CENTRAL RESEARCH QUESTION
Among frontline contact-center employees, which combinations of worker capability, task design, AI functionality, workflow integration, leadership, training, autonomy, transparency, human oversight, and governance produce better performance, learning, customer outcomes, employee sustainability, and responsible decision-making—and under what conditions does AI reduce rather than increase human capability or job quality?
Why this study matters
Study #007 does not assume that AI is inherently beneficial, harmful, inevitable, or a substitute for people. It distinguishes exposure from automation, assistance from managerial control, productivity from quality, short-term efficiency from long-term learning, and AI adoption from responsible use. Null, adverse, heterogeneous, and contradictory findings will narrow or reject parts of the ResolveCX AI thesis.
What the evidence currently suggests
Field evidence shows AI can improve customer-support productivity, especially for less-experienced workers; governance, oversight, learning, work intensification and skill erosion remain material boundaries.
What ResolveCX still has to learn
ResolveCX-specific AI-readiness effect, capacity-release use, failure recovery, resilience and long-term independent capability.
Why this matters to the larger thesis
AI should be treated as a system-design variable, not a standalone productivity promise.
Research status and limitations
This study is being published as ongoing research. Interviews, surveys, employer data, longitudinal evidence, validation, or replication may support, narrow, contradict, or reject the current hypotheses. Preliminary observations will not be presented as industry-wide conclusions.
Questions this study helps answer
What is contact center AI readiness?
Study 007 defines AI readiness as a sociotechnical condition involving worker capability, task fit, AI system capability, workflow integration, human authority, leadership, governance, and value alignment—not simply completion of AI training.
Does ResolveCX assume AI will improve contact center productivity?
No. The research examines both potential value and risks, including overreliance, error, surveillance, work intensification, skill erosion, inequity, and displacement. AI is treated as a system-design variable rather than a guaranteed productivity gain.