Published on December 11, 2023, 12:21 pm

The rapid growth of artificial intelligence (AI), especially in the form of generative AI, is shaping not only how developers work but also how they collaborate within their organizations. With the integration of generative AI technology, software developers need to adapt and collaborate with diverse teams across different functions.

AI has the potential to significantly impact jobs, productivity, and teamwork. Studies suggest that by 2030, AI could amplify worker productivity by up to four times. This transformation presents immense opportunities for organizations to leverage AI effectively.

To fully capitalize on AI’s potential, various professionals from different disciplines should participate in its implementation and utilization. Luis Flynn, senior manager for AI and analytics at SAS, emphasizes the importance of cross-functional teams consisting of domain experts, developers, data scientists, and business analysts who understand how to tailor AI solutions to specific industries.

Mahesh Saptharishi, CTO of Motorola Solutions, highlights the broad range of skills required to maximize the benefits of AI. Coaching and development skills, understanding human behavior and decision-making processes, and effective communication will become increasingly critical in IT roles. Collaborating closely with business stakeholders is essential for refining prompt-engineering techniques as better models are developed.

As AI becomes more prevalent in workplaces, collaboration among team members will also evolve. Just as programmers frequently use code from other sources today, relying on bots to generate code won’t be much different. However, there will be a higher demand for product management skills that bridge the gap between users’ requirements and developers’ expertise.

While generative AI holds promise in reshaping software development and other professional roles within organizations, it remains an emerging technology. The boundaries for effective cross-functional collaboration are still being defined. Developers must proceed with caution when using tools like ChatGPT due to the inherent flaws in human knowledge reflected by current AI capabilities.

Flynn suggests that responsible vetting by experienced developers is crucial to harnessing generative AI effectively. Scrappy data scientists, data engineers, and business analysts can greatly enhance their productivity with the aid of generative AI. However, there is still progress to be made before fully realizing its potential.

Developers need to acquire certain skills to thrive in an increasingly AI-intensive world. Organizational data literacy, coupled with ambition, resourcefulness, and a curious problem-solving approach, will be instrumental in succeeding with AI implementations. Moreover, cross-business collaboration is vital for enforcing compliance, ensuring ethical use of AI applications, advocating for user experience design, and simplifying complex tools like ChatGPT.

The democratization of AI is a significant responsibility for developers and IT professionals. They must ensure that AI technologies are safe, accessible, and beneficial for all users. Flynn draws comparisons to the evolution of the metaverse and how barriers like complicated virtual reality setups limited initial adoption. Developers who understand human factors will know how to seamlessly integrate generative AI into everyday workflows by considering time and space constraints.

While many roles within IT will remain relatively unchanged with the introduction of generative AI tools, professionals can expect increased productivity as powerful tools become more easily accessible. Generative AI platforms like ChatGPT present tremendous opportunities for boosting efficiency and innovation in organizations across various industries

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