Software Product Manager

KION•Grand Rapids, MI

1d•Hybrid

About The Position

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. This is not a traditional software product management role focused only on backlog grooming or roadmap administration. The role requires hands-on engagement across the full AI product lifecycle: use-case discovery, business case development, data and model readiness, pilot execution, customer validation, deployment support, change management, and commercialization. The successful candidate will be technically credible with AI and software teams, operationally credible with warehouse and manufacturing leaders, and commercially credible with customers, sales, and executive stakeholders.

Requirements

7+ years of experience in product management, solution consulting, industrial software, operational excellence, automation, supply chain technology, or a related role.
3+ years of direct experience managing software products involving AI, machine learning, advanced analytics, optimization, and generative AI.
Experience delivering technology solutions into industrial environments such as warehouses, distribution centers, manufacturing plants, material handling operations, or automated facilities.
Strong working knowledge of operational KPIs such as throughput, productivity, order fulfillment, downtime, availability, labor utilization, equipment performance, inventory accuracy, and exception management.
Ability to translate ambiguous business problems into structured use cases, product requirements, workflows, acceptance criteria, and measurable outcomes.
Strong customer-facing communication skills, including the ability to explain technical concepts to non-technical stakeholders and build trust with skeptical customers.
Demonstrated ability to lead cross-functional work across product, engineering, data science, implementation, sales, and customer teams.
Technically credible with AI engineers and software architects, without needing to be the person building the models.
Operationally credible with warehouse, manufacturing, maintenance, IT, and automation leaders.
Commercially credible with customers, sales teams, and executive stakeholders.
Comfortable operating in ambiguity and turning early-stage AI concepts into deployable, repeatable product capabilities.
Able to balance innovation with customer trust, operational risk, explainability, and adoption readiness.
Focused on measurable business outcomes rather than AI terminology, demos, or technology for its own sake.
Applicants must be authorized to work in the U.S. without the need for current or future sponsorship.

Nice To Haves

Experience with WMS, WES, WCS, MES, industrial automation platforms, digital twins, industrial IoT, telemetry, or operational analytics platforms.
Familiarity with LLMs, AI agents, ML models, MLOps, data pipelines, forecasting, anomaly detection, root-cause analysis, causal AI, and simulation.
Experience supporting pilots, proof-of-value programs, customer deployments, or post-sale adoption for enterprise software or industrial technology.
Ability to evaluate data readiness and identify practical constraints related to data quality, integration, latency, context, ownership, and operational interpretation.
MBA, engineering degree, supply chain degree, analytics degree, or equivalent practical experience.

Responsibilities

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, auditability, and appropriate limits on automation.
Monitor deployed AI capabilities and identify opportunities to improve accuracy, usability, operational fit, and customer value.
Ensure AI solutions are positioned as practical decision-support tools that augment customer operations rather than opaque black-box systems.
Support sales, solution consulting, and commercial teams with customer-facing narratives, demonstrations, pilot proposals, business cases, proof points, and value assessments.
Develop practical messaging that explains AI capabilities in operational terms for customer executives, site leaders, IT leaders, and frontline operations stakeholders.
Collaborate on packaging, pricing, deployment models, and post-sale adoption approaches for Command Center AI capabilities.
Build referenceable customer stories, repeatable use-case patterns, and lessons learned from early deployments.