Head of Artificial Intelligence

Enterprise AI | Manufacturing | Supply Chain | Private Label | Business Transformation

Scope: Enterprise-wide, multi-site

Locations: Toronto, Wixom, Chicago and Sardis; regular site travel required

Role model: High-leverage individual contributor / transformation owner, supported by internal teams, vendors and consultants

Primary measure: Measurable business value from AI-enabled process and decision improvement

About the Company

The company is a large contract manufacturer and packager of household cleaning products serving branded and private-label customers across North America.

Position Overview

This position will define and drive the company’s practical adoption of artificial intelligence across the business. This is not primarily an IT infrastructure, software development or research role. It is a business transformation role for someone who can understand how the company makes money, how its plants and customer programs operate, where decisions and processes break down, and where AI can create measurable advantage.

The person will own the AI opportunity portfolio from discovery through value realization: identifying and sizing high-value use cases, redesigning workflows and decision processes, determining data and technology requirements, coordinating internal and external technical resources, guiding pilots into scaled deployment, building organizational capability, and measuring financial and operational results.

The role will initially operate as a high-leverage individual contributor. Success depends less on personally building every model and more on asking the right questions, selecting the right problems, challenging vendors and specialists, and ensuring solutions are adopted in the business and sustained.

Core Mandate

• Build a phased enterprise AI roadmap tied directly to company business priorities, plant economics and customer-program economics.
• Create a repeatable method for finding, evaluating and prioritizing AI opportunities based on value, feasibility, data readiness, adoption requirements and risk.
• Turn selected opportunities into implemented solutions that change how work is performed and how decisions are made, rather than producing disconnected pilots or demonstrations.
• Develop the internal operating model, governance, external partner ecosystem and AI literacy required to scale successful solutions across facilities and functions.
• Own value realization: each meaningful initiative should have a baseline, business case, accountable owner, adoption plan and measurable outcome.

Key Responsibilities

1. AI Strategy, Opportunity Portfolio & Roadmap

• Develop and maintain the enterprise AI transformation roadmap across manufacturing, supply chain, procurement, quality, R&D/formulation, commercialization, commercial/customer processes, finance and supporting functions.
• Build a decision and process inventory to identify repetitive, high-value, data-rich business situations where AI, automation or advanced analytics can materially improve outcomes.
• Prioritize use cases by expected economic value, strategic importance, feasibility, data readiness, scalability, change impact and risk; stop low-value or poorly grounded initiatives early.
• Continuously assess the AI landscape - including generative AI, agents, machine learning, computer vision, optimization, digital twins and industrial AI - and translate relevant advances into practical recommendations for the company.

2. Manufacturing & Intelligent Operations

• Work directly with plant, engineering, maintenance, quality and operations leaders to identify AI-enabled improvements in process control, batch optimization, yield, quality, line performance, changeovers, reliability, downtime, energy, waste and safety.
• Identify opportunities for predictive quality, predictive maintenance, machine vision, intelligent scheduling, digital twins and decision support where the operational data and economics justify them.
• Design pilots with clear operating baselines and success measures, then create the playbook required to replicate proven solutions across relevant sites and assets.
• Ensure AI is embedded into standard operating processes and decision routines with appropriate human oversight, rather than operating as a separate technology layer.

3. Contract Manufacturing, Customer Programs & Commercialization

• Apply AI to the full customer-program lifecycle, including opportunity identification, RFQ/quoting, formulation and product development, packaging/specification management, commercialization, production readiness, fulfillment and ongoing program profitability.
• Develop better decision support around bid/no-bid, pricing floors, capacity implications, changeover burden, working capital, forecast reliability, customer service requirements and true program economics.
• Explore AI-enabled customer and category intelligence that can identify assortment gaps, private-label opportunities, cross-sell opportunities, packaging gaps and product concepts the company can proactively bring to customers.
• Use connected data to improve launch execution, specification/artwork consistency, retailer/customer compliance, deductions/chargebacks and root-cause learning from complaints and service failures.

4. Supply Chain, Procurement & Planning

• Identify AI opportunities in forecasting, S&OP/IBP, inventory, raw-material planning, procurement, sourcing, supplier intelligence, production scheduling, warehousing and transportation.
• Improve the company’s ability to predict customer demand and forecast reliability, allocate capacity and materials, sequence production and manage working capital under uncertainty.
• Use AI to surface exceptions and recommended actions so teams can spend less time assembling information and more time resolving the decisions that matter.

5. Data, Technology & Partner Orchestration

• Define practical data-readiness requirements for AI use cases and work with IT, plant systems, Finance, Quality, R&D and other data owners to improve access, context and reliability.
• Evaluate AI platforms, specialist vendors, consultants and implementation partners based on business fit, architecture, security, scalability, interoperability, total cost and expected value.
• Make informed build-vs-buy-vs-partner recommendations and ensure the company retains appropriate knowledge, control and portability as capabilities scale.
• Act as the business product owner for AI initiatives while technical specialists own the detailed engineering, infrastructure and model construction where appropriate.

6. Governance, Risk & Responsible AI

• Establish proportionate AI governance covering privacy, cybersecurity, customer confidentiality and IP, data access, model risk, human oversight, transparency, validation and regulatory requirements.
• Create clear decision rights defining where AI may inform, recommend or execute, and where human approval remains required.
• Ensure use cases involving regulated products, quality systems or customer-sensitive information are developed and validated with the appropriate functional experts.

7. Change, Adoption & Capability Building

• Build AI literacy among executives, plant leaders, managers and frontline users through practical education tied to the company’s work rather than generic technology training.
• Create a network of functional and site champions who can identify opportunities, support adoption and help scale what works.
• Address resistance, role changes, trust and skill gaps directly; redesign processes and responsibilities where AI changes how work should be performed.
• Communicate wins, failures and lessons openly so the organization becomes progressively better at selecting and deploying AI.

8. Value Realization & Executive Reporting

• Build business cases and establish measurable baselines before meaningful investment is made.
• Track value across cost, margin, working capital, throughput, yield, quality, service, innovation speed, risk reduction and employee productivity as relevant to each use case.
• Maintain a portfolio view of AI investments, adoption and realized value, and report progress, risks and priorities to senior leadership.
• Treat adoption and sustained operating impact - not the number of pilots launched - as the primary definition of success.

Illustrative Initial Opportunity Areas

Manufacturing: Batch/process optimization; predictive quality; machine vision; maintenance/reliability; line scheduling/changeovers; energy, yield and waste.

Product & R&D: Formulation optimization; raw-material substitution; experiment prioritization; specification knowledge; faster customer/product development.

Supply Chain: Demand forecasting; customer forecast quality; inventory; procurement; supplier intelligence; production planning; logistics and working capital.

Private Label / Commercial: RFQ and quote intelligence; bid/no-bid; customer/program profitability; assortment whitespace; cross-sell; customer opportunity intelligence.

Commercialization & Quality: Launch control tower; specification/artwork consistency; complaints-to-root-cause; regulatory/product-stewardship impact analysis; customer compliance.

Knowledge & Administration: Plant/SOP/maintenance knowledge assistants; document processing; management reporting; finance and back-office workflow automation.

Candidate Profile

The strongest candidate is likely an experienced manufacturing, operations, supply-chain, analytics or business-transformation leader who has become sophisticated in AI - not necessarily a career AI researcher or software engineer. Because modern generative AI and agent technologies are new, depth of practical use and demonstrated outcomes matter more than an arbitrary number of years with “AI” in a title.

• Typically 10+ years of progressive experience in manufacturing, operations, supply chain, digital/business transformation, advanced analytics or a closely related environment.
• Demonstrated recent ownership of meaningful AI, advanced-analytics, automation or digital-transformation initiatives from problem definition through deployment and measurable business impact.
• Strong understanding of manufacturing economics and operational processes; experience in process manufacturing, CPG, chemicals, private label, contract manufacturing or other complex multi-site industrial environments is strongly preferred.
• Practical fluency in current AI capabilities, including generative AI, AI agents, machine learning, predictive analytics, optimization, computer vision and intelligent automation. The candidate does not need to personally code every solution, but must understand enough to challenge technical assumptions and vendors.
• Strong financial and business acumen: can build a credible business case, quantify value, understand trade-offs and distinguish an interesting technology from an investable business opportunity.
• Proven ability to lead through influence across plant operations, engineering, supply chain, quality, R&D, Finance, IT/data teams and senior leadership without necessarily owning those functions directly.
• Experience working with external consultants, software/platform providers and specialist technical partners; capable of making build/buy/partner decisions.
• Excellent facilitation and communication skills, from plant-floor working sessions to executive presentations.
• Comfort with ambiguity and an entrepreneurial bias toward practical experimentation, learning and scale.
• Bachelor’s degree in Engineering, Operations, Supply Chain, Business, Computer/Data Science or a related field, or equivalent practical experience. Advanced degree is beneficial but not required.
• Ability and willingness to travel regularly among company facilities as projects require.

What Success Looks Like

First 90 Days

• Visit key company sites and build a grounded view of operations, customer-program workflows, current systems/data and organizational readiness.
• Create an enterprise AI opportunity map and prioritized initial roadmap with business cases, data requirements, owners and risk considerations.
• Establish basic governance, intake/prioritization standards and the initial vendor/partner approach.

By 12 Months

• Move at least 2–3 meaningful AI use cases from pilot into sustained production use with measurable business outcomes.
• Demonstrate value in more than one area of the business, ideally including at least one plant/operations use case and one broader business-process or customer-program use case.
• Have a functioning portfolio/ROI dashboard, governance model, repeatable delivery playbook and visible executive/site engagement.
• Improve AI literacy and create a practical network of internal champions capable of supporting adoption and identifying additional opportunities.

By 12–18 Months

• Demonstrate that successful solutions can be replicated across sites, functions or customer programs rather than remaining isolated pilots.
• Show a credible, measurable pipeline of additional value and a clear view of the internal capabilities the company should build versus continue to source externally.
• Establish AI as a normal part of operational and strategic improvement - not a separate technology initiative.

Leadership Characteristics

• Business-first: Starts with the economics and the decision/process problem, not with the tool.
• Plant credible: Comfortable learning directly from operators, engineers, maintenance, quality and site leadership.
• Technically fluent, not technology-led: Understands what modern AI can do, its limitations, and the questions required to test a solution.
• Practical skeptic: Can separate value from AI hype and is willing to say no to weak use cases.
• Builder and orchestrator: Can create structure from a relatively early AI maturity level and mobilize the right internal and external resources.
• Change leader: Understands that process ownership, incentives, trust, skills and adoption often determine whether the technology creates value.
• Executive communicator: Can move comfortably from detailed working sessions to concise business cases and senior-level recommendations.