New opportunity

AI Transformation Lead - Supply Chain

Resmed · Singapore

About this role

The AI Transformation Lead – Supply Chain drives the adoption and scaling of AI across Resmed’s supply chain organization. The role connects supply chain, digital, data and technology teams to turn high-value business opportunities into practical AI solutions with measurable outcomes.

Responsibilities

Define a supply chain AI roadmap, prioritize use cases and manage an initiative portfolio from ideation and pilots through deployment, scaling, monitoring and optimization.
Lead proofs of concept with data scientists, engineers, digital teams and business stakeholders. Guide the deployment of AI, machine learning, generative AI and automation solutions, and track the business value they deliver.
Improve the data quality, accessibility and governance needed for scalable solutions. Establish AI governance, guardrails and responsible AI practices that support secure, compliant adoption.
Develop playbooks, standards and operating models; lead workshops, training and an AI community of practice. Manage stakeholder engagement and organizational change, communicate outcomes to executives, and evaluate emerging technologies, platforms, tools and vendors with enterprise architecture teams.

Preferred qualifications and experience:

A bachelor’s degree in supply chain management, engineering, computer science, data science, business, information systems or a related field; an advanced degree is preferred.
5+ years leading AI, digital transformation, advanced analytics, automation or technology-enabled business initiatives in supply chain, operations, manufacturing or related domains.
Experience taking AI, machine learning, generative AI or intelligent automation solutions from concept to production; establishing AI governance and adoption frameworks; and leading cross-functional teams and stakeholder engagement.

Nice to have: Experience in large global organizations with advanced AI, digital or supply chain capabilities; supporting enterprise AI centers of excellence, innovation teams or transformation programmes; or leading AI or data science teams. The position is listed in Singapore, Singapore.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

AI strategyMachine learningGenerative AIIntelligent automationSupply chain managementDigital transformationAdvanced analyticsAI governanceResponsible AIData governanceData qualityStakeholder managementChange managementCross-functional leadershipAI adoptionVendor evaluation

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Set the supply chain AI roadmap
Identify and prioritize high-value use cases against business and operational priorities, then manage the initiative portfolio from ideation through monitoring and optimization.
Turn pilots into measurable outcomes
Lead proofs of concept with data scientists, engineers, digital teams and business stakeholders; guide solutions toward deployment and scaling while tracking operational improvements and business value.
Build foundations for responsible scale
Work with data and business teams on data quality, accessibility and governance, and establish AI guardrails and responsible practices for secure, compliant adoption.
Embed adoption across teams
Lead stakeholder engagement and change management; develop playbooks and operating practices, facilitate training and a community of practice, and communicate results to executives.
Align technology choices with enterprise standards
Evaluate emerging AI technologies, platforms, tools and vendors with enterprise architecture and technology teams in support of the supply chain AI strategy.

Likely challenges to think through

Balancing value against readiness
A likely difficulty is that a promising use case may have weak data or an unclear path to operational value. Prepare to explain how you would assess expected impact, data readiness and delivery effort before committing portfolio resources.
Moving beyond a successful pilot
A proof of concept may validate an approach without proving it can be deployed, governed and monitored at scale. Prepare an example of how you would set production criteria, assign ongoing ownership and measure value after launch.
Maintaining momentum within guardrails
Cross-functional delivery may require trading speed against security, responsible AI controls and enterprise standards. Prepare to describe how you would involve business, data and technology stakeholders early, resolve competing priorities and communicate decisions.

Practice your answers

Technical practice

How would you prioritize use cases for a supply chain AI roadmap?

What to coverThe role calls for a roadmap aligned with business objectives and operational priorities. Suggested preparation: explain how you would compare expected value, feasibility, data readiness and delivery risk, then revisit priorities as evidence changes.

How would you manage an AI initiative portfolio from idea through scaling?

What to coverThe posting assigns ownership of the portfolio across ideation, deployment, scaling, monitoring and optimization. Suggested preparation: describe decision points for continuing, changing or stopping initiatives, including who would own outcomes after deployment.

How would you design and assess a supply chain AI proof of concept?

What to coverThe role leads pilots with data scientists, engineers, digital teams and business stakeholders. Suggested preparation: define the business problem, an agreed baseline, validation criteria and the evidence needed before committing to production.

When would you choose machine learning, generative AI or automation for an opportunity?

What to coverThe posting covers all three approaches and expects measurable operational improvements. Suggested preparation: compare their suitability for the task, data needs, reliability, oversight and maintenance rather than assuming an AI model is always necessary.

What data issues would you address before scaling an AI solution?

What to coverImproving data quality, accessibility and governance is an explicit responsibility. Suggested preparation: discuss how you would identify gaps with data and business teams, assign ownership and test whether the data remains fit for the intended use.

How would you establish responsible AI guardrails without stalling delivery?

What to coverThe role establishes governance for secure, compliant and scalable adoption. Suggested preparation: explain proportionate controls for access, review, human oversight and ongoing accountability, while distinguishing proposed practices from any unstated Resmed policy.

How would you monitor and improve an AI solution after deployment?

What to coverThe initiative lifecycle in the posting includes monitoring and continuous optimization. Suggested preparation: cover operational performance, data changes, user feedback and clear triggers for investigation or revision.

How would you demonstrate business value to supply chain leaders and executives?

What to coverThe role tracks realized value and communicates outcomes against supply chain priorities. Suggested preparation: distinguish pilot results from sustained impact, explain the baseline and account for adoption and operating costs.

How would you evaluate an AI platform or vendor with enterprise architecture teams?

What to coverEvaluating platforms, tools and vendor capabilities with architecture and technology teams is a stated duty. Suggested preparation: compare fit to use cases, integration, security, governance, scalability and long-term support, including trade-offs.

What would you put in an AI delivery playbook and operating model?

What to coverThe role develops standards and ways of working to improve delivery and accountability. Suggested preparation: describe reusable steps, decision rights and review points that help teams move consistently from opportunity assessment to sustained use.

Behavioural practice

Tell me about a time stakeholders disagreed on a technology initiative’s priorities.

What to coverThe role bridges supply chain, digital, data and technology teams. Suggested preparation: use a real situation, your actions to surface trade-offs and reach a decision, and the resulting alignment or unresolved issue.

Describe a time you led a cross-functional pilot through a difficult decision.

What to coverLeading AI pilots and proofs of concept across disciplines is central to the posting. Suggested preparation: give the situation, your actions in coordinating expertise and deciding whether to proceed, and the result.

Tell me about a time you addressed resistance to adopting a new way of working.

What to coverThe role includes stakeholder engagement, change management and sustainable adoption. Suggested preparation: explain the situation, how you listened and adapted your approach, and what changed as a result.

Describe a time you helped others build a shared technical capability.

What to coverThe posting calls for workshops, training and an AI community of practice. Suggested preparation: describe the audience and need, your actions to make learning useful in practice, and evidence of the result.

Tell me about a time you balanced innovation with governance or risk.

What to coverThe role calls for AI guardrails while pursuing business value. Suggested preparation: use a real situation, explain your actions in weighing risks and involving the right stakeholders, and state the outcome and what you learned.

Sharpen your knowledge

Topics to prepare

Supply chain AI prioritization
The posting calls for a roadmap aligned with business and operational priorities. As a revision exercise, compare three hypothetical supply chain opportunities by expected value, data readiness, delivery effort and risk; explain which you would prioritize.
AI solution lifecycle
The role owns initiatives from ideation and pilots through production, scaling, monitoring and optimization. Revise the decision criteria for moving a hypothetical use case between those stages, including when to stop a pilot.
Data readiness and governance
The posting emphasizes data quality, accessibility and governance as foundations for scalable AI. For a hypothetical use case, identify the data dependencies and distinguish a quality issue from an access or governance issue.
Responsible AI and controls
The role establishes guardrails for secure, compliant adoption while balancing risk and business value. Revise how you would assess risks and assign review points for a proposed AI, generative AI or automation solution; no specific framework is prescribed.
Adoption and operating models
The posting calls for playbooks, standards, consistent ways of working and a community of practice. Sketch how those elements could support accountable delivery and sustained adoption across supply chain, data, digital and technology teams.

Skills to revise

Build a use-case portfolio
Practice producing a one-page roadmap for hypothetical supply chain AI opportunities, with priorities, dependencies, owners and review points. This mirrors the advertised responsibility to maintain an initiative portfolio.
Plan a proof of concept
Draft a brief for a hypothetical pilot: business problem, participating teams, validation criteria, measurable outcome and production decision. The role leads proofs of concept with data scientists, engineers, digital teams and business stakeholders.
Define value measures
Practice stating a baseline, target, measurement period and operational measure for a hypothetical initiative, then summarize the result in a short executive update. The posting requires tracking and communicating realized business value.
Facilitate stakeholder alignment
Rehearse a workshop agenda that elicits supply chain priorities, surfaces data and delivery constraints, and records decisions. Facilitation, cross-functional leadership and change management are explicit parts of the role.
Evaluate platform or vendor options
Create a comparison sheet for hypothetical options covering fit to the use case, architecture alignment, governance, scalability and risks. The posting calls for evaluation with enterprise architecture and technology teams; it names no required platform.

Your preparation checklist

Map evidence to the preferred experience
Prepare a one-page index of relevant examples covering cross-functional leadership, supply chain or operations transformation, production deployment, AI governance, and adoption. Note your role and documented outcomes in each example.
Draft a sample supply chain AI roadmap
As a preparation exercise—not a proposed Resmed plan—rank two or three hypothetical use cases by business value, data readiness, delivery effort, and risk. Show how you would manage the portfolio from pilot through scaling and monitoring.
Rehearse a concept-to-production walkthrough
Prepare one project story covering the business problem, stakeholder decisions, proof-of-concept validation, production handoff, monitoring, and measured results. Distinguish your contributions from the team’s.
Sketch a data and responsible-AI review
For the hypothetical use cases, list data-quality and access checks, governance decisions, security and compliance questions, and guardrails you would resolve before scaling. The role explicitly calls for these practices.
Prepare an adoption playbook example
Outline how you would align supply chain, digital, data, and technology teams, then support adoption through a workshop, training, or community of practice. Include a way to gather feedback and assign ongoing ownership.
Practice an executive value update
Create a brief sample update with a baseline, outcome measures, risks, decisions needed, and next steps. Be ready to explain how you would verify value rather than equating a completed pilot with business impact.
Frame a platform and vendor evaluation
Prepare a comparison template for candidate AI tools or vendors covering enterprise fit, data controls, scalability, integration, and operating responsibilities. This reflects the role’s collaboration with enterprise architecture and technology teams.
Bring targeted questions for the employer
Ask which supply chain priorities shape the roadmap, how initiatives move from pilot to production, who owns data and AI governance decisions, and how realized value and adoption are measured.

Availability last checked: 4 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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