Software Product Manager

Dematic is seeking a Product Manager, Industrial AI & Operational Intelligence to help define, deploy, and scale AI-driven capabilities within the Command Center platform. This individual will work closely with AI engineers, software development teams, implementation teams, and customers to identify practical industrial AI use cases, guide solution development, validate results in real operating environments, and ensure successful adoption across warehouse, distribution, manufacturing, and automation operations.

• Career Development
• Competitive Compensation and Benefits
• Pay Transparency
• Global Opportunities

Dematic provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.

The base pay range for this role is estimated to be $102,400-$128,000 at the time of posting. Final compensation will be determined by various factors such as work location, education, experience, knowledge, and skills.

• Identify high-value AI opportunities across warehousing, distribution, manufacturing, automation, and industrial operations.
• Translate customer pain points into well-defined AI use cases with clear hypotheses, success criteria, adoption paths, and ROI expectations.
• Create and maintain a prioritized AI use-case roadmap based on customer value, technical feasibility, data readiness, commercial potential, and deployment complexity.
• Define the business requirements, product behavior, acceptance criteria, and operational KPIs needed to evaluate AI solutions in production environments.
• Help separate meaningful customer value from AI hype by focusing on operational outcomes such as throughput, labor productivity, order fulfillment, availability, downtime reduction, inventory performance, and exception resolution.

• Work hand-in-glove with AI engineers and scientists, software architects, UX designers, and platform teams to guide product decisions and prioritize engineering work.
• Provide business context to technical teams so models, agents, analytics, and user experiences are developed around real operating problems rather than technology demonstrations
• Review solution outputs, recommendations, agent behaviors, and analytics workflows to ensure they are understandable, usable, and valuable for operational users.
• Drive alignment between AI capabilities and Command Center product strategy, including predictive visibility, operational recommendations, autonomous monitoring, root-cause analysis, optimization, and decision support.
• Partner with engineering leadership to define practical release plans, pilot readiness criteria, deployment constraints, and support expectations.

• Serve as the product lead during customer AI engagements, from discovery through pilot execution and production rollout.
• Lead customer workshops to understand operational goals, constraints, data availability, tolerance for automation, decision rights, and success criteria.
• Walk skeptical customers through the AI process in a clear and grounded way, including what the solution can do, what it cannot do, how recommendations are generated, how results should be reviewed, and what human oversight remains in place.
• Review AI outputs with customers, operations teams, and internal stakeholders to refine use cases, improve trust, and identify product enhancements.
• Support implementation teams with repeatable deployment playbooks, pilot plans, validation checklists, and adoption guidance.

• Define validation frameworks for measuring model quality, recommendation usefulness, business impact, user adoption, and customer confidence.
• Establish feedback loops from customer deployments back into product, AI engineering, data engineering, and UX teams.
• Help shape responsible AI practices for industrial deployments, including explainability, transparency, escalation paths, human review,…