Andrew Lau is co-founder and CEO of Jellyfish, a pioneering engineering management platform.
We’re living in the “Year of Generative AI.” Ever since OpenAI launched ChatGPT at the end of 2022, the possibilities of AI-driven tools have captured media attention and driven a wave of imagination throughout the world. We’re seeing new articles on a daily basis, as everyone from academics and analysts to founders make predictions for an AI-defined future.
It feels like we’re still in the planning phase. Early innings. But for engineering leaders, the window to start taking action is already closing.
Engineering Organizations Can’t Afford To Wait
We’ve seen time and time again that first movers often have an enormous advantage when adopting new technologies, and it’s likely that generative AI won’t be an exception. Companies that take a “wait and see” approach to tools like Copilot, Ghostwriter, Cody and CodeAssist are soon going to find themselves overtaken by competitors and startups.
This is a 100-meter dash—if you’re slow off the starting blocks, you’re going to lose.
I believe that generative AI is going to reshape engineering organizations in three specific ways:
• Changing Responsibilities: The repetitive tasks in engineering are over. Instead of spending time on the most tedious and time-consuming aspects of code generation, engineers are going to be spending more time in pre- and post-generation: developing effective prompts and reviewing AI-generated code. Engineers are going to need to zoom out and start thinking about how to solve problems and drive efficiencies with architecture improvements.
• Changing Skill Sets: If responsibilities and team structures are changing, then we’re going to see changes to employee skill sets as well. Experience and decision-making skills are going to be more valuable than ever because we’re going to need human expertise to channel all of that AI productivity. Also, with teams zooming out and making decisions on a macro level, it makes sense that teams are going to be prioritizing breadth of expertise over depth. The more languages you have a handle on, the better.
• Changing Hiring Priorities: Engineering leaders need to start recruiting for the next phase of their organization, looking for team members with exceptional creativity and big-picture thinking.
We know where this is going. Instead of standing in place and planning the journey, it’s time to start moving forward.
Capitalizing On The AI Opportunity
This isn’t a moment to panic about what AI will mean for engineering teams in the long term. If you don’t lead the technology, the technology will end up leading you. Instead of viewing AI as a threat or a challenge, view it as an opportunity.
Engineering leaders have always wanted a seat at the table and a chance to influence business decisions. I know that frustration.
With generative AI, every CEO in the world is taking a renewed interest in their engineering teams. How are they implementing code assistants and other AI-enabled tools? What is this going to mean for the business?
This is your chance to join the conversation and increase your influence. Don’t miss it.
Running Your First AI Experiment
I’m not saying you need to have a fully baked, multi-year strategy for generative AI. But it’s time to get off the sidelines and start experimenting.
Select a segment of your engineering organization that will be tasked with implementing Copilot or other AI-backed tools in their day-to-day work. Set a two- or three-month timeframe and then compare the results between the segment using AI and the control group. Is one team pushing code faster than the other? How do the defect rates compare?
Don’t just look at quantitative data—analyze the qualitative differences in their work. What tasks are the AI-enabled team focusing on compared to the control team? How has their job satisfaction changed?
You may not have a detailed plan for generative AI today, but the data from this first round of experiments will give you direction on how to implement these tools on a wider scale. This isn’t a one-size-fits-all type of technology—every team will need to find the right approach to integrate generative AI tools into their workflows and optimize performance.
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