In our session live from the IBA in Toronto, Moray McLaren, co-founder of Lexington Consultants, and Mari Cruz Taboada, Partner at Lexington Consultants, examined whether law firms are “sleepwalking” into an AI-driven future. The conversation moved beyond tool talk to focus on pricing, profitability, leverage, governance, and culture, asking what changes if AI changes the core of legal service delivery.

Inside the AI Shift

Taboada and McLaren set the scene with a simple observation: most firms approach AI as a technology acquisition, not a business model change. In their advisory work, they see the same pattern—pilots proliferate, but few firms translate early wins into redesigned pricing, talent models or knowledge infrastructure. The risk is a form of somnambulism: walking forward, eyes half-open, while the ground underneath is already shifting.

What Actually Changes in Law Firm Economics

Our Moderators focused on the compression effect, AI reduces time and manual effort across research, drafting, and reviewing. That has additional consequences:

• Pricing: Time-based billing becomes misaligned with value where months of work compress into days. Retainers, subscriptions and clearly defined “AI-assisted” packages are emerging responses.
• Profitability: Margin protection hinges on outcome-oriented pricing and disciplined scoping with clients. Without this, accelerated delivery simply shrinks revenue against largely fixed costs.
• Leverage: Traditional pyramids flatten as some junior tasks are absorbed by systems. New roles—legal engineers, data/ops specialists—enter the mix, while senior lawyers focus on judgement, negotiation and client leadership.

The Real Risk Factors

Technology risk is not the blocker most expect. The harder issues are organisational:

• Data and knowledge: AI gains are only as good as the firm’s document hygiene, taxonomy and interoperability. Siloed knowledge undermines scale.
• Governance and incentives: Partnership mechanics, credit allocation, origination, and lockstep can resist cross-practice platforms and shared investment. Misaligned KPIs encourage local optimisation over institutional value.
• Trust and disclosure: Clients want clarity on when and how AI is used, and what quality controls apply. Transparency is now part of risk management, not a marketing afterthought.

Adoption Reality

McLaren noted that firms often pilot broadly but institutionalise narrowly. Budget cycles, fragmented ownership of tech and conservative pricing habits slow momentum. In a higher-rate environment, leaders scrutinise ROI more closely, making focused, client-backed use cases more persuasive than sprawling experimentation.

Collaboration with Clients

Taboada emphasised that the most successful programmes are co-designed with clients. Joint pilots—agreeing on use cases, guardrails, and review criteria, create trust and produce tangible wins such as faster turnarounds, clearer scoping, and better predictability. This collaboration, in turn, supports new commercial models: retainers, subscription bundles, or matter phases explicitly priced for AI-assisted work.

Impact on Talent and Training

As AI absorbs repetitive tasks, apprenticeships must be re-engineered. Firms that thrive will:

• Reallocate junior time towards client context, judgment calls and communication.
• Introduce structured simulations and secondments to replace the lost “osmosis” from volume work.
• Hire and integrate legal engineers and product-minded professionals into matter teams, not just IT.

Operating Model and Infrastructure

Winning firms treat AI as a platform initiative, not a tool rollout, they ensure the following:

• Centralised knowledge with robust permissions and audit trails.
• Clear product ownership and change management across practices and offices.
• A cadence for evaluating models, prompts and workflows—with metrics tied to client value and profitability, not just usage.

Lessons Learned

From Our Moderators’ experience across multiple jurisdictions, different Law Firm sizes and business models, we can conclude that:

• Structure beats spend: Institutional firms with unified data and shared incentives turn pilots into repeatable products.
• Measure what matters: Track cycle time, write-offs, matter predictability and client satisfaction alongside fees and margin.
• Price with intent: Move early to outcome-oriented models where AI provides clear acceleration or consistency gains.
• Codify quality: Embed review protocols, human sign-off points and disclosure standards; make assurance visible to clients and tribunals where relevant.

Our Strategic Takeaway

AI is not merely another tool in the stack; it is a forcing function for business model clarity. For firms, success rests on disciplined pricing, de-siloed knowledge, and incentives that reward institutional collaboration. For clients, value comes from transparency, predictability, and outcomes. As Taboada and McLaren argued, “the question is not whether AI works—it is whether firms will reorganise themselves to capture its benefits before they wake up and find the economics of the market have already moved on.”