GenAI, or generative artificial intelligence, has the potential to democratize coding and save resources for organizations. However, managing contributors without coding experience is crucial. Jon Puleston from Kantar emphasizes the challenges of creating accurate synthetic personas using AI and ML techniques. Kantar’s experiment shows that achieving accurate results with synthetic data models requires a significant amount of inputs. Real human insights remain essential for market research, as natural-language generative-AI tools may not be reliable. Code optimization company TurinTech highlights the importance of governance when engaging in AI code and in-house applications.
GenAI can help junior developers sharpen their skills, but it should be used as an assistant to expertise, not a replacement. Domino Data Lab’s Kjell Carlsson notes that tools like Amazon Q may be limited in use for non-coders and caution should be taken when using AI to build apps. Streetbees’ Gavin Harcourt and Shaf Shajahan stress the importance of expertise and investment in developing with GenAI. Mendix’s Hans de Visser discusses the need for policies, practice, and governance specific to AI-assisted development.
In conclusion, while GenAI can enhance productivity and democratize coding, it is essential to consider the expertise level required for using such tools effectively. Organizations need to carefully assess the suitability of AI features for their developers and ensure proper governance to manage risks and ensure successful outcomes.