This episode explores the challenges and evolving paradigms in AI application development, drawing from a research paper on project archetypes for AI development1. The episode examines how existing project management frameworks fall short in addressing the unique uncertainties of AI projects, leading to the emergence of a new archetype – the cognitive computing project.
Traditional Archetypes vs. the Reality of AI Development
The episode highlights four traditional project archetypes often applied to AI development, each with its own set of assumptions and limitations.
Agile Software Development: While appealing for its iterative and client-focused approach, agile methodologies struggle with the unpredictable nature of AI development, where outcomes heavily depend on data quality and model training.
Integration, Customization, Implementation: Viewing AI development as simply adapting an existing platform underestimates the complexities of data-driven AI, which requires extensive data processing and model training.
Design Thinking Project:
Though design thinking's focus on problem identification and creative solutions is valuable, AI projects often face constraints due to data availability and technical feasibility, limiting the open-ended exploration typically associated with design thinking.
Big Data Analytics:
While emphasizing data analysis is crucial, the goal of AI projects extends beyond generating insights; they aim to build functional applications, requiring skills beyond data science, such as business understanding and user interface development.
The Rise of the Cognitive Computing Project
The episode introduces the cognitive computing project as a new archetype better suited for AI development.
Key characteristics include:
• Focus on collaborative exploration: Acknowledging the iterative and unpredictable nature of AI, the project emphasizes joint efforts between the client and vendor to understand data potentials and align them with the platform's capabilities.
• Data-centric approach: Recognizing the critical role of data, the project prioritizes data understanding, preparation, and iterative model training.
• The need for a Data Consultant: Bridging the gap between business needs and data science expertise, this role ensures alignment between data insights and business goals.
Challenges and Opportunities for the Future
The episode discusses the limitations of the cognitive computing archetype, such as the need for better guidance on transitioning from exploration to exploitation, addressing knowledge gaps between business users and data scientists, and defining effective collaboration strategies. The episode concludes by emphasizing the importance of:
• Further research on AI development methodologies: This includes understanding the balance between exploration and exploitation, developing effective collaboration techniques, and defining the data consultant role more comprehensively.
• Training and education: Equipping business professionals with a basic understanding of AI and data science, while also educating data scientists on practical application challenges, will be crucial for successful AI development. This episode offers valuable insights for anyone involved in AI development, highlighting the need for new approaches and collaborative strategies to navigate the complexities of this rapidly evolving field.
https://arxiv.org/pdf/2408.04317