How Law Firms Can Use AI to Reshape Legal Practice: The Gilbert + Tobin Experience

Australia's leading law firm Gilbert + Tobin is attempting something rather unusual: treating AI as a fulcrum for redefining its legal business model, not merely as a small tool for improving document efficiency. Sam Nickless, the firm's partner and CEO, shared the firm's thinking on AI in a conversation.

From Tools to Restructuring Business Logic

Most law firms start introducing AI with efficiency in mind: searching case law, drafting contracts, organizing due diligence materials. But Gilbert + Tobin's angle is more fundamental—it first asks whether "the way legal services are delivered itself needs to change." The fact that Nickless, as the firm's CEO, is participating in this discussion shows the issue has moved beyond the IT department and into the realm of business decision-making.

The underlying judgment is this: if AI is only used to help lawyers do the same things faster, the firm gains only marginal cost reductions; but if AI changes the path by which clients obtain legal services and the way those services are priced, the firm faces a restructuring of its business model. The former is optimization; the latter is transformation.

Why the Law Firm CEO Is Personally Discussing AI

That the CEO rather than the CTO is speaking about AI is itself a signal. In partnership law firms, driving cross-departmental technological change is often constrained by partners' independence and short-term billing pressure. CEO-level involvement means AI is being treated as a firm-level strategic issue requiring coordination of resource allocation, talent development, and client communication.

This has reference value for the industry: the bottleneck in implementing legal AI is rarely model capability—it is more often organizational structure, incentive systems, and client acceptance. Whoever can settle these issues at the governance level is the one who can convert technological advantage into business advantage.

Partnership law firms differ significantly from traditional companies in governance structure: the firm is jointly owned by equity partners, major decisions typically require approval by a partners' meeting or management committee, and each partner has considerable autonomy over their business team. This "federal-style" structure makes top-down technological change difficult to advance—any measure affecting billing habits, workflows, or profit distribution may encounter resistance from individual partners. The role of the CEO (or managing partner) is therefore particularly critical: their core function is not to write code or select models, but to coordinate resource investment, design incentive mechanisms, and manage resistance to change. When the AI issue rises to the CEO level, it means it is regarded as a strategic matter requiring cross-departmental coordination, long-term investment, and redistribution of interests—not a marginal project that some team can experiment with on its own. This also explains why the bottleneck in implementing legal AI is often not on the technology side but on the governance side.

Implications for the Legal Industry

The legal services industry has long centered on billable hours, and the efficiency gains brought by AI will directly strike at the internal logic of this model—the faster you work, the less revenue you may earn. By focusing the discussion on "business" rather than "tools," Gilbert + Tobin has precisely touched this contradiction.

Based on available information, this conversation is more of a directional statement than a disclosure of specific products and process details. For those following legal tech, what is worth continued tracking is: how such firms will adjust pricing structures, how they will define the boundary between AI output and lawyer responsibility, and how they will explain the value of services involving AI to clients. The answers to these questions will determine the actual trajectory of AI in the legal industry more than the capabilities of any single-point tool.

The billable hour system is the core pricing model that the legal services industry has used for decades: lawyers bill by the time actually invested in work, and clients pay for "time" rather than "results." The inherent incentive of this model is that the more complex the case and the longer it takes, the higher the firm's revenue. The efficiency gains brought by AI run precisely counter to this: due diligence and contract review that once required dozens of hours from junior lawyers can now have a first draft completed by AI in minutes, directly compressing billable hours. This is the root of the contradiction mentioned in the article—"the faster you work, the less revenue you may earn." The industry has already explored alternatives such as fixed fees, subscriptions, and value-based pricing, but the difficulty of transformation lies in: partners' income distribution, clients' perception of "why pay for AI output," and how to measure service value with AI assistance. Adjusting the billing model is essentially a restructuring of law firm governance and interest distribution, not simply changing a price list.

One-line Summary

Australian law firm Gilbert + Tobin treats AI as a fulcrum for restructuring its legal business model rather than a mere efficiency tool.

Paragraph Summary

Australia's leading law firm Gilbert + Tobin is trying to use AI as a fulcrum for redefining its legal business model rather than merely as a tool to improve document efficiency. Partner and CEO Sam Nickless personally participates in the discussion, showing that the AI issue has risen from the IT department to the firm's business decision-making level. The article points out that if AI only lets lawyers do the same things faster, the firm gains only marginal cost reductions; if AI changes the path by which clients obtain legal services and the way services are priced, it triggers a business model restructuring. That the CEO rather than the CTO speaks out signals that AI is regarded as a firm-level strategic issue requiring coordination of resource allocation, talent development, and client communication. The article emphasizes that the bottleneck in implementing legal AI lies more in organizational structure, incentive mechanisms, and client acceptance than in model capability; there is an inherent tension between the billable hour model and AI-driven efficiency gains, which is the core contradiction in law firm transformation. Available information leans toward directional statements, and pricing structures and responsibility boundaries are worth continued tracking.