Your AI Agent Passed Every Test. Your Humans Still Rejected It. Now What? Building the Human Quality Layer for Agentic AI Adoption
The next frontier of agentic AI will not only be determined by who builds the smartest agents — it will be determined by which organizations successfully integrate AI into the way humans work, decide, create, and lead.
As AI moves from experimental tools to autonomous agents embedded in daily workflows, quality engineering must expand beyond traditional measures of performance, accuracy, and reliability. The question is no longer only "Does the AI work?" The next question is: "Can humans successfully work with the AI?"
Many technically successful AI initiatives struggle during adoption because organizations underestimate the human side of deployment: trust, resistance, changing roles, decision ownership, communication, and workforce readiness. Building AI at scale requires understanding both the technology system and the human system it enters.
This session explores the "human quality layer" of agentic AI — the organizational and behavioral factors that determine whether AI moves from impressive demonstration to meaningful adoption. Drawing from research in organizational behavior, workforce transformation, human-centered AI, and neurodiversity, we will examine how leaders and builders can design AI implementation strategies that create effective partnerships between humans and intelligent systems.
Participants will explore why employees trust or reject AI recommendations, how workflows change when humans collaborate with agents, and why the future of quality must include measuring adoption, usability, psychological safety, and human impact.
The future of AI success will not belong only to organizations that build the best agents. It will belong to organizations that build the best human + AI teams.
Key Takeaways:
Learn why trust, transparency, and workflow design are critical quality measures for agentic AI.
Explore the emerging relationship between humans and AI agents as workplace collaborators.
Identify leadership and culture factors that accelerate or block AI transformation.
About the speaker
Jill Hosmer-Jolley:
Dr. Jill Hosmer-Jolley is an educator, researcher, and speaker focused on the human side of artificial intelligence adoption. Her work explores how organizations successfully integrate AI by addressing the people, leadership, culture, and workforce changes required for technology to create meaningful impact. As a faculty member at California State University, Monterey Bay and an executive educator, Dr. Hosmer-Jolley teaches at the intersection of artificial intelligence, organizational behavior, leadership, and workforce transformation. She develops AI curriculum that prepares students and professionals to work effectively in a future where humans and intelligent systems collaborate. Her current research focuses on human-centered AI adoption, neurodiversity, organizational readiness, and the changing relationship between humans and technology. She examines why technically successful AI initiatives often struggle during implementation and how organizations can build trust, redesign workflows, and create environments where both people and AI systems thrive. Dr. Hosmer-Jolley brings a practical, applied perspective to AI conversations by bridging academic research and real-world business challenges. Her message emphasizes that the future of AI will not be determined by technology alone — it will depend on how effectively humans learn to lead, adapt, and partner with intelligent systems.
About
TestMu Conf
Testμ (TestMu) is the world’s largest virtual conference on agentic engineering and quality, built by the community, for the community. As AI reshapes how we build, test, and ship software, Testμ Conf is where you connect, grow, and lead: agentic workflows, autonomous quality, battle-tested AI playbooks, hands-on workshops, and the engineering culture driving it all.
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