AI Manufacturing & Technology Leader

DuPont•Minneapolis, MN

About The Position

The AI Leader for Operations will define, operationalize, and scale the use of artificial intelligence and advanced analytics across global operations to drive measurable improvements in safety, quality, delivery, inventory, asset reliability, and cost. This role serves as the primary product owner for AI in Operations, bridging operations leadership, Digital/IT, data science, and product teams to translate concrete operational problems into AI‑enabled solutions that are embedded into ways of working, adopted at scale, and sustained over time. The role is accountable for the Operations AI roadmap, product lifecycle management from intake through scaled deployment, and value realization—working within DuPont’s enterprise AI strategy, architecture, and Responsible AI guardrails.

Requirements

Bachelor’s degree in engineering, Operations, Computer Science, or related field; advanced degree (MBA/MS) preferred or equivalent experience.
10+ years of leadership experience in manufacturing operations, and AI transformation in industrial environments.
Proven experience translating operations into AI solutions that deliver value at scale.
Deep understanding of discrete, process, or hybrid manufacturing environments and the realities of plant‑floor systems and data.
Demonstrated ability to lead cross‑functional change across multiple sites or businesses; experience in diversified manufacturing strongly preferred.
Strategic thinker with a strong execution bias and the ability to simplify complexity into scalable operating mechanisms.
Highly credible with plant and operations leadership; capable of influencing from the frontline to the executive level.
Strong business and financial acumen, including strategic planning, value realization, and portfolio prioritization.
Excellent communicator who can translate effectively between operators and engineers, data and IT teams, and senior executives.

Responsibilities

Develop and execute the Operations AI roadmap aligned to business and OpEx priorities, including multi‑year sequencing across sites and regions.
Translate operational objectives into a balanced AI product backlog/portfolio with clear ROI, scalability, risk, and change‑impact criteria; identify high‑value use cases to prevent fragmentation and stop low‑value efforts early.
Own value realization and sustained business impact with measurable KPIs
Ensure successful use cases progress from pilot to productized capability to scaled, enterprise deployment with reuse across the footprint.
Lead end‑to‑end deployment of AI solutions from design through adoption, including process redesign, controls, training, and sustainment.
Embed AI into standard operating processes and decision routines with appropriate human‑in‑the‑loop controls.
Partner closely with plant leadership, supply chain, engineering, quality, EHS, reliability, and regional operations leaders to prioritize and deploy solutions.
Build and lead an Operations AI enablement team (federated model) that accelerates delivery locally while aligning to enterprise standards and guardrails.
Implement and ensure adherence to enterprise standards for governance, security, ethics, and lifecycle management for Operations AI use cases.
Establish scalable delivery playbooks and reusable assets for Operations (e.g., intake templates, deployment runbooks, validation and monitoring checklists) aligned to approved enterprise platforms and architectures.
Build AI literacy across Operations with role‑based curricula for operators, supervisors, engineers, planners, reliability, and quality teams.
Lead end‑to‑end change management and adoption, working cross-functionally with IT, HR and other functions to maintain consistency with enterprise programs.
Define new or evolving roles in Operations (e.g., AI product owners, digital reliability leads); develop internal AI talent and communities of practice to sustain adoption.