AI Workforce Transformation Lead

ABOUT THE COMPANY
EZ STAK is a leading global manufacturer of innovative storage solutions for commercial vehicles and workspaces. Driven by a strong commitment to quality, efficiency, and customer satisfaction, EZ STAK delivers expertly engineered products supported by streamlined operations that ensure consistent production, reliable delivery, and exceptional service.

THE OPPORTUNITY
AI. Automation. People.
At EZ STAK, we're looking for an AI & Workforce Transformation Lead to help us unlock the potential of AI across our manufacturing operations while ensuring our people remain at the center of the journey.

You'll identify high-impact opportunities, lead implementation of AI and automation initiatives, and help shape the skills, roles, and ways of working that will define our future. If you're excited by the challenge of turning technology into real business results, while building trust and adoption along the way, we want to hear from you.

The ability to travel to our facility in Watertown, NY on a semi regular basis is required.

KEY RESPONSIBILITIES
AI Strategy and Governance

• Own the AI and automation roadmap: where the business invests, in

what sequence, and on what data foundation.

• Establish AI governance covering acceptable use, data handling and

confidentiality, tool procurement, vendor risk, and management of
unsanctioned tool use.

• Set clear boundaries on employee-facing AI — transparency, appropriate

use in people processes, and where the business chooses not to deploy
it.

• Advise the Chief People Officer and executive team, translating

capability into business terms and separating genuine opportunity from
vendor noise.

• Build the business cases, including capital requests, that justify

investment.

• Identify AI opportunities beyond the plant floor — planning, quality,

back office and people operations — and prioritize them honestly.

Delivery

• Maintain a prioritized use-case pipeline, each item carrying a baseline

metric, an estimated payback and an honest data-readiness assessment.

• Own the data foundation AI depends on — machine connectivity,

historian, MES/ERP integration, data quality and labelling — specifying
what is needed and driving it to completion with IT and Engineering.
Job Description

• Scope, select and manage external integrators, vendors and platforms,

holding them to measurable outcomes.

• Deliver end to end, including sensor and instrumentation gaps, SOP

changes and adoption follow-through.

• Partner closely with production, quality and maintenance leadership, coowning

targets so improvements stick.

• Kill or defer initiatives that do not hold up, and explain why.
• Report results in operational and financial terms, not model metrics.

Workforce Impact

• Assess the workforce implications of every initiative before it is

approved: which roles change, which tasks are automated, what new
skills are required, and what it means for headcount and job quality.

• Partner with HR and Learning & Development to build reskilling and

upskilling pathways ahead of deployment, not after.

• Design the change management and communication approach for each

rollout, including being straight with people about what is and is not
changing.

• Build operator and supervisor trust in deployed systems; treat resistance

as information about the design rather than an obstacle to overcome.

• Work with employee representatives on consultation where applicable.
• Track adoption and job-quality outcomes alongside efficiency metrics,

and report both.

The First 12 Months

• Months 1–3: Assess current state across plant systems, data availability

and workforce readiness. Build relationships with production, quality,
maintenance and IT. Deliver a prioritized roadmap with data-readiness
and workforce-impact assessments.

• Months 4–6: Stand up AI governance and the acceptable-use policy.

Close the highest-impact data gaps. Begin first delivery with its change
and training plan.

• Months 7–12: Deliver two initiatives with measured efficiency results

and demonstrated adoption. Establish a repeatable intake, evaluation
and benefits-tracking process.

POSITION REQUIREMENTS
Essential Experience

• Five or more years applying AI, analytics or automation in a

manufacturing or industrial setting, including hands-on delivery.

• Demonstrated efficiency wins the candidate can describe with real

numbers — baseline, intervention, measured result and cost.

• Direct experience leading the people side of technology change:

training, job redesign, communication and building frontline buy-in.

• Working fluency with plant-floor data: PLC/SCADA tags, historians,

MES/MOM, OEE capture, and quality and downtime data.

• Practical understanding of where AI genuinely applies in manufacturing,

and where it does not yet.

Assets

• Continuous improvement background (Lean, Six Sigma), which pairs

naturally with the people-centred approach of this role.

• Experience establishing AI or data governance from scratch.
• Experience building a data or automation capability from a low starting

point.

• Familiarity with emerging AI regulation, particularly as it applies to

employment decisions.

• Experience working across multiple sites, or across the Canada–U.S.

border.

Skills & Knowledge
AI, Data and Technical:

• Practical understanding of where AI genuinely applies in manufacturing

— vision inspection, yield and scrap analysis, scheduling optimization,
predictive maintenance — and where it does not yet.

• Working fluency with plant-floor data: PLC/SCADA tags, historians,

MES/MOM, OEE capture, and quality and downtime data.

• Sufficient understanding of IT/OT architecture and industrial security to

specify requirements and partner credibly with IT.
Job Description

• Data foundation development: connectivity, integration, data quality

and labelling.

• Benefits measurement — baselining, attribution and reporting results in

operational and financial terms.

Workforce and Change:

• Job redesign and workforce impact as