Last month, I moderated a fascinating discussion on our webinar, From Hype to Reality: Leveraging AI to Boost Engineering Productivity. It was a packed session filled with honest, practical insights from three top engineering leaders: Sakshi Garg, Head of Engineering at Hydrolix; Ryan Cooley, Director of Engineering at ProcessMaker; and Anant Gupta, SVP of Engineering and Data Science at Included Health. These are engineering leaders who’ve been in the trenches, so their perspectives are as real as it gets.
Here’s a recap of the key takeaways for those of you who couldn’t join us live (and a refresher for those who did). Let’s dive in.
1. AI Tools Are Already Transforming Engineering Workflows — But They’re Not Perfect
AI tools like GitHub Copilot, ChatGPT, and Tabnine are making waves in engineering teams, helping automate repetitive tasks and speeding up workflows. Some teams are running pilots to see how these tools can be integrated into their day-to-day processes, while others are diving straight in. That said, the tools still have their limits—especially when it comes to handling complex or nuanced problems. One thing we all agreed on: adoption requires thoughtfulness and experimentation. High-caliber software engineers are still the “grownups in the room”.
2. Documentation and Testing Are the Low-Hanging Fruit
If you’re wondering where to start with AI, documentation and testing might be your best bet. AI tools can help generate and maintain documentation (something we know often gets deprioritized), though keeping it up to date still requires some human oversight. Testing workflows, particularly unit and automated testing, are seeing great gains as well. However, don’t expect AI to solve everything—manual intervention is still crucial for edge cases and integration tests.
3. AI Adoption Requires Trust, Training, and Guardrails
Trust is key when bringing AI tools into your organization. Whether it’s securing partnerships with AI providers or carefully reviewing privacy policies, it’s important to ensure your data stays protected. On top of that, training your team to use AI tools effectively—and ethically—is non-negotiable. While AI can automate a lot, human code reviews and ethical considerations remain essential for maintaining quality and accountability.
4. The Future of Engineering: AI as an Enabler, Not a Replacement
The consensus among the panel was clear: AI isn’t here to replace engineers, but to elevate what they can achieve. By handling repetitive coding tasks, AI frees up engineers to focus on more creative, strategic work like system design and high-level problem-solving. As the tooling evolves, agentic devs could become engineers’ pair-programmers or even more junior counterparts. This shift isn’t just exciting—it’s transformative for teams that embrace it.
5. Measuring AI’s ROI: A Work in Progress
How do you measure the impact of AI on productivity? This question sparked a lot of debate. While adoption rates and metrics like tab completion usage offer some insight, it’s just as important to look at the bigger picture. Developer satisfaction, overall engineering efficiency, and team morale often tell a more complete story about whether these tools are actually delivering value.
6. What’s Next?
AI is poised to become an integral part of the entire software development lifecycle—from ideation to deployment. To stay ahead, companies will need engineers who are adaptable and comfortable collaborating with AI tools. At the same time, ethical considerations like data privacy and bias will need to take center stage as AI’s role in engineering workflows grows.
Final Thoughts
This webinar underscored an exciting but nuanced reality: AI is here to stay, and it’s already transforming how we build software. However, it’s not a magic bullet. Engineering leaders need to strike a balance between bold experimentation and responsible implementation to make the most of these tools.
I want to extend a huge thank you to Sakshi, Ryan, and Anant for sharing their candid insights. Their experiences really grounded the conversation and made it clear that we’re all figuring this out together. If you missed the session or want to revisit any part of the conversation, check out the recording of the webinar here on YouTube.
What are your thoughts on AI’s role in engineering? Let me know in the comments or feel free to reach out directly—I’d love to hear what you think.
We have a follow up webinar, “Engineering Team Metrics That Actually Matter in 2025” on March 13th at 2pm EST with engineering leaders from Dropbox, ProcessMaker, and Vibrant. Reserve your spot today!
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