a particular platform ecosystem, its AI capabilities are largely determined by the integrations and features available within that system.
This has significant implications for the justice sector.
Over the past decade, many courts, legal aid bodies and law centres invested heavily in custom-built case management systems.
These systems were designed around specific operational needs, but will prove increasingly difficult to adapt to rapid advances in AI.
By contrast, institutions that purchased off-the-shelf or configurable platforms, such as Salesforce, have been able to benefit from AI capabilities integrated directly into the platform.
Salesforce, for example, has been embedding AI functionality into its ecosystem since the launch of Einstein AI in 2016, with more recent generative AI integrations expanding those capabilities further.
Over the next decade, organisations operating on supported platform ecosystems may therefore have a structural advantage, as AI capabilities can be introduced through platform updates rather than costly system redevelopment.
From AI tools to AI infrastructure
The broader lesson may be that AI in the justice system is evolving from a collection of tools into a form of infrastructure.
For commercial law firms, that infrastructure may include individually procured AI-tools customised and integrated into practice-management platforms.
For community legal services and low-cost providers, AI will typically appear as bundled functionality within case-management systems and productivity suites.
In both cases, the transformation occurs when AI becomes embedded in the everyday workflow of legal service delivery.
Access to justice has always been constrained by a simple reality: demand for free or affordable legal services outstrips supply. Technology will not eliminate that gap entirely. But AI-enabled platforms will allow legal services to operate more efficiently, expanding the capacity of the system.
The critical factor impacting on AI-adoption pace, impact and risk over the next decade may therefore be: the choice of platform , and whether the platform delivers integrated AI across productivity, operational and client facing workflow.