A new role emerges for software leaders: Overseeing generative AI

Generative AI will be a common part of software work in the near future, and not just for code generation. A majority of software leaders will soon be incorporating generative AI into their day-to-day work, a recent analysis out of Gartner predicts. 

By 2025, more than half of all software engineering leader role descriptions will explicitly require oversight of generative AI, the consultancy estimates. This brings an urgency to extending the scope of software leadership well beyond the bounds of application development and maintenance. 

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Team management, talent management, business development, and enforcing ethics will be part of generative AI oversight, according to Gartner analyst Haritha Khandabattu. While generative AI will not replace developers, it has the ability to automate certain aspects of software engineering,” she adds. While it “cannot replicate the creativity, critical thinking and problem-solving abilities that humans possess,” it serves as a force multiplier.

Industry leaders agree that generative AI is not only a productivity tool for developers, but also represents business opportunities that software leaders need to understand and push forward. “AI projects aren’t just technology projects,” says John Roese, global chief technology officer at Dell Technologies. “The good ones are aligned to business outcomes. AI projects almost inevitably interrupt organizational structures and those aren’t technical decisions. Every investment and shift to automation causes legacy jobs to disappear and creates new jobs charged with making that automation operate.”

Expect an expansion of the teams in which software leaders participate or lead. “AI breakthroughs have given rise to a new level of technical expertise such as AI specialists and machine learning engineers who develop and deploy AI algorithms and neural networks,” says Bryan Madden, global head of AI marketing at AMD. “AI and its deployment are evolving at a rapid pace. AI projects need a rounded approach to make sure not only are practical and technological factors considered, but that governance, policy, and ethics are also following suit.”

While most AI efforts are generally led by the CEO, CIO, or head of engineering, “employees from various departments should collaborate together, building internal use cases to accelerate product capabilities for customers,”  says Naveen Zutshi, CIO of Databricks. “Teams from the business side of the organization can work with engineers, those under the CIO, and IT to build internal large language models that improve business processes in all departments.”

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Accordingly, the success of AI “will depend on open partnerships and collaboration across technology, business and society,” says Madden. “As AI becomes more ubiquitous across industries such as healthcare, finance, and education, there will be a need for domain experts to provide context and insights for AI application developers. Those insights will help the technology community hone their application of AI in the best way for the best return for their customer base. There will be roles emerging that bring policy experts into the realm of application development.”

There is also a growing emphasis on prompt engineering or in-context learning, says Zutshi. “This is a newer ability for developers to optimize prompts for large language models and build new capabilities for customers, further expanding the reach and capability of AI tools.”

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Another area where software leaders need to take the lead is AI ethics. 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,” Khandabattu reports in her analysis. They will need to identify and help “to mitigate the ethical risks of any generative AI products that are developed in-house or purchased from third-party vendors.”

Recruiting, developing, and managing talent will also get a boost from generative AI, Khandabattu adds. Generative AI applications can speed up recruitment and hiring tasks, such as performing a job analysis and transcribing interview summaries. For example, software leaders “can enter a prompt requesting keywords or key phrases related to skills or experience for platform engineering.” In addition to recruitment, generative AI supports skills management and development. “This will help software engineering leaders rethink roles by identifying skills that can be combined to create new positions and eliminate redundancies.”