Diana Kearns-Manolatos United States Ahmed Alibage United States Generative artificial intelligence success asks leaders to do more than adopt technology. They need to seamlessly collaborate across data, engineering, and business teams throughout the software development life cycle. Building on our first article in this research series, “ How can organizations engineer quality software in the age of gen AI? ,” where we examined ways to overcome software development life cycle (SDLC) challenges, this piece identifies four engineering obstacles that organizations could address to help enhance data and model quality and fully unlock gen AI’s potential, whether for SDLC use cases or beyond. 1 From the need for clear data architecture to the management of uncontrolled model drift, these challenges underscore the importance of designing robust systems that address AI’s probabilistic nature while fostering trust and consistency. In some ways, AI and gen AI are raising the bar for quality data and changing the software engineering life cycle in ways that can enable the next generation of AI and gen AI–powered applications, such as AI agents. In this second installment, we investigate how organizations can build AI and data environments that address gen AI data integration, privacy, and model accuracy […]
K&X Design and Investment Technology, LLC
Invest The Invisible Impact
K&X Design and Investment Technology, LLC
Invest The Invisible Impact
