Dave Cook is Global Director of Resource Management at BigHand, where he leads work focused on resource management, work allocation, and the technology that supports those processes. His career spans around 15 years in legal, following earlier experience at PwC, and includes founding Mason & Cook, which was acquired by BigHand in 2020. Throughout the session, Cook’s central motivation was clear: helping law firms allocate work more fairly and effectively while ensuring that technology supports, rather than replaces, human judgement.

Overview

The discussion began with the evolution of work allocation in law firms. Cook explained that traditional partner-led models often relied on familiar relationships and preferred associates, which could leave some lawyers overworked while others were overlooked. More structured resource management helps firms better match work to skills, experience and capacity, improving fairness and access to opportunities. However, change is as much cultural as it is operational, requiring engagement and reassurance that systems support, rather than replace, partner judgement.

AI is accelerating this shift by surfacing relevant information faster, rather than making allocation decisions itself. It can quickly generate shortlists of suitable lawyers, but human judgement remains essential for factors such as relationships, client preferences and team fit. Cook also noted that AI adoption varies widely across firms, with differences driven more by risk appetite than size, particularly around how much data firms are willing to expose to AI tools.

A key theme was how lawyers use the time AI creates. With many reporting several hours saved per week, Cook highlighted the opportunity to reinvest that time into business development, client relationships, pro bono work and deeper expertise. This raises questions for firms about utilisation and pricing models, while reinforcing that alongside AI literacy, human skills such as curiosity, communication and judgement will become even more important.

Key Takeaways

• Structured work allocation improves fairness and performance by better matching skills, capacity and experience, reducing overwork and missed opportunities.
• AI should support, not replace, resource decisions – it can narrow options quickly, but client fit, relationships and judgement remain essential.
• AI adoption varies by risk appetite, with some firms moving fast on training and use, while others remain cautious around data and governance.
• Time saved through AI should be reinvested in business development, client relationships, pro bono work and deeper expertise.
• Efficiency gains will pressure traditional billing models, requiring firms to rethink productivity and pricing.
• Career success will depend on human skills as much as AI fluency, especially curiosity, communication, judgement and client understanding.