Generative artificial intelligence will not just affect the deployment of technology in enterprises, but also the roles of software engineering leaders in the future.
By 2025, more than half of all job descriptions for software engineering leader roles will explicitly require oversight of generative AI, according to research and consulting firm Gartner.
Software engineering leaders will find themselves at a significant disadvantage if they do not acknowledge and adjust to these changes in the roles and responsibilities—facing the risk of being replaced by those who embrace this disruptive technology, Gartner suggested.
“Outside of generative AI’s impact on technology implementation, it also changes the managerial responsibilities of software engineering leaders,” said Haritha Khandabattu, senior director analyst at Gartner. “This includes those related to team management, talent management and code of ethics,” Khandabattu said.
Focusing On Team Value When Deploying Generative AI
When piloting generative AI projects, software engineering leaders must show the business value of utilising the technology to bolster their teams. Leaders will be able to use this to create a strong business case for continuing to invest in their teams.
Given the uncertainty around the implementation and use of generative AI, software engineering leaders must also be transparent with their teams and focus conversations on how the technology will enhance productivity of developers through a level of automation, rather than focusing on how it can substitute job roles and people.
“Generative AI will not replace developers in the near future. While it has the ability to automate certain aspects of software engineering, it cannot replicate the creativity, critical thinking, and problem-solving abilities that humans possess. Leaders should reinforce the value of their teams by demonstrating how generative AI is a force multiplier that can enhance efficiency,” said Khandabattu.
Transforming Recruitment And Talent Management
In the recruitment, hiring and talent management processes, tasks like conducting a job analysis and transcribing interview summaries can be accelerated by generative AI applications. Software engineering leaders, for instance, could put in a prompt requesting keywords or key phrases pertaining to platform engineering expertise or experience through generative AI.
By investing in generative AI, leaders can leave the routine tasks to technology and spend more time focusing on people-centric aspects of their jobs. The investment will also allow software engineering leaders to continuously upskill engineers and foster a workforce that is adaptive to incremental technologies.
“In addition to recruitment, skill management and development lie at the core of leaders’ responsibilities. AI-enabled skills management—a dynamic skills approach that helps in supporting talent and work processes—will help software engineering leaders rethink roles by identifying skills that can be combined to create new positions and eliminate redundancies,” said Khandabattu.
Setting Policy Standards For Ethical Use Of Generative AI
“The use of foundational AI models can introduce risks such as hallucinations, the generation of false, yet plausible-seeming content, and bias. Software engineering leaders need to be cautious when using this technology,” said Khandabattu.
Software engineering leaders must collaborate with—or establish—an AI ethics committee, which can help set policy standards for teams to responsibly use generative AI tools for design and development. Software engineering leaders have a crucial role in identifying and helping with the mitigation of any ethical risks associated with generative AI solutions, whether they are produced internally, or purchased from third-party vendors.
“Refrain from using generative AI to replace tasks that require human judgment and critical thinking. Constantly evaluate use cases where generative AI can add maximum value in day-to-day activities,” said Khandabattu.
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