GenAI isn’t taking software engineering jobs, but it is reshaping leadership roles

Generative artificial intelligence (genAI) is reshaping the managerial responsibilities of software engineering leaders, according to Haritha Khandabattu, a senior director analyst at Gartner. Khandabattu said that while the technology is highly advanced, its primary function is to enhance team effectiveness and efficiency.

A Gartner survey of 400 software engineering team leaders found that up to half of their software development teams use genAI tools to augment their work, acting as a force multiplier rather than a replacement for human developers. “While genAI tools are highly advanced, their purpose is not to replace engineers,” Khandabattu said in a report released yesterday by Gartner.

Help for developers at all levels

GenAI can help experienced engineers adapt across different platforms and projects. Less experienced team members can also benefit from automating routine tasks, so they can concentrate on more complex challenges, Khandabattu said.

As software engineering leaders pilot and scale genAI tools, they should focus on demonstrating their tangible 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,” Khandabattu said.

Khandabattu reiterated that genAI is not a cost-cutting measure or a means of staff replacement but rather a potent ally in enhancing the efficiency of engineering teams.

Recruitment strategies evolve with genAI

The integration of genAI is also altering how software engineering leaders approach talent acquisition and management. Khandabattu said that traditionally time-consuming tasks such as summarizing interview feedback, writing job descriptions, and onboarding new hires can be streamlined through genAI. In fact, a Q4 2024 Gartner survey of 487 CIOs and IT leaders indicated that more than a third of respondents use AI to generate job descriptions.

Khandabattu said that genAI can expedite the hiring process by facilitating identifying top candidates. For instance, leaders can use genAI to conduct job analyses by inputting prompts like, “What are the top skills for a platform engineering manager?” While this provides a valuable starting point, Khandabattu cautioned that human review remains essential. Furthermore, AI-driven interview intelligence platforms can transcribe and summarize interviews, leading to significant time savings.

Onboarding processes can also be made more seamless with genAI. “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,” Khandabattu said. This accelerated onboarding allows new hires to become productive on key projects more quickly.

Generative AI impacts the responsibilities of a software engineering leader.
Gartner

Actions software engineering leaders should take now

To help their teams succeed in the era of genAI, Khandabattu outlined three areas software engineering leaders should prioritize:

Strategic skill management and development: Khandabattu said that skill management and development are central to a leader’s responsibilities. “Software engineering leaders must upskill their teams in large language models (LLMs), prompt engineering, and more, so they can tackle new challenges.” They also must collaborate with human resources departments to develop tailored AI training programs.

Cultivating a culture of learning: Fostering agile learning programs can lead to improved business outcomes, more adaptable employees, and a proactive plan to address evolving skill requirements, according to Khandabattu. “The idea is to develop each employee’s genAI skills ahead of demand.”

Establishing new ethics policies: To implement clear AI ethics policies, software engineering leaders should define responsibilities across DevOps, DataOps, and ModelOps cycles. Khandabattu stressed the critical role of legal and security teams in these efforts. “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.”