Emerging Leadership in Generative AI Oversight: The Shift Towards Specialized Management Roles

Emerging Leadership in Generative AI Oversight: The Shift Towards Specialized Management Roles | TechCrunch

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A majority of software leaders are already – or soon will be – incorporating generative AI into their day-to-day work activities. By 2025, more than half of all software-engineering leadership role descriptions will explicitly require oversight of generative AI , according to a Gartner analysis .

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The Intersection of Generative AI and Engineering The surge of generative AI can harness tremendous potential for the engineering realm. It can also come with its challenges, as enterprises and engineers alike figure out the impact of AI on their roles, business strategies, data, solutions, and product development. What does the future roadmap look like for bringing generative AI into the software fold? ZDNET decodes from all angles. Read now

This shift in responsibilities brings an urgency to the need to extend the scope of software leadership well beyond the bounds of application development and maintenance. 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. And while it “cannot replicate the creativity, critical thinking and problem-solving abilities that humans possess,” AI serves as a force multiplier that can enhance efficiency.

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Other experts also recognize the importance of software engineering leadership positions. “The role of managers in the burgeoning societal transformation involving AI cannot be overstated,” states Nicholas Berente of the University of Notre Dame and Bin Gu of Boston University, writing in MIS Quarterly.

“It is the managers that make all key decisions about AI. They oversee the development and implementation of AI-based systems, managers use them in their decision making, leverage them to target customers, and monitor and adjust the decisions, processes, and routines that appropriate AI. Managers allocate resources, oversee AI projects, and govern the organizations that are shaping the future.”

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Challenges for managers include mapping AI against business strategies, promoting human-AI interfaces, as well as paying attention to “data, privacy, security, ethics, labor, human rights, and national security,” Berente and his co-authors point out.

Business alignment will be another key leadership capability. Industry leaders suggest AI in its leading forms – generative and operational – is not only a productivity tool for developers , but that this emerging technology also presents 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.”

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The demand for new leadership skills means IT professionals should 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.”

It’s also important to remember that the leadership of AI is likely to be a team game. 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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This demand for collaboration means the success of AI “will depend on open partnerships and collaboration across technology, business, and society,” says AMD’s 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.”

In addition to line-of-business expertise, the rise of AI will mean there is also a growing focus on prompt engineering and in-context learning capabilities. Databricks’ Zutshi says, “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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Yet another area where software leaders will 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,” Gartner’s Khandabattu reports in her analysis. Software leaders 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.”

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