How engineering managers can use GenAI

Estimated reading time: 5 minutes

Generative AI is dramatically changing how software engineers work, with more than three quarters of developers using or planning to use GenAI tools in their workflow, according to Stack Overflow’s 2024 developer survey.

But what about their managers? While help with writing code is by far the most popular application of Generative AI to date, there are some interesting ways to apply its unique skills to key management tasks.

1. Reviewing code

While engineering managers might spend less time writing code, they will often be part of the review cycle. For time-pressed managers, chatbots like ChatGPT and coding assistants like Zed can massively speed up code review. At the very least, they can help triage code reviews and only flag issues that warrant a deeper code review.

Tarun Eldho Alias, co-founder and CTO of health tech startup Neem says that while they often lack the time to perform a detailed review of his direct reports’ work, copying and pasting code into ChatGPT and asking it to identify any issues can speed things along. If ChatGPT does flag something, he can send it off to his engineers to fix. Otherwise, he’s fairly confident that the code will work as needed.

2. Project management and planning

GenAI has a huge amount of potential when it comes to project management and Agile rituals.

Engineers at Neem use Linear and work in two-week cycles. Alias has been able to use ChatGPT to help prioritize tasks for the next cycle by providing the full backlog alongside some context like features requests, issues, or bug reports. ChatGPT can then suggest the most important tasks that are achievable in the next cycle. 

While GenAI might not always manage to correctly prioritize your tasks or oversee your sprints, it can be used to break more complex problems down into manageable chunks. 

3. Brainstorming and architecture design

Because large language models (LLMs) are trained on a huge corpus of data, they can be surprisingly effective brainstorming partners. Three-quarters of executives say they trust business advice from AI more than from their colleagues and friends, according to a recent survey from SAP. Microsoft CEO Satya Nadella wrote on X that he has been “thinking with AI and collaborating with other people.”

If you’re considering adding a feature to your product, you can use a chatbot to suggest the different approaches you could take. If you give it your limitations and requirements, it can help you drill down on the best option for your needs. For example, if you are looking to add live chat support to your app, it can suggest the different ways of accomplishing it, as well as their pros and cons. 

GenAI isn’t limited to solely technological problems either. If you’re weighing up whether to use a subscription model or charge a one time fee, it can help you evaluate the two options and answer any questions you have.

4. Searching technical documentation 

LLMs are effective at quickly parsing large and complicated documents. If you need to find out if a vendor or an API supports a particular feature, you can upload the technical docs directly to a chatbot and then ask it if it has the features you need. 

Similarly, it can make assessing service agreements, contracts, and other lengthy documents simpler by answering your questions about them. For example, if there are various due dates for deliverables scattered through a proposal, you can have the chatbot pull all the relevant information out and organize it into a table. Or asking ChatGPT if there’s anything non-standard about a contract before flagging it with a lawyer.

5. Writing better emails

Emails are an unfortunate fact of life, but generative AI tools can make dealing with them easier. Email applications like Gmail already have AI assistants built in, allowing anyone to jot down a few short bullet points and ask it to write a professional email. If you want to provide your engineers with instructions and resources, you can draft it however you like and have GenAI turn it into a coherent message.

These tools can also help with potentially tricky emails to important clients, investors, and other stakeholders. If you want to make sure your email hits all the right notes and has the perfect tone, you can give GenAI a draft and ask it to adjust the tone until it sounds just right.

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6. Posting to social media

An active social media presence that demonstrates your expertise can help boost your leadership profile. However, maintaining an active social media presence can feel like another low-priority item on your never ending to-do list.

Try giving a chatbot an outline and prompt it to write a post optimized for your channel of choice, complete with calls to action and all the relevant keywords and tags. Or, if you are really serious about marketing yourself, consider using a GenAI writing tool like Jasper. Using GenAI can really speed up sharing your wins, showcasing experiences, and make you more visible online.

However, you have to be careful not to just use generative AI to create a generic social media post. Originality.ai estimates that more than half of posts on LinkedIn may be written by AI, and to be honest, it often shows. You don’t want to fall into the trap of filling your profile with low-quality content.

7. Research

The days of lengthy research projects to assess competitors, market opportunities, and deep dive on an interesting technology area may be over.

ChatGPT can now search the web and summarize what it finds, while its Deep Research feature can create multi-thousand word reports on any subject you want. Similarly, Perplexity is an AI-powered search tool, and there are other more focused apps like Research AI (which is built on ChatGPT) to deliver an extensive research report.

More to come

While generative AI has had a major impact on software engineering over the past two years, there is still plenty more opportunity to apply these tools outside of the coding domain. 

The past few months have seen the release of new models like OpenAI’s o3-mini, DeepSeek’s R1, and Claude 3.7 Sonnet that are capable of more advanced reasoning. These kinds of models open up new and powerful features like AI agents. Because agents are able to work autonomously, these new developments could have a huge impact on how engineering managers work. 

Imagine being able to offload tasks like writing project updates, update a budget, or even book a flight for you onto an AI assistant. What would you do with all that time?