Generative AI Oversight: New Software Leadership Roles Emerge

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

This shift in responsibilities brings an urgency to the need to expand the scope of software leadership beyond the confines of application development and maintenance. According to Gartner analysts, applying generative AI to team management, talent management, business development and ethics will be part of oversight. Haritha Khandabattu.

While generative AI won’t replace developers, “it has the ability to automate certain aspects of software engineering,” he adds. And while it “cannot replicate human creativity, critical thinking and problem-solving abilities,” AI acts as a force multiplier that can increase efficiency.

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

“It’s managers who make all the key decisions about AI. They oversee the development and implementation of AI-based systems, managers use them to make decisions, use them to target customers, and monitor and adjust decisions, processes and routines. That’s appropriate. AI. Managers allocate resources, oversee AI projects, and manage organizations shaping the future.”

Challenges for managers include mapping AI against business strategy, promoting human-AI interfaces, as well as “focusing on data, privacy, security, ethics, labor, human rights and national security,” Barente and his co-authors noted.

Business alignment will be another key leadership ability. Industry leaders are suggesting that 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 must understand and move forward. “AI projects are not just technology projects,” he says John RoseGlobal Chief Technology Officer of Dell Technologies.

“Good ones are aligned with business outcomes. AI projects almost inevitably disrupt organizational structures and are not technical decisions. Every investment and shift to automation causes legacy jobs to disappear and new jobs to be charged with managing that automation.”

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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 experts and machine learning engineers who develop and deploy AI algorithms and neural networks,” said Brian MaddenGlobal Head of AI Marketing at AMD

“AI and its deployments are evolving at a rapid pace. A rounded approach is needed to ensure AI projects consider not only practical and technical factors, but also governance, policy and ethics.”

It’s also important to remember that AI leadership can be a team sport. While most AI efforts are typically led by the CEO, CIO or head of engineering, “employees from different departments should collaborate together, creating internal use cases to accelerate product capabilities for customers,” says Navin Jutshi, CIO of Databricks.

“Teams on the business side of the organization can work with engineers, who are under the CIO and IT, to build large internal language models that improve business processes across all departments.”

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This need for collaboration means AI’s success will depend on “open partnerships and collaborations across technology, business and society,” says AMD’s Madden.

“As AI becomes more ubiquitous in industries such as healthcare, finance and education, domain experts will be needed to provide context and insight for AI application developers. Insights will help the technology community to optimize their application of AI For optimal returns for their customer base. There will be emerging roles that bring policy experts into application development.”

In addition to line-of-business capabilities, the rise of AI will also mean an increasing focus on prompt engineering and in-context learning capabilities, said Databricks’ Zutshi: “This is a new capability for developers to optimize prompts for large tasks. Language models and AI expands the reach and capabilities of tools, creating new capabilities for customers.”

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Another area where software leaders need to lead is AI ethics. Software engineering leaders should “work with or form an AI ethics committee to develop policy guidelines that help teams responsibly use generative AI tools for design and development,” Gartner’s Khandbattu reports in its analysis. Software leaders must identify and help “mitigate the ethical risks of any generative AI products developed internally or purchased from third-party vendors.”

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

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