Over the past year the AI legaltech landscape has shifted in ways that are easy to miss at a surface level but obvious when speaking directly with research leaders, partners, librarians and innovation teams. Firms have moved beyond the early experimentation phase of generative AI and are now making more deliberate, informed decisions about the legal AI tools they adopt. This shift has been especially visible in the Australian market, where regulatory expectations, risk culture and jurisdictional nuance play a central role in the evaluation of legal research AI.
The first wave of adoption was driven by curiosity. Many firms trialled broad legal AI platforms that promised wide-ranging capabilities and impressive text generation. These tools helped lawyers understand what was possible and introduced natural language interfaces as a legitimate way to interact with information. But as pilots concluded and teams reflected on real workflows, many realised that general-purpose AI alone could not meet the demands of high quality legal research or analysis.
As of December 2025, we are entering a second wave of AI legaltech. In this wave, decision makers are more discerning. They are less impressed by generic AI outputs and far more interested in tools that demonstrate genuine domain expertise. Many have started upskilling themselves, not only in using AI but in understanding the architecture, data foundations and reasoning methods that underpin legal AI systems. As their knowledge grows, so does their ability to distinguish between polished surface features and deep legal capability.
Across conversations with Australian law firms and barristers, four themes consistently emerge.
Lawyers want jurisdiction-specific AI solutions that reflect the reasoning patterns and jurisprudential approaches of their courts. This is not simply a matter of training data volume. It involves a deeper understanding of precedent, statutory interpretation, procedural context and the way arguments are constructed. Generalised models struggle to capture this reliably, which is why Australian legaltech tools that focus on local law are gaining traction.
Legal work varies significantly between litigation, commercial advisory, insurance, regulatory compliance and other domains. Firms are increasingly favouring specialised legal intelligence that is purpose-built for a practice area or workflow. A generic assistant cannot easily replicate the lived structure of a litigator’s research process or a regulatory lawyer’s approach to horizon scanning. Tools that embed themselves naturally into these workflows have a clear advantage over broad alternatives.
The profession’s tolerance for opaque AI has diminished quickly. Firms expect legal research software to provide full traceability for every answer, including citations, document provenance and a clear connection to underlying sources. Without this, trust collapses. Legal teams are now comparing tools based on how easily they can verify each step of the reasoning process, which is shaping the adoption of high trust AI legal research tools and reducing the appeal of anything that obscures its methodology.
The most successful tools are those that integrate with document management systems, practice management software, knowledge repositories and established workflows. Firms want legal workflow automation that feels seamless, not another standalone surface. Tools that reduce friction are easier to adopt, easier to scale and easier to justify to internal stakeholders.
These themes illustrate a market that is growing more sophisticated. General-purpose AI will continue to play a role, particularly for drafting, idea generation and exploratory tasks. But the real growth and long-term adoption are shifting toward specialised AI for law firms. Tools that combine retrieval, verification, domain reasoning and legal method offer something that generic models simply cannot replicate. They create real value because they understand the practitioner’s world and because they provide reliable, traceable outputs.
This shift is one of the key reasons Habeas is receiving interest from firms that were previously more cautious. It signals that the market is ready for legal AI platforms that are jurisdiction-native, workflow-specific and designed with transparency at their core. It also reflects a maturing of the legal industry. Decision makers are no longer satisfied with AI that looks impressive in a demo but fails to hold up under real legal scrutiny. They want tools that actually solve problems at the level of detail legal work demands.
If the first wave of AI legaltech was about exploring the possible, this second wave is about choosing the tools that deliver reliable, high quality outcomes. Firms will continue to use general-purpose models, but they will increasingly complement them with dedicated legal document analysis and legal research AI systems that offer depth rather than breadth.
The companies that succeed in this next phase will be those who respect the complexity of legal work, understand the demands of each practice area and build tools that lawyers can trust. Precision, transparency and domain-level intelligence are becoming the new baseline. The market is no longer dazzled by AI. It is assessing whether the technology truly supports sound legal reasoning and improves the way firms work.
The next few years will belong to the teams that build with this in mind.
