AI-Enabled Business Models in Legal Services: From Traditional Law Firms to Next-Generation Law Companies?
The application of artificial intelligence (AI) and related technologies is poised to transform the way value is created and captured in professional services. AI involves the use of computer systems to perform tasks normally requiring human intelligence. Machines have long been used to automate routine tasks, but what makes AI highly pertinent for professional work is its growing capacity to automate non-routine or knowledge-based work. AI has been successfully applied to a wide range of professional and business contexts, from HRM and CRM, supply chain management, to medicine and healthcare. However, the application of AI to legal services has so far been little studied. In a recent paper, we ask: what will happen to law firms and the legal profession when the use of AI becomes prevalent in legal services? Will the profession preserve its traditional role and forms of organizing? Or else, how will their role and forms of organizing change in the face of competition from alternative legal service providers?
From a macro perspective, the impact of AI technology can be understood as one of a number of forces that together are putting pressure on professional autonomy and traditional forms of law firm organization. These include intensifying competition—both within and between professions—fostered by globalization and fragmentation of professional expertise, associated pressure to redefine professional standards in terms of commercial outcomes, and deregulation of professional monopolies. Consequently, large globalizing law firms face pressures to move away from the traditional professional partnership (P²) model towards more managed professional businesses. Nevertheless, lawyers have retained much professional autonomy and discretion, and the role of partners as owners and decision-makers in law firms has proved remarkably robust.
The transformational promise of AI as a ‘general purpose technology’, however, lies in its potential to substitute for humans altogether. Does this mean that the long-foretold demise of the P² model is now imminent, as predicted by commentators who have pronounced the ‘death of Big Law’ and the ‘end of lawyers’ in the face of technological change? Or might the P² model be reinvigorated by using AI to automate professional work within these firms, reversing the trend towards offshoring to low-cost locations?
We derive answers to these questions from an analysis of complements to the productive deployment of AI. In a productive process, ‘complements’ may be defined as inputs to production and organizational features the combined effect of which is greater than the sum of their individual effects. Our theoretical analysis focuses on a linked cluster of such complements: the business models for most effective deployment of AI, the assets—human and nonhuman—necessary to implement these business models, and the organizational structures best suited to assemble and manage these assets.
We develop our argument by transitioning through three levels of analysis: tasks, business models, and organizations. We begin by considering the technical capabilities of AI for application to tasks (not jobs) in legal services. Some tasks are substituted for, and others are augmented, by AI. Today’s AI systems are increasingly capable of substituting for ‘non-routine’ legal tasks, but are augmented by multi-disciplinary expert inputs (in data science, project management, etc.). Limits also remain: client-facing work, and services that are highly tailored to a particular client, are unlikely to be automated any time soon. Moreover, because today’s AI requires large amounts of relevant and pre-labelled data, it can scale analysis only where such data are available.
We next consider how capitalising on these opportunities may engender new business models in legal services, understood as firms’ logic of value creation, delivery and capture to satisfy customer need. In contrast to the traditional legal advisory business model, the AI-enabled business models we identify seek to deliver services that can be scaled, using output- rather than input-based pricing and drawing on a very different mix of assets, encompassing nonhuman as well as a multidisciplinary mix of human capital. The assembly and management of these new combinations of assets in turn has implications for organizational structure. Law firms are traditionally structured as partnerships that complement the legal advisory business model and associated human resource practices. AI-enabled business models, by contrast, imply greater reliance on multidisciplinary teams of human capital and outside capital, for which the corporate form, with more hierarchical management and access to outside capital by issuing shares to investors, appears a better complement. This presents strategic challenges for incumbent law firms, as they attempt to mix old and new business models. We review law firms’ experimentation in mixing business models during a nascent stage in industry change, and draw implications for shifts in professional fields of lawyers and other experts.
Our study contributes to taking stock and charting the future of professions and professional service firms (PSFs) in at least two ways. First, by carefully analyzing how AI affects tasks in legal services, we go beyond a simplistic ‘AI will destroy professional work’ view, and also beyond charting the potential exposure of professions to AI. In particular, we highlight the central importance of business models and organizational complements in mediating how AI affects the complex interplay of task substitution, task augmentation, and the creation of new tasks. Second, we contribute to the literature on changes in professionalism as a result of deregulation, digital technology, globalization, and other forces. Specifically, our study demonstrates that different combinations of traditional and AI-enabled business models lead to varying pressures for members of the legal profession to hybridize their expertise. Lawyers may themselves acquire skills in data science and management not traditionally associated with them. Thus, our study highlights the utility of business model as a level of analysis to inform the debate on the future of the professions.
John Armour is the Hogan Lovells Professor of Law and Finance at the University of Oxford.
Mari Sako is Professor of Management Studies at Saïd Business School, University of Oxford.
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