As AI assumes more of the material production in knowledge work, human effort shifts toward planning, orchestration, and evaluation, all of which revolves around goals. Yet goals remain poorly represented in knowledge work tools and workflows: implicit, unexpressed, or confused with outputs. Beyond their importance for human work, clear goals are fundamental to human-AI communication and collaboration. We review research establishing the value of explicit goals, show through a review of commercial tools that existing ecosystems support goal tracking but not goal articulation, alignment, or contextual use, and use meetings as a proving ground demonstrating that upstream goal articulation produces disproportionate downstream value for both humans and AI agents. We argue that goals should be encoded as first-class abstractions that drive human-AI collaborative workflows and that generative AI's natural-language capabilities make this a uniquely opportune moment. We outline six design requirements for goal-oriented human-AI collaborative systems.
BibTeX
@inproceedings{tankelevitch2026goals,
author = {Tankelevitch, Lev and Rintel, Sean},
title = {Goals as First-Class Abstractions in Human-AI Collaboration},
booktitle = {AutomationXP26 Workshop, CHI 2026},
year = {2026},
month = {April},
publisher = {ACM},
url = {https://matthiasbaldauf.com/automationxp26/papers/AutomationXP26_paper_0982.pdf},
}