As a visionary leader at the intersection of law and technology, Byong Kim has spent years bridging the gap between traditional legal practice and cutting-edge digital transformation. Currently serving as the Chief Data and AI Officer at Seyfarth Shaw, he has moved from leading specialized innovation teams to orchestrating a firmwide strategy that integrates generative AI into the very fabric of legal service delivery. His expertise lies not just in the software itself, but in the complex human and organizational shifts required to make technology truly effective for global clients.
In this conversation, we explore the challenges of scaling niche innovations, the strategic “buy versus build” dilemmas facing modern firms, and the reality of managing client expectations regarding AI-driven cost savings. We also delve into the changing definition of what it means to be an attorney in an era where technology is no longer an optional tool, but a core competency.
You recently transitioned from leading a specific innovation lab to a C-suite role overseeing firmwide data and AI strategy. How do you scale niche technology solutions across every practice group, and what specific steps do you take to ensure these tools improve service delivery for relationship partners?
The shift from a senior director role to the C-suite has fundamentally changed my perspective from being project-focused to being firm-focused. When I led Seyfarth Labs, we were very team-centric, often diving deep into specific technical hurdles, but now I have to view the entire firm as a cohesive ecosystem. To scale these solutions, I engage in direct, high-level dialogues with relationship partners and every single practice group to identify their unique pain points. It is not enough to simply build a tool; we must ensure it integrates into their specific service delivery models so it adds tangible value to the client relationship. This process involves a rigorous feedback loop where we take the successful experiments from our labs and adapt them to meet the broader, more complex needs of our diverse legal teams.
When managing internal programs like science-fair-style training or innovation labs, how do you balance the need for scalable internal tools with nuanced client demands? Could you share an example regarding the “buy versus build” decision-making process when considering new vendor partnerships?
Striking that balance is a constant exercise in strategic prioritization because focusing too heavily on one side can lead to missed opportunities. If we only solve for the client side, we end up with niche solutions that are too narrow to scale; if we only focus internally, we lose sight of the actual value we provide to the market. Our “SEYence Fair” program is a perfect example of how we find that “sweet spot” by having client teams collaborate with staff who have already tackled similar challenges. Regarding the “buy versus build” dilemma, we are currently evaluating several new vendor partnerships while simultaneously developing our own proprietary agentic workflows. We decide to “build” when a solution requires a level of customization that current market vendors cannot provide, but we “buy” when a partner offers a foundational technology that allows us to move faster without reinventing the wheel.
While many firms are just now exploring agentic workflows, some have a long history with robotic process automation. How are you integrating these foundational automation tools with modern AI agents, and what specific metrics do you use to determine if an agentic workflow is successful?
We are in a unique position because we were one of the first law firms to adopt robotic process automation (RPA), which gave us a very sturdy foundation for what we are doing today. Integrating these legacy automation tools with modern AI agents allows us to move beyond simple task replication to more complex, decision-based workflows. We measure the success of these agentic workflows by monitoring real efficiency gains and tracking how well the technology facilitates cross-team collaboration. While everyone is talking about AI agents in the news right now, our focus is on ensuring these tools actually work in the high-stakes environment of a law firm. Success is not just about the technology running correctly; it is about whether the agent can autonomously handle a multi-step process that previously required hours of manual oversight.
Clients often expect immediate cost reductions from AI, yet simply adopting new tech does not always guarantee efficiency or savings. How do you manage these difficult ROI conversations, and what specific process redesign steps are necessary to ensure that AI integration actually results in tangible gains?
These are incredibly important and difficult conversations because there is a common misconception that “slapping on” technology will automatically lead to cheaper and better results. I am very honest with our clients: technology adoption is a “change management” problem as much as it is a technical one, and it does not guarantee immediate savings. To ensure AI results in real gains, we have to engage in a total process redesign, looking at how the tech interacts with every stakeholder and current workflow. We are still in the stage of figuring out the exact ROI metrics, but the key is monitoring the actual impact on service delivery rather than just the speed of the software. If we don’t fix the underlying legal process, the AI will simply do an inefficient job faster, which doesn’t help anyone’s bottom line.
The legal profession is currently rethinking the very definition of a lawyer to include technology adoption as a core competency. How are you restructuring talent management and attorney training to keep up with the rapid pace of AI releases, and what specific skills are you now prioritizing during the hiring process?
The definition of a lawyer has evolved significantly over the last year; it is no longer enough to just be a brilliant legal mind. We are now looking for attorneys who can naturally adopt technology as a core part of their daily workflow, essentially making tech-fluency a requirement for the job. Because the pace of innovation is so frantic—with major announcements happening every two or three weeks—we have restructured our talent management to focus on continuous, agile training. It is my job to help our attorneys understand these tools so they don’t have to become tech experts themselves, but they must be willing to adapt. During the hiring process, we prioritize candidates who demonstrate a mindset of curiosity and the ability to work alongside AI, as these are the people who will thrive in a deconstructed and remodeled legal industry.
What is your forecast for generative AI?
I believe we are entering a phase where generative AI will move from a specialized novelty to a fundamental layer that impacts every single area of the law firm’s business. In the coming years, the focus will shift away from the “magic” of the technology itself and toward the practicalities of how we use it wisely to provide genuine value to clients. We will see a more mature integration where AI agents and human lawyers work in a seamless partnership, but this will require a sustained commitment to rethinking our old ways of working. Ultimately, the firms that succeed will be those that view AI not as a replacement for legal expertise, but as a catalyst for a more efficient and insightful way of practicing law.
