Marina Martynenko
Director of Technology Infrastructure and Support
University of Michigan
Marina Martynenko is Director of Technology Infrastructure and Support at the University of Michigan’s Center for Academic Innovation, where she leads technology infrastructure, enterprise systems, studio technology, and support for Michigan Online. Before joining U-M, she spent nearly two decades at Eaton, a Fortune 100 company, leading enterprise-scale transformations across ERP, PLM, Salesforce, SAP modernization, AI, automation, and resilience. Her work focuses on turning data, platforms, and emerging technologies into trusted operational capabilities.
Scout shows that institutional AI adoption depends more on trust, governance, workflow design, human oversight, and organizational readiness than on model choice.
I built Scout, an AI-enabled hiring workflow tool, to solve a real operational problem: managing 60+ candidates across multiple open technology roles with limited recruiting support.
Scout evolved from a resume analysis script into a deployed application with role-specific candidate pipelines, AI-assisted screening, a Kanban review workflow, video introduction support, panel feedback collection, and stakeholder-facing dashboards. The technical work was significant, but it was not the hardest part.
The harder challenge was moving from prototype to institutional use: building trust in the tool, protecting candidate data, validating AI recommendations against human review, navigating governance questions, aligning stakeholders, and deciding where AI should assist versus where human judgment must remain central.
This 10-minute lightning talk shares lessons from building Scout within a large university environment and uses that experience to explore a broader point: successful AI adoption depends less on model choice than on organizational readiness, trust, workflow design, and responsible implementation.