End-to-end responsibility for digital products and AI/Advanced Analytics use cases along the value chain (Operations, Terminals, Railcar/Asset)., Bring topics from problem definition through MVP to go-live, rollout, and adoption – with a clear focus on measurable business impact, operational transparency, and stable integration into regular operations., Transform operational pain points into value-adding data/AI products – from discovery/problem definition through MVP to go-live, rollout, and adoption., Prioritize and implement AI and Advanced Analytics solutions based on business value and transfer PoCs cleanly into stable operation (including monitoring)., Increase organizational transparency and control – e.g., through real-time status/ETA, exception handling, and disruption management., Define data flows, data quality, and interfaces (internal/external) so that integrations run without media breaks and manual workarounds/shadow IT decrease., Clearly manage backlog, scope, and priorities in tandem with IT & business departments – with decisions on goals, benefits, dependencies, and timelines., Ensure external providers/partners deliver predictably in your initiatives (deliverables, quality, acceptances, SLAs – depending on the scope)., Integrate AI Governance & Compliance-by-Design (guidelines, documentation, approvals, control points, auditability) with Legal & IT Security from the start., Make benefits visible: establish KPI/outcome tracking, measure impact, derive improvements, and make informed scaling decisions.