AI and automation solutions are quickly becoming embedded into the day-to-day working lives of lawyers and legal teams, transforming the way in which legal services are being delivered and consumed. With so many viable legal technology solutions in the market, the “what” and the “why” are important to the selection process and the business case, but it’s the “how”, ie how such solutions are being implemented in practice, that is the key to success in terms of delivering real value.
For law firms, successful adoption is not just about deploying the solution, it is about the broader change management process, aligning solutions to the needs of the business and embedding those solutions as seamlessly as possible into the day-to-day activities of the legal teams.
1. How law firms are implementing AI and automation
Key AI & automation use cases
With ideas and solutions fighting to keep pace with each other in the legal technology arena, there are already many examples of how AI and automation solutions are being applied in practice to support and enable legal service delivery, including:
Document review / eDiscovery: processing large volumes of documents to identify relevant information, significantly reducing the time and cost associated with legal due diligence and litigation discovery.
Contract analysis and lifecycle management: utilising natural language processing (NLP) capabilities to enable automated contract review, risk analysis and clause extraction.
Workflow automation: automating routine tasks such as billing, matter management and compliance checks to reduce administrative burden and free up lawyers to focus on client management activities and other higher-value work.
Knowledge management: using AI to deliver more accurate and contextually relevant case law and precedents and generally improving the lawyer experience within legal research and other areas of knowledge management.
Predictive analytics: using data-driven insights to forecast litigation outcomes, assess risk and inform strategic decision making.
Implementation methodology
However, implementation of such solutions is not simply plug and play. Law firms are increasingly adopting implementation methodologies that mirror more structured product development lifecycles within a traditional change management framework, including:
Scope and design: engaging with legal teams early to identify their needs and translating those needs into technical requirements.
Pilot / proof of concept: delivering pilots or proofs of concept to test assumptions with legal teams and taking their feedback into account to refine the overall approach.
Build and test: iteratively building and testing the solution across a series of development cycles, as well as formal user acceptance testing with the legal teams.
Go live / BAU: user training and collateral, change management support, communication, usage monitoring and continuous improvement processes.
2. Challenges and opportunities in a regulated, client-focused environment
While AI and automation solutions present significant opportunities for law firms, their staff and their clients, it is also important to be mindful of the challenges with the implementation of such tools. The legal sector’s regulatory landscape is complex, and client trust is paramount.
Challenges
Data sensitivity and privacy: information held by law firms, including legal data and client data, is typically confidential and privileged. Firms must ensure that AI tools comply with GDPR, SRA guidelines, and client-specific data handling requirements.
Explainability and accountability: lawyers must be able to explain and defend decisions. Black-box AI models pose risks in litigation and advisory contexts. Implementation teams must prioritise transparency and auditability.
Cultural resistance: lawyers are trained to be risk averse. Change initiatives must be framed not as threats to professional judgment but as tools that enhance it. This requires thoughtful communication and leadership buy-in.
Opportunities
Enhanced client service: AI can free lawyers from repetitive tasks, allowing more time for strategic advice. Firms that implement AI well can offer faster turnaround, better insights, and more competitive pricing.
New business models: automation opens the door to fixed-fee arrangements, subscription services, and scalable legal products. Implementation teams should work closely with business development to align tech with commercial strategy.
Regulatory leadership: firms that navigate AI implementation responsibly can position themselves as thought leaders in legal ethics and innovation. This builds brand equity and client trust.
The key is to treat regulation not as a barrier but as a design constraint – one that shapes smarter, safer implementations.
3. Why legal technology must be built around the way lawyers actually work
Legal technology implementations are, first and foremost, change management projects; far too often, they fail because they are built in isolation from the end users. A common criticism from lawyers and legal team members is that they feel legal tech it is being done “to” them, rather than “for” them, and “with” them.
Successful implementations address this by engaging the legal team early and working alongside them to design and build solutions around the way in which they actually work in practice.
User-centric design
Implementation teams must engage lawyers early and often. This includes:
User journey mapping: understanding how lawyers draft, review, and collaborate helps identify where AI can add value without disruption.
Co-design workshops: bringing lawyers into the design process ensures that tools reflect real-world needs, not abstract capabilities.
Iterative testing: agile methodologies allow for rapid prototyping and refinement based on user feedback.
Change enablement
Even the best tools fail without adoption. Implementation must include:
Training and support: not just how-to guides, but contextual training that shows how AI fits into specific legal tasks.
Champions and superusers: Identifying early adopters who can advocate for the technology and support peers.
Metrics and incentives: tracking usage and outcomes helps demonstrate value and encourage engagement.
Alignment with legal practice
Finally, implementation must respect the nuances of legal work:
Precedent and risk: AI tools must support, not override, the lawyer’s judgment. Implementation should include safeguards and override mechanisms.
Collaboration and communication: legal work is inherently collaborative. AI tools must integrate with communication platforms and support shared workflows.
Time recording and billing: any tool that affects how lawyers spend time must align with billing practices. Implementation should include updates to time recording protocols and client reporting.
Conclusion
For law firms, safety, compliance and purpose should sit at the heart of embracing AI and broader legal technology solutions. By doing so, it will ensure that innovation enhances the delivery of legal services whilst minimising the disruption to teams.
A key part of achieving this is ensuring that AI and technology strategies are lawyer-led, developed in close collaboration with both legal and technology teams. In an era where legal services are being reshaped by technology, the most impactful transformations are those led by lawyers themselves. A lawyer-led approach ensures that AI and automation are not just technically sound but contextually relevant – designed with an understanding of legal process, regulatory complexity, and client expectation.
By embedding innovation into legal practice through thoughtful implementation, user-centric design, and a commitment to safety and compliance, we are demonstrating how legal technology can truly enhance service delivery. The future of legal services lies not in technology alone, but in how lawyers harness it – with purpose, precision, and partnership.
