Wolfe, D. A., Choe, A., & Kidd, F. · arXiv (cs.CY) · September 2025
Why do 95% of enterprises report no measurable profit impact from AI, despite massive investment? This paper argues the problem is paradigmatic, not technical. We propose a 2×2 framework that maps AI strategy along two dimensions - degree of transformation and treatment of human contribution - surfacing four dominant patterns in practice and an underexplored frontier: collaborative intelligence. The research identifies three mechanisms required to reach that frontier (complementarity, co-evolution, and boundary-setting) and reframes AI transformation as an organizational design challenge, not a technology deployment problem.
AI Strategy
Wolfe, D., Price, M., Choe, A., Kidd, F., & Wagner, H. · arXiv (cs.CY) · October 2025
What actually drives employees to adopt AI at work - and what holds them back? We surveyed 2,257 professionals across global regions and organizational levels within a multinational consulting firm, using an extended UTAUT framework that reintroduces affective dimensions like attitude, self-efficacy, and anxiety. The findings challenge common assumptions: demographics explain limited variance in adoption, but emotional and cognitive responses to AI - particularly anxiety and performance expectancy - vary meaningfully across organizational contexts. The results make the case for integrating affective and organizational factors into how we design AI rollouts, rather than treating adoption as a training problem.
AI StrategyMethods
Wolfe, D. & McClean, C. · Avanade Insights · January 2025
Trust is the variable most organizations overlook in AI implementation. This article examines what shapes employee trust in AI systems and how that trust connects to workplace experience. Rather than treating trust as a binary (people either trust AI or they don't), the research explores how trust is built, eroded, and designed for - and why getting it right determines whether AI tools get adopted or abandoned.
Trust & CalibrationAI Strategy
