Generative AI is Redefining the Role of Software Engineering Leaders

Generative AI (GenAI) is revolutionizing the managerial responsibilities of software engineering leaders, who must adapt to its impact on productivity, recruitment processes, and ethical considerations.

The technology is perceived primarily as a cost-reduction tool, but leaders should emphasize its role as a force multiplier that enhances team efficiency rather than replacing staff.

We spoke with Haritha Khandabattu, Sr. Director, Analyst at Gartner, about how software engineering leaders can effectively lead in the age of GenAI by reinforcing the value of their teams and demonstrating how AI can augment productivity without replacing human creativity and problem-solving skills.

Journalists who would like to speak with Haritha regarding this topic can contact matt.lodolce@gartner.com. Members of the media can reference this material in articles with proper attribution to Gartner.

A: No. While GenAI tools are highly advanced, their purpose is not to replace engineers. Instead, they are geared toward enhancing productivity. GenAI can automate repetitive tasks, but it can't replicate the creativity, critical thinking, and problem-solving skills that engineers bring to the table.

In fact, a Q4 2024 Gartner survey of 400 software engineering leaders in the U.S. and U.K. indicated that up to half of their software development teams employ various GenAI tools to augment workflows, acting as force multipliers rather than replacements.

For experienced engineers, GenAI offers guidance and scaffolding, allowing them to adapt more efficiently across various platforms and projects. Less-experienced team members can save time on routine tasks, freeing them up to focus on more complex challenges.

As software engineering leaders pilot and scale GenAI tools, the goal is to demonstrate their business value. It's not just about proving that AI can work; it's about showing how it transforms teams to drive real business outcomes. By linking technology outcomes to business goals, they will build a compelling case for continued investment in their teams.

Remember, GenAI isn't a cost-cutting tool or a staff replacement. It's a powerful ally that enhances engineering teams' efficiency. 

A: Recruiting and managing talent often involves time-consuming tasks like summarizing interview feedback, crafting job descriptions, and onboarding new hires. GenAI can be a game-changer in streamlining these processes. A Gartner survey of 487 CIOs and IT leaders in the fourth quarter of 2024 found that over 33% of respondents were already using AI to generate job descriptions.

GenAI can speed up hiring by helping to quickly identify the best candidates. Imagine using GenAI to perform a job analysis with prompts like, "What are the top skills for a platform engineering manager?" This gives software engineering leaders a solid starting point, though they'll still need to review the data. Plus, AI-driven interview intelligence platforms can transcribe and summarize interviews, saving time.

GenAI can also make onboarding smoother. AI-powered chatbots can assist new employees with FAQs and guide them through paperwork and training. This will enable them to get up to speed quickly and work on key projects sooner.

A: 

1. Skill management and development lie at the core of leaders’ responsibilities.  Software engineering leaders must work with HR to create tailored AI training programs that offer personalized learning experiences for engineers at different levels.
   
   Gartner predicts that by 2027, 70% of all software engineering leader role descriptions will explicitly require oversight of generative AI, up from less than 40% today. With this in mind, software engineering leaders must upskill their teams in large language models, prompt engineering, and more, so they can tackle new challenges.
2. Build a culture of continuous learning. Agile learning programs can lead to better business outcomes, adaptable employees, and a plan to meet evolving skills needs. The idea is to develop each employee’s GenAI skills ahead of demand. 
3. Create new ethics policies. Software engineering leaders should implement policies that assign clear responsibility across DevOps, DataOps, and ModelOps cycles. Tasks like model retraining and version rollbacks are now shared efforts between software engineering and data teams. There is a clear need to coordinate these cross-functional activities, ensuring accountability and smooth handoffs. Legal and security teams must also be involved in these efforts.

Gartner clients can learn more in Generative AI Changes Software Engineering Leaders’ Responsibilities.

About Gartner Application Innovation & Business Solutions Summit

Application innovation and software engineering strategies will be discussed at the Gartner Application Innovation & Business Solutions Summits taking place June 3-5 in Las Vegas, June 18-19 in Tokyo, and September 8-9 in London. Follow news and updates from the conferences on X using #GartnerAPPS.