Date of Award
7-2026
Document Type
Dissertation
Degree Name
Doctor of Business Administration (DBA)
Department
Bisk College of Business
First Advisor
Abram Walton
Second Advisor
Shellie Halstead
Third Advisor
Ronda Smith
Fourth Advisor
Carlos E. Otero
Abstract
Organizations are committing resources to artificial intelligence (AI) without a systematic way to determine which work it can perform, and misallocation follows. Measurement has advanced on the capability side, quantifying what AI systems can do, but task-level measures of how amenable organizational work is to AI remain scarce. This study developed and validated AI-ability, defined as the degree to which a work task is amenable to augmentation or replacement by AI. An exploratory sequential mixed methods design demonstrated the construct and its dimensions from subject matter experts through grounded theory coding, then administered the resulting task-level instrument to 172 job incumbents across Functional, Project Management, and Technical roles at a global enterprise resource planning implementation firm, and to an independent AI expert panel. Task interdependence emerged as the strongest structural boundary on AI-ability, inversely associated with it. Cognitive complexity, long emphasized in automation narratives, showed a weaker association that did not persist once interdependence was controlled, suggesting that the boundary in this setting was relational rather than cognitive. No task was rated low on both dimensions, indicating that work performed independently was generally judged amenable to AI. Verb type patterned consistently with both dimensions, and AI experts rated tasks more amenable on average than incumbents. A composite algorithm scored from task structure alone reproduced incumbent judgments with cross-validated rank accuracy of .71. The study contributes an operational definition, a validated task-level instrument, a scoring algorithm, and a replicable method for guiding AI adoption, workforce planning, and ROI prioritization.
Recommended Citation
Shah, Natalie Nisha, "Strategic Technology Road Mapping: A Methodological Framework for Determining the AI-ability of Work and Human Interactions" (2026). Theses and Dissertations. 1655.
https://repository.fit.edu/etd/1655
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