A fundamental shift is underway in one of the most traditional professional services sectors. Law firms are beyond adopting artificial intelligence; they are restructuring services and operating modelsaround it. In some cases, this means becoming technology-led businesses, while in others it means embedding AI into existing workflows to enhance how legal services are delivered and firms are operated.
This transformation is being driven by economics and client expectations rather than hype. Clients increasingly expect faster, more transparent, and more predictable outcomes, with technology-based questions becoming a mainstay and increasingly higher weighted part of invitations to tender. Firms face pressure on margins and competition from alternative providers. AI offers a way to reconcile these demands by automating process-heavy work and improving operational efficiency.
A market divided: tech-led vs tech-enabled
The legal sector is not evolving in a single direction. Instead, a divide is emerging between tech-led models and tech-enabled ones. Tech-led businesses use AI and automation to deliver legal outcomes directly, often minimising human involvement. Tech-enabled firms, by contrast, use AI to support professionals, allowing them to focus on advisory work and client relationships.
This divergence reflects the nature of legal work itself. Some tasks are inherently suited to automation, while others depend heavily on context, judgement, and human interaction. The result is likely to be a bifurcated market, where execution and advisory services are increasingly distinct.
The automation of commoditised work
AI is already transforming areas of law that are repetitive, structured, and high volume. Tasks such as due diligence, document review, and standardised drafting are becoming increasingly automated. With access to structured datasets, including Companies House and the Land Registry, technology platforms are streamlining processes that were previously time-intensive.
These systems are evolving rapidly with AI capabilities on the rise. What began as data extraction and workflow support from machine learning and smaller language models is moving toward drafting assistance, and ‘comprehension’ of legal risk by large language models, leading ultimately, to full transaction automation for straightforward matters. This creates a category of commoditised legal services where value is defined by speed, accuracy, and cost.
Consider conveyancing, where leasehold transactions such as the purchase of a flat involves more work for a typically less valuable property, because there is a lease to review. For the most part, there is a prescribed set of points to check to determine whether a lease is satisfactory for the buyer and its lender. Understanding those points and the implications of the various answers means a set of rules can be written and delegated to a person, or an AI, to review and check, taking the time to review a lease from a few hours to a few seconds.
However, such shifts introduce strategic risk. Commoditised work typically operates on lower margins and is more exposed to competition. Firms that rely heavily on this type of work may find it difficult to differentiate themselves as automation becomes more widespread, and the firms participate in the proverbial race to the bottom.
The role of AI Agents in legal workflows
A further development is the emergence of agentic AI workflows, systems capable of executing multi-step tasks with limited human intervention. Unlike earlier tools that assist with discrete activities, AI agents can manage entire workflows, from gathering information to producing outputs.
In legal contexts, this could include coordinating onboarding details such as identification, conducting conflict of interest checks, preparing fee estimates based on questionnaires, conducting due diligence processes, managing transaction checklists, or progressing matters through defined stages. These agents operate within set parameters, but their ability to chain tasks together represents a significant step toward end-to-end automation.
Despite this progress, their effectiveness depends on clearly defined rules and governance of both data accessibility and output oversight. Legal work often involves exceptions, ambiguities, and evolving circumstances that are difficult to encode fully. As a result, AI agents are currently best used as accelerators of process rather than replacements for professional judgement.
If we consider again a conveyancing transaction, an agentic workflow could, for example, review title information, searches, and enquiries, all independently of one another, checking for prescribed items that would be considered as acceptable, undetermined, or unacceptable, flagging to an enquiry agent that knows how to raise appropriate enquiries or report risks in a client-friendly manner.
Why advisory work remains human-centred
In contrast to commoditised services, advisory work remains deeply dependent on context. Legal advice often requires interpreting incomplete information, balancing competing priorities, and anticipating future developments.
Wills and estate planning provide a clear illustration. While AI can assist with drafting documents, the underlying advice depends on understanding family dynamics, long-term intentions, potential changes in circumstance, and the sometimes irrational human emotion. These are not technical considerations.
Many firms already have experience with digital will-writing platforms, which demonstrate both the efficiency gains and the limitations of automation. Technology can improve access and consistency, but it cannot fully replace the need for tailored guidance.
Most current AI models used in professional services are not tailored to seeing and understanding the physical condition of a client, and what intuitively a human might think is relevant to them. Context is critical in delivering sound legal advice. Knowing to ask questions based on experience and listening to not only the words but the initiation of a reply and how to follow that, is something that is not currently replicated in mainstream technology solutions.
Immediate gains: efficiency and consistency
The most immediate impact of AI is operational. Processes such as client onboarding, anti-money laundering checks, and matter triage can now be completed far more quickly than through traditional methods. In some cases, these activities can be accelerated by up to 90%.
This allows firms to reallocate time and resources toward higher-value work. Lawyers spend less time on administrative tasks and more time on strategy, negotiation, and client engagement. This is of course further enhanced with the AI capabilities now available in many ‘back office’ business support services, which is beyond the scope of this article, where AI can help reduce the increasing demand of non-chargeable time.
Crucially, AI does not remove the lawyer from the process. Instead, it changes the nature of their involvement. Professionals move from execution to oversight, ensuring that outputs are accurate, appropriate, and aligned with client objectives.
Client risk assessments and anti-money laundering is an interesting example of the enormous benefits that AI can deliver to law firms and professional services generally. This is critical, regulated work that must be done, but which clients don’t value. Firms must balance the challenge of spending sufficient time and being certain about work for which they rarely get paid. AI can help reduce the overhead by quickly reviewing client due diligence documents and also check additional sources of information that firms may consider excessive or unnecessary but which potentially contain key client data.
Legal as a Service – The legal sector’s next big transformation
Legal firms are also rethinking how ongoing services are delivered. One emerging model is “Legal as a Service” (LaaS), which reframes the traditional matter and estimate model with a subscription-based offering.
Under this model, clients engage firms on a retainer basis for ongoing support. Automation handles routine tasks and while lawyers continue the traditional work, the guarantee of workplaces greater focus on the need for process improvement and automation, with a longer-term focus on strategic advice and complex decision-making.
Clients benefit from greater cost-certainty and convenience, while firms gain predictable revenue and improved scalability. This reflects a broader shift away from reactive, transaction-based work toward proactive, relationship-driven services.
Why is this shift happening
The traditional approach to legal services is fragmented and labour-intensive. In some cases, clients note the ‘billable hour’ as an incentive to ‘draw out’ matters. This is understandable, but it is not the ambition of progressive firms.
As demand increases for efficiency, transparency, and predictability, firms are exploring ways to modernise these processes. AI and automation enable integration and oversight, while retainer-based models provide a more stable and scalable framework for delivery.
The limits of AI in legal practice
Despite its potential, AI is not a complete solution. Its effectiveness depends on the quality of data, the clarity of prompts, and the presence of appropriate safeguards. Without these, there is a risk of error and over-reliance, the latter of which is often lamented by seasoned lawyers who have poured over statute books and case law, absorbing the nuance of judgement and how laws are created on the consideration of case context.
Legal practice also involves ethical considerations and professional accountability. Responsibility for outcomes cannot be delegated to technology. Human oversight remains essential, particularly where decisions carry significant legal or personal consequences.
This underscores the importance of balance. AI should be viewed as a tool that enhances legal services, not as a replacement for the principles that underpin them.
The next phase of transformation
The next phase of change in the legal sector will not be defined by whether firms adopt AI, but by how they integrate it. The most successful firms will be those that embed AI into their operating models while maintaining a clear focus on where human expertise adds value.
A clearer division is likely to emerge. Commoditised work will continue to be automated and delivered at scale, often through tech-led platforms. Advisory work will remain human-centred, supported by AI, but not replaced by it.
Ultimately, law firms are not becoming purely technology companies. They are evolving into hybrid organisations, where AI drives efficiency and consistency, and human professionals provide judgement, context, and trust. This balance will define the future of legal services.
