Generative AI is actively reshaping how work is done across all industries. But for law firms, the pathway from experimentation to real, responsible adoption isn’t obvious.
In 2025, Gowling WLG Canada ran a 12-week, Firm-wide GenAI pilot to better understand how this technology could support legal work without compromising quality, professional judgment, or client trust. More than 300 lawyers and business services professionals participated, completing tasks across seven leading GenAI solutions with enterprise-grade security and Canadian data residency.
What emerged was not a case for wholesale transformation, but a clearer picture of where GenAI can add value, where it introduces new risks, and where human expertise remains indispensable.
Key pilot observations
- More consistent support for routine work: GenAI was most effective when applied to structured, repeatable, non-billable administrative tasks.
- Measured quality improvements, dependent on use-case: Most tasks benefited from the clearer structure that GenAI-assisted workflows tend to require, although outcomes varied significantly and consistently required professional review.
- Review support, not risk mitigation: Many participants found GenAI tools useful as a third set of eyes that occasionally surfaced oversights or inconsistencies, while leaving accountability and professional responsibility unchanged.
Taken together, the findings suggest that GenAI tools will play a constructive role in legal and business services when deployed thoughtfully, governed carefully, and used to reinforce, not shortcut, the standards that define the profession. Their value lies less in automation for its own sake and more in supporting consistency, awareness, and judgment across procedural and knowledge-based workflows.
In the months since the pilot, Gowling WLG has been deploying GenAI solutions in targeted practice and business areas. These implementations were informed by close collaboration across our technology ecosystem and supported by our technology partners: Thomson Reuters, Lexis Nexis, HarveyAI, Legora, Microsoft, and OpenAI and their respective platforms: Westlaw with AI Assist and CoCounsel, Lexis Protege, Harvey, Legora, Copilot for M365 and ChatGPT Enterprise.
The adoption of GenAI and broader AI and automation tools across the legal industry will require deliberate leadership, clear guardrails, and realistic expectations. With that in mind, for legal teams looking to explore how generative AI and automation can fit into their workflows responsibly and with reduced friction, we’ve developed the following playbook to help you.
Your playbook for responsible GenAI adoption
Introducing any form of automation across your workflows and teams requires structure, discipline, and consistent iteration and improvement. Based on pilot outcomes and industry best practices, this playbook outlines five essential strategies for in-house legal teams looking to scale GenAI responsibly and effectively.
1. Start in the “assistant zone”
Why it matters:
GenAI performs best on structured, repeatable tasks with clear parameters and established workflows.
What Gowling WLG pilot data shows:
The most effective and sustainable use of GenAI in legal practice begins by focusing on the “assistant zone,” meaning routine, predictable tasks where GenAI can provide dependable support within well-defined boundaries. Pilot results show that GenAI delivers the most value when used as a digital assistant rather than as an autonomous expert, particularly for activities such as first-draft writing (memos, emails, policies), clause-level contract review, compliance synthesis, and reporting.
Across these tasks, the benefits observed during the pilot were most evident in responsiveness and consistency of work. GenAI was particularly helpful in applying logical structure, aligning tone with firm standards, and highlighting potential gaps such as missing exhibits or undefined terms. When used appropriately, it also supported review processes by helping surface routine issues that warrant closer human attention.
Action for in-house teams:
Start with a small number of clearly defined use-cases and embed guardrails early, including mandatory source links, source-freshness prompts, and two-step human review processes. This allows teams to build familiarity with GenAI while maintaining control over quality, accountability, and professional standards.
Focusing initial adoption on assistant-level tasks provides a stable foundation for broader GenAI integration over time, enabling legal and business teams to introduce the technology in a measured way and expand its use gradually as confidence, governance, and experience develop.
2. Embed AI into daily workflows
Why it matters:
Seamless integration outperforms technical firepower.
What Gowling WLG pilot data shows:
Pilot participants reported that access to GenAI through familiar platforms such as Microsoft Word, Outlook, and document management systems played a significant role in adoption and day-to-day usefulness.
Embedded tools were most effective for routine, high-frequency tasks, including summarizing meeting notes, drafting follow-up emails, and preparing first-draft memoranda. In these contexts, GenAI supported administrative work by reinforcing uniform structure, tone, and completeness across documents.
The pilot data underscores that professionals are more likely to use GenAI consistently when it is available at the point of need, without requiring them to leave their primary workflow or learn a new interface. This ease of access is particularly important for administrative and knowledge support tasks, where speed and convenience are paramount. Features such as one-click summarization and direct export to document management systems were frequently cited as drivers of adoption and user satisfaction.
Action for in-house teams:
Focus on integrating GenAI into the platforms legal professionals already use. Prioritize embedded tools for routine drafting and summarization, and reserve standalone environments for more complex research or bespoke drafting, where appropriate.
3. Design a lawyer-in-the-loop protocol
Why it matters:
AI is a digital assistant, not an autonomous expert.
What Gowling WLG pilot data shows:
Across more than 1,200 tasks, participants consistently reported that an initial AI draft followed by lawyer review, and where appropriate a further AI revision, supported quality while maintaining professional standards.
The pilot data also highlights the limits of GenAI outputs. While GenAI helped surface potential issues such as missing clauses or outdated citations, a subset of tasks flagged hallucinated content, and incomplete or incorrect citations, particularly in complex legal reasoning, bespoke drafting, and research tasks. The lawyer-in-the-loop protocol proved critical in identifying and correcting these issues before work progressed.
Standardized safeguards such as prompt templates requiring live source links, source-freshness prompts, and two-step human review cycles further supported consistent and responsible use. These measures have the added benefit of providing guardrails for users who might be more susceptible to over-reliance on AI outputs.
Action for in-house teams:
Adopt a clear lawyer-in-the-loop workflow for GenAI-assisted legal tasks.
- AI draft: Use GenAI to generate the initial draft, conduct research, or extract key information.
- Lawyer review: A qualified lawyer reviews the output for accuracy and completeness, including verification of sources and reasoning.
- AI revision (optional): Where appropriate, use GenAI to revise the draft based on the lawyer’s feedback.
Embedding a lawyer-in-the-loop protocol in your pilot helps ensure GenAI supports legal work in a controlled and responsible manner while preserving professional accountability.
4. Build skills through rapid iteration
Why it matters:
Effective GenAI use is learned through practice and refinement, not assumed.
What Gowling WLG pilot data shows:
The pilot data showed that users developed stronger judgment and more consistent results through repeated, structured experimentation. Short, iterative sprints helped teams quickly identify what worked, where risks emerged, and how to adjust their approach in real-time.
Targeted upskilling resources, including concise task playbooks and role-specific prompt libraries, supported this learning process by helping users move beyond basic experimentation toward more deliberate, context-aware use. The pilot also reinforced the importance of prompt design with well-structured prompts producing more reliable outputs than open-ended or vague instructions.
Built-in feedback loops allowed teams to test new workflows, share lessons learned, and incorporate improvements quickly. These cycles surfaced recurring issues, such as over-reliance on AI by junior staff or gaps in citation verification, which were addressed through updated guidance and safeguards rather than ad hoc correction.
Action for in-house teams:
Structure GenAI adoption around short, focused sprints that allow teams to pilot new tools or workflows, gather feedback, and refine their approach before broader rollout. Additionally:
- Develop concise, role-specific playbooks and prompt libraries that capture effective practices and common pitfalls, and update them as experience grows.
- Establish lightweight feedback mechanisms to inform training, update protocols, and guide future iterations.
- Provide space for controlled experimentation by setting clear boundaries for acceptable use and reinforcing expectations around review and accountability.
Building skills through rapid iteration supports sustainable adoption and helps enable GenAI use to mature alongside professional judgment.
5. Use metrics to justify expansion
Why it matters:
Leadership support depends on clear, credible evidence of where GenAI adds value and where it does not.
What Gowling WLG pilot data shows:
The pilot data provided early, evidence-based insight into where GenAI can most effectively support legal and business work. By taking a structured, data-informed approach, the pilots demonstrated how responsible experimentation can surface practical opportunities to improve internal processes and support client service, while maintaining the standards of judgment and care that define legal practice.
Clear metrics also played an important role in informing leadership discussions and guiding future investment decisions. Tracking outcomes helped distinguish between use-cases that benefited from GenAI support and those where the technology added limited value, supporting more disciplined and targeted adoption.
Action for in-house teams:
Track a small set of consistent metrics across GenAI-assisted tasks to inform adoption decisions and resource allocation. Where appropriate, use proxy measures to translate internal improvements into business-relevant insights.
Review this data regularly to identify where GenAI support is most appropriate and where additional safeguards or alternative approaches may be required. Use metrics to guide prioritization and scaling decisions, rather than as a measure of individual performance.
Defining boundaries: Task suitability and risk considerations
The GenAI pilot confirmed that hallucinations, incomplete citations, and in some cases, outdated information, remain a material risk in generated outputs, particularly for work requiring complex reasoning, bespoke drafting, precise statutory interpretation, or detailed redlining. In these contexts, error rates increased.
Professional oversight remains critical to any task leveraging GenAI. These observations reinforce the importance of deliberate task selection and clear boundaries for GenAI use. The following use-cases highlight where the technology aligned well with these parameters during our pilot and may act as guiding examples for your own.
Legal research and analysis
GenAI supported research by consolidating authorities, surfacing additional considerations, and acting as a sense-check across jurisdictions. While outputs frequently required verification due to hallucinated or outdated sources, participants found value in using GenAI as a research aid rather than a source of authority.
Drafting and summarization
GenAI performed well in generating first drafts of memoranda, emails, and summaries, often producing serviceable outputs with clear structure and consistent tone. Still, 100% of drafts required human review; however, participants valued the tools for accelerating early-stage drafting and flagging basic instruction issues such as inconsistent references or missing elements.
Contract and document review
GenAI was effective in high-volume review tasks, including clause extraction, redline comparisons, and deal summaries. Lawyers found particular value in structured outputs and automated tables that supported faster review, provided hallucination risks were managed through verification and oversight.
Administrative and knowledge support
Non-billable tasks such as summarizing meetings, creating follow-up communications, and preparing internal updates aligned well with GenAI capabilities. While individual gains were modest, aggregated efficiency and improved clarity made this a low-risk, high-utility use-case when paired with basic review.
Training, compliance, and knowledge creation
GenAI supported the development of training materials, compliance digests, and know-how content by synthesizing large volumes of source material into structured outputs. Users reported moderate quality improvements, with continued reliance on human review to confirm accuracy, currency, and jurisdictional relevance.
Closing perspective
Ultimately, the value of generative AI in legal practice lies not in the technology itself, but in how deliberately it is applied. When used within clear boundaries, supported by professional judgment, and measured against real outcomes, GenAI can strengthen professional work without compromising on the standards this industry is known for.
Responsible adoption is less about pace and more about precision. The legal teams that succeed will be those that treat AI as a tool to be governed, not a shortcut to be pursued.
Read the original article on GowlingWLG.com
The content of this article is intended to provide a general guide to the subject matter. Specialist advice should be sought about your specific circumstances.
[View Source]
