In yesterday’s session, Julia Klingberg, Moderator and founder of Legal Network of Sweden, was joined by Ke Ma, Co-founder and COO of Concordia, who outlined a non-linear path into legal tech. After studying French and Government, Ma began his career in publishing before pivoting to law school, followed by a stint at McKinsey. Dissatisfied with the perceived rigidity and lack of innovation in traditional legal careers, he explored alternative paths and eventually practised as a litigator at the Cook County State’s Attorney’s Office. There, he managed a high caseload and observed first-hand the inefficiencies and manual burden embedded in litigation workflows.
These experiences directly informed the founding of Concordia, an AI-native litigation company aiming to streamline legal processes and improve efficiency across the litigation lifecycle.
The Problem: Inefficiency in Litigation
Ma emphasised that litigation remains highly repetitive and operationally inefficient. Lawyers often spend significant time on manual, low-value tasks such as drafting standard documents, managing filings, and tracking deadlines. This is particularly acute in under-resourced environments, where attorneys lack adequate support staff and technology infrastructure.
The result is a misallocation of legal expertise: time that could be spent on strategy, client engagement, and advocacy is instead consumed by administrative work.
Concordia’s Approach: AI + Services
Concordia began as a software-first platform but evolved into a hybrid model combining software with legal service delivery. Clients initially requested not just tools, but also execution, leveraging the founders’ legal expertise to produce litigation outputs directly.
This dual model now represents a core strategic direction:
- Software layer: Automating workflows such as complaint analysis, document parsing, and drafting.
- Services layer: Delivering legal work (e.g. pleadings, responses) using AI-enhanced processes.
Ma highlighted that this approach aligns with broader industry trends where “services + software” creates stronger value and resilience, particularly as standalone SaaS becomes increasingly commoditised.
Example Workflow
A typical Concordia workflow begins with integration into litigation databases such as PACER, enabling automatic retrieval of case documents. These documents are then processed and parsed, with complaints broken down into individual allegations. The system generates structured responses to each allegation and exports fully formatted, court-ready documents.
This process significantly reduces the time spent on routine drafting while maintaining accuracy and consistency.
AI-Native Law Firm Model
Ma described an “AI-native law firm” not as a marketing label but as a practical operating philosophy. The approach starts with manual execution to gain a deep understanding of workflows. From there, repeatable processes are identified, incrementally automated and continuously optimised. Tools are refined based on real-world usage, creating an ongoing feedback loop between product development and service delivery.
Market Dynamics and Industry Shift
The discussion identified several structural shifts within the legal industry. Legal services are becoming increasingly commoditised as greater transparency and data begin to standardise pricing. Boutique firms are gaining ground by offering specialised services at lower cost, challenging the dominance of large law firms. At the same time, the push for efficiency is being driven more by clients than by lawyers themselves. This is also shaping talent demand, with professionals who combine legal and technical expertise expected to be particularly valuable.
Although litigation pricing remains opaque, Ma suggested that technology will gradually normalise expectations and reduce variability across the market.reduce variability.
Risks and Constraints
The primary risk to this emerging model lies in regulation. In the United States, law firms are generally required to be owned by lawyers, and state-level regulation creates fragmentation across jurisdictions. There is also the possibility of resistance from traditional structures that may seek to limit change.
However, these risks are balanced by a broader trend towards liberalisation and the democratisation of legal services, which could support the growth of AI-native models over time.
Long-Term Outlook
Looking ahead, Ma anticipates:
- Greater commoditisation and standardisation of litigation services
- Increased efficiency and reduced costs for clients
- Continued importance of the human layer, particularly in trust, judgement, and client relationships
Ultimately, he views legal tech as a mechanism to “move the needle” in an industry historically resistant to change, improving both outcomes and accessibility.
