I constantly encourage my teams to leverage Generative AI (GenAI) in their daily work. It's a powerful way to offload manual toil, allowing them to focus their mental energy on higher-level creative tasks and shrink the gap between an idea and its realization.
There are countless GenAI techniques geared toward individual contributors who spend most of their time building, configuring, deploying, or maintaining systems. However, as a leader, my day-to-day looks quite different. I dedicate very little time to individual contribution tasks. The vast majority of my time is spent collaborating, planning, aligning, tracking, and, generally, addressing the challenges faced by the people interacting with technology. This means I need a more novel approach to discovering how GenAI can benefit me and my peers.
That said, I truly believe in this technology's transformative potential. I also believe in practicing what I preach. Over the past six months—which is when I truly integrated GenAI into my regular work toolkit—I've discovered several use cases particularly well-suited to GenAI augmentation.
I'm excluding scenarios that are now ubiquitous and often built directly into email and video chat software. Examples include rewording emails for conciseness or tone, and automatic meeting transcription and note-taking. While these uses of GenAI are incredibly beneficial, they're not particularly interesting to discuss at this point.
Reviewing contracts
Several times a quarter, I receive contracts outlining work engagements with collaborating partners for my team or project. These documents can range from a few pages of boilerplate text requiring minimal attention to dozens of unique, complex pages. The agreement's context might also include specific details that, if not highlighted amidst dense paragraphs, could significantly impact the initiative. I have nearly zero legal experience, and while I work with legal partners who can review agreements from a purely fiduciary perspective, only I possess the context necessary to connect the dots to the underlying tactical and financial outcomes. Using a GenAI tool to summarize key points and discuss the agreement in the voice of a legal professional has saved me tremendous time and effort across multiple contract reviews.
Team working agreements
I believe all engineering teams should maintain a team working agreement. The best way to draft an initial version is to adapt one from a similar team within your organization. If that's not possible, a GenAI tool is easily the most effective way to generate a solid first draft quickly. When doing so, be sure to provide appropriate context, such as team size, locality (in-person, hybrid, remote, number of time zones, etc.), core working hours, tech stack, planning cadence (yearly, quarterly, etc.), and agile process (Kanban, Scrum, etc.), if applicable.
Summarizing weekly team accomplishments
Each week, our leadership team must summarize and roll up accomplishments or blockers from the previous week. Witnessing firsthand the improved quality of updates from my leaders who leverage GenAI for this task has been astounding. A key enabler here is the gradual improvement in tools that can connect to engineering backlogs, gleaning insights about completed tasks while integrating context from other sources like team correspondence via email or Slack, and internal documentation. I've even received direct feedback that using GenAI for this weekly task highlighted details a leader would have otherwise missed unless they meticulously reviewed every Jira ticket and Slack thread from the past five business days.
Authoring BPMs from incidents
When systems break and involve numerous people and systems, it can be daunting to reconvene after the dust settles and organize the past chaos into a coherent Blameless Postmortem document. Now that virtually every virtual meeting can be fully transcribed and combined with a Slack channel summary almost instantly, adding context to perform these tasks in the voice of an Incident Manager and generating the output in an enterprise-ready BPM structure becomes trivial. I've actually found this technique more useful for incidents I wasn't directly involved in. Being on the call and hearing all the context firsthand reduces the augmentation's boost. However, if I'm on the outside looking in, I can piece together key insights like business impact and "no-repeat" suggestions with a few well-crafted prompts. This is invaluable for systems outside my direct scope of responsibility but perhaps a critical dependency, or even when someone casually asks me what happened with a system that was degraded last week.
Codebase analysis
While I don't spend as much time in the code as I used to, as a leader, I'm deeply invested in key aspects of the code my team creates and maintains. I trust my smart team members to uphold proper coding standards, but in some cases, I use GenAI tools as a forensic aid to understand where code content might be contributing to unwanted friction or negatively impacting key outcomes for my teams. There's no shortage of static code analysis tools that already do this effectively. However, similar to my example of contract analysis, the real novelty of using GenAI here is its conversational interface. Instead of simply viewing a static code analysis report that assigns a one-dimensional letter grade to codebase "quality," I can have a conversation about the codebase. I can inquire about the multifaceted elements that contribute to its overall quality from the perspective of where I observe or hear friction within the broader human and technology systems.
Prototyping
This might be the use case most likely to be scoffed at by senior engineers. For those who are immersed in code daily, I understand that the gap between an idea and working code is far smaller than it is for a leader who only gets to write a few lines of code in their free time a couple of times a month. To that senior engineer, I say: I've been there and completely understand. But now, with my coding reflexes dulled over multiple years outside individual contribution, there's nothing more satisfying than translating an idea I conceptually know how to code into a working prototype in a few minutes instead of a few frustrating hours. I most commonly use rapid prototyping when explaining a design concept; rather than whiteboarding it, I can quickly whip up a low-fidelity working version in about the same amount of time.
I might do a second edition of this "use-case log" in another six months, as further experimentation yields additional valuable scenarios from the engineering leader's perspective. Please engage if you're interested.