The AI Dividend in Legal Services and Its Economic Implications
In recent years, corporate legal departments have been informed that artificial intelligence will enhance the speed, efficiency, and cost effectiveness of legal work.
Despite these claims, the anticipated reduction in legal expenditure remains uncertain.
AI technology is significantly reducing the time required for tasks such as contract review, first pass drafting, clause comparison, diligence review, playbook analysis, and routine redlining, often compressing hours of work into mere minutes.
However, many corporate legal departments are not witnessing a corresponding decrease in their legal spending. According to the Thomson Reuters 2026 State of the Corporate Law Department Report, 36% of General Counsel indicated they expect an increase in overall spending on outside counsel in the coming year, while only 20% anticipate a decrease. Similarly, the CLOC 2026 State of the Industry Report revealed that only 37% of departments expect an increase in outside counsel spending, a decline from 58% the previous year, with only 32% expecting to increase attorney headcount.
At the same time, law firm economics appear robust. Thomson Reuters reported a 7.4% year on year increase in worked billing rates during the second quarter of 2025, while U.S. inflation was approximately 2.8%. Additionally, profits per lawyer rose by 8% and profits per equity partner by nearly 12%.
This situation raises a critical question for corporate legal departments: if AI is enhancing the efficiency of legal work, who is reaping the financial benefits?
Every technological advancement typically generates a dividend, with value being captured by either the provider or the customer. In the context of legal services, the distribution of this value remains unclear.
Law firms should not be expected to lower their prices solely due to reduced execution costs from AI. Clients have historically compensated firms for their expertise, judgement, responsiveness, and outcomes, rather than merely for hours worked. Nevertheless, corporate legal departments must remain aware of the economic implications of AI. If routine tasks can be completed significantly faster, the question arises as to how this efficiency should be reflected in pricing, staffing, and matter budgets.
Corporate legal departments should engage with outside counsel regarding the application of AI in their matters, the types of work that have become more efficient, and how these efficiencies impact staffing and budgets. Furthermore, they should consider whether repeatable tasks are better suited to fixed fees or alternative pricing models.
The most progressive law firms will leverage AI not only to enhance their profitability but also to deliver services in a manner that is more transparent, predictable, and aligned with client value. Firms that proactively adjust their pricing to reflect AI enabled efficiencies are likely to strengthen their relationships with clients.
Conversely, some firms may choose to implement AI discreetly, accelerating internal processes while maintaining traditional billing assumptions. While this approach may be effective temporarily, it risks scrutiny as corporate legal departments become more discerning consumers of AI enabled legal services.
Beyond law firm pricing, there is a significant opportunity to rethink how contract work flows through organisations. Traditionally, contracting has involved sales, procurement, and business teams negotiating agreements, with routine issues often referred to Legal for consistent application of company policy.
This model was appropriate when contract volumes were lower and automation was limited, but it is less suitable in today's context.
AI enables the integration of Legal's guidance directly into contracting workflows. Instead of reviewing every routine issue, Legal can establish playbooks, approval thresholds, and risk tolerances, allowing AI to identify deviations and recommend fallback language while escalating only genuine exceptions.
With well designed workflows, procurement, sales, and business teams can manage many routine agreements within the parameters set by Legal. Legal continues to define policy and manage exceptions, but does not need to be the first point of contact for every contract.
This shift influences how the AI dividend is realised.
If companies persist in sending high volume routine work to outside counsel, they may indirectly benefit from law firms' AI usage. However, they should not assume that these efficiencies will automatically result in lower legal bills. By redesigning contracting processes to retain more routine work within the organisation under Legal's governance, companies are more likely to realise direct savings.
The benefits extend beyond reduced outside counsel expenditure. Quicker contracting can lead to faster sales cycles, more efficient procurement, greater consistency across agreements, improved contract data, and legal teams that can focus on strategic advice rather than repetitive review.
This does not diminish the role of outside counsel, as complex transactions, litigation, investigations, regulatory advice, and novel legal questions will always necessitate experienced lawyers exercising professional judgement. However, a significant portion of routine commercial contracting no longer requires adherence to the traditional operating model.
As organisations continue to develop legal AI, it is essential to recognise that technology alone does not transform an industry; it first alters incentives, prompting changes in operating models.
The current transition is indicative of this evolution. The key question is no longer whether lawyers are utilising AI, but whether organisations are rethinking how legal work is conducted. Procurement, Sales, Finance, Compliance, and Legal all play a role in contracting, and AI allows Legal to establish rules, oversee risk, and maintain governance without becoming a bottleneck for every routine decision.
The initial phase of legal AI focused on productivity, while the next will centre on economic implications. The organisations that stand to gain the most will not necessarily be those employing the most AI, but those that intentionally redesign their workflows to ensure the AI dividend benefits the business rather than dissipating elsewhere in the system.