Al Hounsell on Driving Legal Transformation That Lasts

In our Innovation Spotlight series, we interview innovation leaders about how they approach their jobs and how they’ve implemented and deployed Harvey.

In this edition, we chat with Al Hounsell, National Director of AI, Innovation & Knowledge at Gowling WLG.

Al Hounsell is the National Director of AI, Innovation & Knowledge at Gowling WLG, where he leads the Practice Technology, Knowledge & Practice Support, Library & Information Services, and Solutions & Applied AI teams. He guides firmwide AI strategy, governance, and adoption, and led Gowling WLG to become the first Canadian law firm to deploy Harvey enterprise-wide to all lawyers.

An entrepreneur-turned-legal futurist, Al focuses on building resilient, future-ready capabilities that improve client outcomes and unlock new ways of working. He also teaches disruptive innovation in a law school context, bringing industry parallels into the classroom and back into practice.

Innovation is about building bridges into the future. It starts with a clear view of the future state we are aiming for and an understanding of how adjacent industries are evolving. It requires an honest assessment of where the organization is today — its culture, capabilities, and strengths — and then the discipline to construct practical pathways from here to there.

In practice, that means building capacity and organizational “muscle memory” for the capabilities that will matter tomorrow. We experiment strategically to learn faster where value will concentrate, and we run a structured, data-driven process to measure, assess, and capture value from each project. The bridge is the overarching strategy; the measurement and captured value are the bricks we lay through the day-to-day initiatives that move us forward.

My life outside work revolves around family and staying active. Depending on the season, you will find us wakeboarding or snowboarding together. Health and fitness keep me energized and grounded, and that translates into clear thinking and consistent follow-through at work.

I am also a real estate investor and a dedicated AI tinkerer. I use AI heavily in my personal life to close gaps where administrative work is not my strength. I experiment, write code, and test new workflows — often spending hours in voice interactions with ChatGPT — to see what really works. That hands-on curiosity informs how I lead innovation: stay curious, prototype quickly, keep what works, and scale it with intention.

What truly excites me is the feeling of standing on the edge of the most transformative disruption the legal industry has ever seen. I have spent a lot of time studying how disruptive innovation plays out across different industries, and I also teach these principles in a law school context, which lets me hear creative insights from students who look at legal with fresh eyes.

Now, being able to apply those lessons in real time and shape strategies that will lead us into a successful future is incredibly exciting. We are at a moment where the legal world is rapidly transforming, and it is thrilling to be at the forefront, crafting a path forward where we do not just adapt to change — we help define it. Notably, while legal has often been a last mover with major technologies, with generative AI the legal industry is right at the front of the change curve this time.

We ran a series of focused pilots to identify the highest-value use cases in our context. We measured client responsiveness, quality, and risk reduction, and we listened closely to practitioner feedback about day-to-day workflow fit. Through that process, Harvey emerged as a great tool for the type of work our lawyers do and the outcomes we care about. Selecting Harvey at the end of the pilot was a natural decision, and we are excited to partner closely with the Harvey team.

What we aim to achieve with Harvey is simple and ambitious: embed AI into the fabric of legal service delivery at scale. That includes enabling lawyers to deliver higher-quality work product faster, increasing responsiveness and reducing risk. It also means building durable capabilities — playbooks, prompt patterns, and repeatable workflows — so AI becomes a dependable part of how our firm operates.

Adoption has been broadly uniform. We are seeing enthusiasm across age spectrums, practice areas, and seniority levels. That evenness has been a pleasant surprise; rather than clustering in one discipline or cohort, interest and usage have spread widely and steadily, which reinforces that the value proposition resonates across the firm.

Our adoption program is led by our Director of Practice Technology, Michelle Fernando, and it has accelerated since she joined. We combine structured training with an organic, peer-driven model. Internal champions and power users share concrete examples inside their practice groups, we highlight pioneers who are discovering new ways to use Harvey in their domain, and we layer that with targeted enablement sessions. The blend of formal coaching and grassroots storytelling helps the platform become part of everyday workflows.

1. Large-scale contract and document review: We use Harvey for crafting prompts and patterns that support bulk extraction and structured analysis across document sets. This sits at the intersection of LLM strengths and firm-scale ROI, and it has helped us rethink due diligence, contract review, and related processes for quality, accuracy, and speed.

2. Litigation preparation: Harvey helps teams strategize and synthesize large matter files into usable work products — chronologies, timelines, issue maps, and draft outlines — so lawyers can brainstorm faster and prepare more comprehensively for trial and key milestones.

Success is really defined by client outcomes. Because of that we're focused on delivering higher-quality outputs, offering more holistic and strategic advice, and strengthening our role as trusted business partners. To gauge progress, we track sub-metrics that roll up to client value: client responsiveness, quality of work product, and risk reduction. We also monitor adoption depth — how often and how naturally Harvey shows up in regular workflows — as a proxy for sustained impact. I think these are all really good indicators of success.

First, personal experimentation matters. Individuals who explore GenAI in their personal lives tend to integrate it into professional workflows more quickly. This finding parallels a lot of the recent studies on this subject: You need to give people space to create silly poems and generate images of cats in phase 1, because phase 2 occurs after people are comfortable experimenting with the tools and begin to ramp up very quickly applying them to professional use cases. Creating space for safe experimentation accelerates comfort and competence, which leads to real gains at work.

Second, I find myself repeating this over and over: disaggregate complex tasks. Break work into component parts and guide the legal AI tools step by step. Iterative workflows with manageable inputs and explicit decision criteria consistently outperform one-shot, overloaded prompts. So it's important to set users' expectations accordingly: know what the tools do well, where they struggle, and work interactively with the tools in light of that reality, approaching one task at a time through to the completion of the overall project.

The most significant impact on our industry is that GenAI will reshape the core of how legal value is delivered — this is a lot bigger than shoehorning a new fancy tool into legacy processes. It is about reimagining how we operate, the outcomes we produce for clients, and the business models that support those outcomes.

The real disruptive power isn’t actually in GenAI capabilities, but in the new business models it enables. This mirrors a repeated historical pattern: When a disruptive innovation cuts to the core of how an industry’s offerings are produced, it eventually triggers a reimagining of the entire business model. That is the most significant transformation we will see in legal and where I'm spending a lot of my free time imagining, strategizing, testing, and planning.