How to Use AI to Be a Great Engineering Leader
10 engineering leaders are sharing their real-world cases on how they are using AI to help them be better at their roles!
Intro
There was a lot of great feedback for the article: How to use AI to increase Software Development productivity. Special thanks to all of the 11 engineering leaders who shared their insights with us in that article.
If you haven’t read the article yet, it’s a must-read for everyone interested in using AI to increase Software Development productivity:
Now, we are doing a similar edition. This time, 10 engineering leaders are sharing their real-world cases on how AI is helping them to be better at their roles.
These engineering leaders are from all different sizes of companies -> from startups, mid-size companies and all the way to Big Tech.
Their roles span from Tech Leads, Staff Engineers to Engineering Managers, Directors, VPs, and CTOs.
Thanks to all of the engineering leaders who have shared their insights:
Ronald van Velzen, ML Tech Lead at Eneco,
Owain Lewis, Director, Software Engineering at Oracle,
Tarik Guney, Principal Engineering Manager at Microsoft,
Miroslaw Stanek, PL Site Leader & Engineering Director at Papaya Global,
Dr Milan Milanović, CTO at Trucking Hub,
Rafa Paez, Senior Engineering Manager,
Tanmoy Bhattacharjee, Senior Engineering Manager at PropertyGuru,
Shivam Anand, Staff ML Engineer at Meta,
Balaji Narayanan, VP of Engineering at Clari,
Joe Disharoon, CTO at OneSource Virtual.
Let’s start with the first insight!
1. Using AI as a thought partner
This was shared by Ronald van Velzen, ML Tech Lead at Eneco. He leads a Generative AI team at a large sustainable energy company in the Netherlands. With a background in mathematics and Data Science, he worked in AI for over six years, focusing on Natural Language Processing.
Before the recent AI wave, he was already building machine translation systems and speech-to-text models. So seeing the current explosion in possibilities feels both exciting and familiar for him.
His team builds AI enablers that make business processes more efficient, especially in customer experience. One key project involves summarizing millions of customer conversations to extract actionable insights. There's an interesting recursion here: they build AI products while using AI to build them.
In his day-to-day work, AI has become an indispensable tool across many areas for him. Since their projects are highly experimental, fast iteration is crucial. GenAI helps by:
Quickly generating initial code for MVPs or analysis scripts, giving a strong starting point to improve upon.
Streamlining documentation with draft READMEs.
Making test writing more efficient with generated test cases that can be expanded.
Beyond coding, he found a lot of value in using GenAI as a thought partner. He regularly uses tools like Claude, ChatGPT, and Perplexity to brainstorm presentations and other communication or to explore alternative approaches to technical challenges.
This often reveals perspectives he might have overlooked. Perhaps most valuable is GenAI's role as an on-demand knowledge base. When encountering unfamiliar technologies or concepts, he can quickly get concise explanations that help him make informed decisions without falling down in too many rabbit holes.
The result has been a more agile, informed leadership style. Exactly what he needs when working at the cutting edge of generative AI applications in enterprise settings.
2. Using AI every single day for writing documents
Shared by Owain Lewis, Director, Software Engineering at Oracle. He uses AI every single day in his role as a manager. While developers have obvious use cases like writing code, managers can benefit just as much.
Writing is a huge part of his job and AI helps him to write executive documents, technical or product one-pagers, emails, team documentation and performance summaries (turning data into a structured template).
An AI tool he’s been using for years is Grammarly. Claude is the best model overall for writing.
Quick tip from him: Make templates for your common docs and let AI help fill in first drafts.
Even though he’s not coding daily, he uses Cursor for development on side projects. It's super important for managers to keep up their technical skills - AI makes this way easier than before. Plus, you need to know this stuff to help your engineers who might not realize how much AI can boost their productivity.
Big companies have restrictions on how you use AI. Security is a major headache. You don't want sensitive information leaking through public APIs. If you work in a large enterprise company, you’ll likely be highly restricted on the AI tools you’re allowed to use in your work.
AI costs money and companies are still figuring out the budget side. There's surprising resistance from some developers he speaks to. He personally think using AI is non-negotiable for modern developers. You can write code without it, but... why would you?
His personally interested in how to use AI to improve day-to-day processes including operations. The potential here is huge.
He also mentioned that AI Hack Days work really well. Give your team time to play with AI tools, build cool stuff and then share what they have learned. It's a great way for everyone to learn together and get excited about the possibilities.
Once you start using AI in your day-to-day work, it’s hard to imagine how you’d operate without it.
3. 6 areas where AI helps to be more effective
This is from Tarik Guney, Principal Engineering Manager at Microsoft and previously an engineering leader at Adobe and Motorola Solutions.
He found out that one of the most pressing challenges in engineering leadership is scaling your time.
It’s not just about managing more people or tasks → it’s about using leverage to extend your impact in meaningful ways: enabling clarity, accelerating collaboration, and creating progress across teams and stakeholders.
In his role, he is constantly balancing strategy, execution, and people development while collaborating with senior leaders.
As Andy Grove said:
Your output as a manager is the output of your organization plus the output of the people you influence.
These are the 6 areas that AI helps him:
Aligning people faster through better communication
For him, AI has become a key partner in this. As a leader, one of his biggest forms of leverage is communication → whether it's setting direction, aligning stakeholders, or unblocking teams.
He uses AI to review and refine his messages, especially as a way to reduce organizational drag and improve alignment across technical and business stakeholders, before critical updates or tough conversations, so that he can ensure his words land well across diverse audiences.
His process → He feeds the drafted message into an AI assistant and ask it to act as a “perspective simulator”, reviewing the message as if it were an engineer, a peer manager, or a VP of Engineering.
This helps him spot assumptions he might be making, clarify technical or strategic intent, and better align with the recipient's mental model.
Recalling the critical context when people can’t
He also uses AI to review meeting summaries and code snippets when he wants a quick understanding without pulling engineers into ad hoc explanations.
Surprisingly often, AI can recall and summarize past decisions or conversations more accurately than people, especially when it's drawing from structured meeting notes or chat logs.
For instance, when revisiting a design review from a month ago, AI was able to surface the original rationale and trade-offs discussed, something even the attendees had partially forgotten.
This allows him to surface the critical context quickly, make better-informed decisions, and keep the team focused on what truly matters.
Preserving trust and unblocking teams
Another core source of leverage for him is the institutional knowledge he carries and the trust-based relationships he’s built across teams.
Trust isn't just built through personal interactions; it's built and reinforced through consistent preparedness over time, which compounds into what he thinks of as “reputation capital”.
Showing up informed and respectful of others’ time consistently is what sustains that trust. This is where AI becomes a surprising ally.
He uses AI to retrieve past context → decisions, dependencies, or past commitments. It allows him to approach those cross-team conversations with clarity and precision.
Instead of asking others to rehash prior agreements, he comes in already aligned on what was said or decided. That shows respect, reduces friction, and strengthens trust.
For example, if an engineer asks, "Did we ever decide which team owns this integration point?" or "What was the context for this design constraint?", he can feed meeting notes into AI and get a reliable summary, avoiding the need to chase down people or restart threads.
Similarly, when a peer program manager or his own manager asks for a quick refresher on how or why something was decided, he can surface the relevant thread or summary immediately.
This strengthens trust not just laterally but upward as well, showing that he’s informed, prepared, and mindful of others’ time.
Architecture and code reviews
AI also plays a role in architectural decision-making. When they are faced with a new problem, he often uses AI to search through internal documentation, Slack/Teams threads, or meeting transcripts to surface how similar problems were solved in the past, especially under similar constraints.
This helps them to avoid reinventing the wheel, make faster decisions, and surface institutional memory during solution design.
Similarly, during code reviews, AI assists him in identifying more appropriate or idiomatic usage of APIs, classes, or methods, often surfacing examples from other parts of our codebase or adjacent repositories.
This is particularly helpful when reviewing unfamiliar modules or cross-team contributions.
Automating daily tasks
He regularly uses AI to help with small but recurring technical tasks, like writing shell scripts, generating one-off commands, escaping/unescaping tricky JSON payloads, or reviewing long stack traces from exception messages to extract key insights quickly and catch details that are easy to miss in regular reviews.
These small tasks often consumed more time and attention than they should have, frequently requiring third-party tools. Over time, AI has become his go-to assistant for these needs, progressively reducing his dependence on external tools.
Distilling signals from noise
AI also helps him to reduce the cost of context switching by distilling signals from noise.
One of the most effective ways it does this is by summarizing long threads, whether in Microsoft Teams channels, email chains, or internal docs, so he can get up to speed quickly.
Instead of spending his mental energy parsing scattered updates or half-followed discussions, he can instantly grasp what decisions were made, what’s still open, and where attention is needed.
This helps him act faster and minimize the need to interrupt others for clarification.
The tools he uses
Communication, writing, and information recall
For the workflows described above, he uses a mix of ChatGPT, Microsoft Copilot, and Claude Sonnet. Since he works at Microsoft, Copilot is his primary tool for work-related tasks, especially when dealing with internal systems and documents.
He also relies heavily on Microsoft Teams, which has a fantastic Copilot integration. It enables many of the things he described above, like summarizing threads, recalling meeting context, and reducing the burden of context switching.
Outside of that, he frequently uses ChatGPT where appropriate, and occasionally turns to Claude Sonnet for more structured, exploratory tasks.
If you're exploring similar setups, be sure to review your company’s policies around data handling to ensure your tool choices align with security and compliance standards.
Coding & developer tooling
he mentioned that Cursor has been particularly helpful for tasks like code generation, refactoring, and navigating unfamiliar codebases.
At work, GitHub Copilot in VS Code is deeply integrated into his workflow and supports him throughout the day. He’s also experimenting with JetBrains Rider’s AI Assistant, which brings context-aware suggestions tailored to .NET development.
For command-line work, He’s been impressed with Warp’s AI-enhanced terminal, which streamlines writing and explaining shell commands.
He also mentioned the following:
He treats AI like a co-pilot for thinking, understanding, and translating across roles. It helps him protect his time and invest it where it matters most: people, decisions, and long-term outcomes.
Used intentionally, AI isn’t just a productivity boost, it’s a leadership multiplier.
Most importantly, it makes him significantly more productive → giving him more space for deep thinking, personal growth, and meaningful time with his family.
That’s the real win: leading better at work without sacrificing what matters most outside of it.