“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. Software engineering leaders will find themselves at a significant disadvantage if they do not recognize and adapt to these changes – facing the risk of being replaced by those who embrace this disruptive technology.”
Generative AI Requires Software Engineering Leaders to Focus on Their Team’s Value
When piloting generative AI, software engineering leaders must demonstrate the business value of using generative AI to augment their teams. This will help software engineering leaders build a compelling business case for ongoing investment in their teams.
Software engineering leaders must also be transparent with their teams and focus conversations on how AI technology will enhance developer productivity, rather than focusing on how it will replace staff.
“Generative AI will not replace developers in the near future,” said Khandabattu. “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.”
Generative AI Transforms How Software Engineering Leaders Recruit and Manage Talent
Generative AI applications can speed up recruitment and hiring tasks, such as performing a job analysis and transcribing interview summaries. For example, software engineering leaders can enter a prompt requesting keywords or key phrases related to skills or experience for platform engineering.
Software engineering leaders can also invest in generative AI to allocate more time to focus on the people-centric aspects of their role. Investing in generative AI technologies will allow software engineering leaders to continuously upskill engineers and cultivate an adaptable workforce.
“In addition to recruitment, skill management and development lie at the core of leaders’ responsibilities,” said Khandabattu. “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 redundances.”
Generative AI Introduces Ethical Concerns That Require New Policies
“The use of foundational AI models can introduce risks such as hallucinations, the generation of false yet plausible-seeming content, and bias,” said Khandabattu. “Software engineering leaders need to be cautious when using this technology.”
Software engineering leaders must work with, or form, an AI ethics committee to create policy guidelines that help teams responsibly use generative AI tools for design and development. Software engineering leaders play a key role in identifying and helping to mitigate the ethical risks of any generative AI products that are developed in-house or purchased from third-party vendors.
“Refrain from using generative AI to replace tasks that require human judgement and critical thinking,” said Khandabattu. “Constantly evaluate use cases where generative AI can add maximum value in day-to-day activities.”