AINeedsPlanner: A Workbook to Support Effective Collaboration Between AI Experts and Clients
Authors
Clients often partner with AI experts to develop AI applications tailored to their needs. In these partnerships, careful planning and clear communication are critical, as inaccurate or incomplete specifications can result in misaligned model characteristics, expensive reworks, and potential friction between collaborators. Unfortunately, given the complexity of requirements ranging from functionality, data, and governance, effective guidelines for collaborative specification of requirements in client-AI expert collaborations are missing. In this work, we introduce AINeedsPlanner, a workbook that AI experts and clients can use to facilitate effective interchange of clear specifications. The workbook is based on (1) an interview of 10 completed AI application project teams, which identifies and characterizes steps in AI application planning and (2) a study with 12 AI experts, which defines a taxonomy of AI experts’ information needs and dimensions that affect the information needs. Finally, we demonstrate the workbook’s utility with two case studies in real-world settings.
Research Questions / Practical Problems
Question signals indexed for this paper.
- 100%
From Fitting Participation to Forging Relationships: The Art of Participatory ML
CHI '24· Human-LLM Collaboration +1
- 100%
Understanding the LLM-ification of CHI: Unpacking the Impact of LLMs at CHI through a Systematic Literature Review
CHI '25· Human-LLM Collaboration +1
- 80%
Through the Lens of Human-Human Collaboration: An Configurable Research Platform for Exploring Human-Agent Collaboration
CHI '26· Human-LLM Collaboration +2
- 67%
Exploring The Impact of Proactive Generative AI Agent Roles In Time-Sensitive Collaborative Problem-Solving Tasks
CHI '26· Human-LLM Collaboration +2
- 67%
Mapping the Wizards' Path: A Systematic Review of Wizard-of-Oz in HCI
CHI '26· Participatory Design +3
- 67%
Cultural Variations in Human-AI Partnership: Initial Cross-Cultural Validation of the Transactive Memory System with GenAI (TMS-GenAI) Measurement Tool
CHI '26· Human-LLM Collaboration +2
- 67%
Human-Human-AI Triadic Programming: Uncovering the Role of AI Agent and the Value of Human Partner in Collaborative Learning
CHI '26· Human-LLM Collaboration +2
- 67%
Framing 'Collaboration': How Human-Human Principles Translate into Human-AI Realities
CHI '26· Human-LLM Collaboration +2
- 60%
Mapping Machine Learning Advances from HCI Research to Reveal Starting Places for Design Innovation
CHI '18· Human-LLM Collaboration
- 60%
Effects of Communication Directionality and AI Agent Differences in Human-AI Interaction
CHI '21· Human-LLM Collaboration +1
Based on Jaccard similarity of research subtopics & professions (≥60%)