New opportunity

Supply Chain AI lead (SCM)

Capgemini · Chennai, India

About this role

Capgemini seeks a Supply Chain AI lead (SCM) in Chennai (ex Madras) for a permanent role. The lead will shape Agentic AI strategies and solutions for autonomous supply chains, connecting supply chain operations, AI strategy and digital transformation to improve cost, service levels, resilience and agility.

Responsibilities

Define the Agentic AI vision and roadmap for supply chain transformation. Prioritize opportunities such as autonomous planning, self-healing supply chains and intelligent order management, and promote GenAI, AI agents and decision intelligence across engagements.
Design agent-based architectures covering demand and supply planning, S&OP, logistics, order management and manufacturing. Define how agents, people and enterprise systems interact; integrate AI with ERP and supply chain platforms; and establish scalable data, model and governance frameworks.
Lead development of AI, GenAI and Agentic AI supply chain use cases. Develop transformation offerings, reusable accelerators, demos and solution artifacts; support large deal pursuits; and produce points of view on autonomous supply chains, multi-agent orchestration and AI governance. Monitor developments in LLMs, digital twins, simulation and reinforcement learning, and contribute whitepapers and client-forum presentations.
Analyze operational and high-level financial data to identify opportunities, generate insights and build business cases.

Qualifications and expertise:

At least 14 years of experience in supply chain consulting or operations, with proven experience leading digital or AI transformation. Experience in FMCG, CPG, retail or manufacturing; deep understanding of supply chain pain points and transformation levers; relevant Agentic AI certifications; and a bachelor's degree in engineering.
Expertise across end-to-end supply chain management, including planning, inventory, logistics, order-to-cash and network optimization. Experience with GenAI and LLM agent frameworks and copilots; AI/ML forecasting, optimization and simulation models; multi-agent orchestration and decision engines; and strong data architecture and analytics capabilities. Exposure to SAP AI/Joule, Microsoft AI, Databricks, AWS or GCP AI, and planning tools such as Kinaxis, o9 and Blue Yonder is sought.
Preferred: multinational exposure, an engineering degree from a Tier-1 college, and an MBA in operations, supply chain, logistics or engineering. Familiarity with AI-first or AI-hybrid commercial models is beneficial.

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

Skills for this role

Agentic AIAI agentsGenerative AILarge Language ModelsMulti-agent orchestrationDecision intelligenceMachine learningForecastingOptimizationSimulationReinforcement learningDigital twinsSupply chain managementDemand planningSupply planningSales and operations planningInventory managementLogisticsOrder managementManufacturingNetwork optimizationSupply chain consultingDigital transformationSolution architectureData architectureAnalyticsAI governanceSAP S/4HANASAP IBPSAP Joule AI assistant software programs???

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 a business-led Agentic AI roadmap
Define and prioritize supply chain use cases, including autonomous planning, self-healing supply chains and intelligent order management, against cost, service, resilience and agility objectives.
Design end-to-end agent architectures
Connect demand and supply planning, S&OP and execution across logistics, orders and manufacturing. Specify how agents, human decision-makers and ERP or supply chain platforms interact.
Lead deployable AI use cases
Lead AI, GenAI and Agentic AI solution development, supported by scalable data, model and governance frameworks. The posting also calls for forecasting, optimization and simulation expertise.
Turn solutions into client value and offerings
Analyze operational and high-level financial data to build business cases. Develop reusable accelerators, demos and points of view, support large deal pursuits, and communicate ideas through whitepapers and client forums.

Likely challenges to think through

Balancing competing supply chain outcomes
Preparation hypothesis: A use case may lower cost while affecting service or resilience. Prepare a prioritization example that states the operational baseline, trade-offs, financial assumptions and measures of success; these are preparation suggestions, not a stated Capgemini method.
Deciding where agents should act autonomously
Preparation hypothesis: Self-orchestrating workflows may be difficult to reconcile with human decision rights and AI governance. Prepare an architecture walkthrough showing which decisions agents make, when people intervene and how decisions are monitored.
Making cross-platform designs practical
Preparation hypothesis: Connecting planning and execution across ERP and supply chain tools may constrain data availability and integration choices. Prepare to explain how you would assess data readiness, define system boundaries and phase deployment without assuming a particular client stack.
Balancing reusable offerings with client-specific needs
Preparation hypothesis: Accelerators and demos must be reusable, while large deal solutions must address distinct supply chain pain points. Prepare an example that separates a reusable core from configurable elements and explains how its business case would change by client context.

Practice your answers

Technical practice

How would you prioritize an Agentic AI roadmap for supply chain transformation?

What to coverThe role calls for prioritizing autonomous planning, self-healing supply chains and intelligent order management against cost, service, resilience and agility objectives. Suggested answer focus: compare operational value, data readiness, implementation complexity and the degree of autonomy appropriate for each use case.

How would you connect demand planning, supply planning and S&OP in an AI-led solution?

What to coverThe posting spans all three planning activities and cites AI/ML forecasting and optimization. Suggested answer focus: explain how forecasts inform constrained plans, how exceptions reach S&OP decision-makers, and how you would assess planning outcomes.

What interaction model would you design for agents, people and enterprise systems?

What to coverDefining these interactions and multi-agent orchestration is an explicit responsibility. Suggested answer focus: describe agent responsibilities, decision boundaries, human approvals and how conflicting recommendations would be resolved.

How could a self-healing supply chain respond to an inventory or logistics disruption?

What to coverSelf-healing supply chains, inventory and logistics are stated areas of work. Suggested answer focus: trace the path from detecting an exception to evaluating recovery options, accounting for service, cost and operational constraints before action.

How would you design intelligent order management across order-to-cash?

What to coverThe posting identifies intelligent order management and end-to-end order-to-cash expertise. Suggested answer focus: explain which decisions could be assisted or automated, what enterprise data they depend on, and where human review remains valuable.

How would you integrate an agent-based solution with existing supply chain platforms?

What to coverThe role includes integration with ERP and planning platforms such as SAP S/4HANA, IBP, Kinaxis and o9. Suggested answer focus: discuss data exchange, ownership of operational decisions, and trade-offs between extending an existing platform and adding a separate AI capability.

What data, model and governance framework would support deployment at scale?

What to coverEstablishing scalable data, model and governance frameworks is a stated responsibility. Suggested answer focus: address data quality and lineage, model monitoring, access controls and accountability for decisions made or recommended by agents.

When would you use an LLM agent, a copilot or an optimization model?

What to coverThe posting names LLM agent frameworks, copilots, decision engines and optimization models. Suggested answer focus: distinguish language-based assistance from constrained quantitative decisions, including how each approach could be checked before use in supply chain operations.

How would you evaluate simulation, digital twins or reinforcement learning for a supply chain use case?

What to coverThe role calls for tracking these technologies and turning emerging developments into actionable opportunities. Suggested answer focus: compare their suitability for testing scenarios or policies, the evidence needed to trust results, and the limits of applying simulated outcomes to operations.

How would you build a business case for an AI-led supply chain use case?

What to coverThe role requires analysis of operational and high-level financial data to identify opportunities and prepare business cases. Suggested answer focus: connect a baseline and measurable cost, service or resilience outcomes to expected investment, uncertainty and evaluation milestones.

Behavioural practice

Tell me about a time you led a digital or AI transformation across supply chain stakeholders.

What to coverSuggested practice: choose a real situation that shows the leadership called for in this role. Explain the situation, your actions to align stakeholders and drive change, and the observable result.

Describe a time operational experts and technology teams disagreed about a solution.

What to coverSuggested practice: use a real example relevant to designing interactions among people, AI and enterprise systems. Set out the disagreement, how you handled the trade-offs, and what decision or outcome followed.

Tell me about a time you had to prioritize competing supply chain improvement opportunities.

What to coverSuggested practice: select a real example involving objectives such as cost, service or resilience. Describe the constraints, your prioritization actions and the result, including any trade-off you communicated.

Describe a time you turned a technical idea into a reusable offering or client-facing proposal.

What to coverSuggested practice: draw on a real situation involving an asset, demo, solution artifact or deal pursuit, if applicable. Explain your contribution, how you made the idea useful to its audience, and the outcome.

Tell me about a time you communicated an emerging technology opportunity to senior stakeholders.

What to coverSuggested practice: choose a real example that shows how you assessed an emerging approach rather than just promoted it. Describe the audience and situation, the evidence and limitations you presented, and the resulting decision or next step.

Sharpen your knowledge

Topics to prepare

End-to-end supply chain decisions
Revise how demand and supply planning, S&OP, inventory, logistics, order-to-cash and manufacturing affect one another. Exercise: trace how a demand spike changes inventory, fulfillment and service levels. The posting calls for end-to-end supply chain expertise and architectures spanning planning and execution.
Agentic AI transformation strategy
Compare autonomous planning, self-healing supply chains and intelligent order management. Exercise: rank these use cases for a hypothetical client by cost, service, resilience and agility impact. The role defines an Agentic AI vision and roadmap aligned to those business objectives.
Agent decision rights and governance
Revise multi-agent orchestration, decision engines and the boundaries between agent recommendations, human approvals and enterprise-system actions. Exercise: specify who can approve an order change and how that decision is recorded. The posting explicitly calls for interaction models and AI governance.
Forecasting, optimization and simulation
Revise what each method contributes to supply chain decisions and where an LLM-based agent or copilot fits. Exercise: explain which method you would use to predict demand, allocate constrained supply and test disruption scenarios. The posting seeks experience with these AI/ML approaches.
Enterprise AI integration and data foundations
Revise the data, model and governance considerations for connecting AI to ERP and planning platforms. Exercise: identify the inputs, outputs and data-quality checks for an AI-assisted planning decision using SAP S/4HANA, IBP, Kinaxis or o9 as an example. These platforms and scalable frameworks are named in the posting.

Skills to revise

Build an opportunity business case
Practice turning hypothetical operational metrics and high-level financial figures into a baseline, improvement estimate and assumptions list for an autonomous-planning use case. The posting asks for operational and financial analysis to identify opportunities and prepare business cases.
Map a multi-agent workflow
Draw a practical workflow for an order exception: agent inputs, decision-engine output, human approval, enterprise-system update and escalation path. This rehearses the posted responsibility to design agent-based supply chain architectures and define agent–human–system interactions.
Specify a platform integration
Draft an illustrative data-flow and interface specification between a planning platform named in the posting and an AI agent, including data ownership, refresh timing and failure handling. This is a preparation exercise, not a stated deliverable; it relates to the required integration and data-architecture work.
Present a reusable solution concept
Prepare a short client-facing demonstration outline for intelligent order management: the pain point, agent actions, governance controls and measurable outcome. The role involves reusable demos, solution artifacts, deal support and points of view.

Your preparation checklist

Organize evidence for the stated requirements
Prepare a concise record of relevant supply chain consulting or operations work, digital or AI transformation leadership, industry experience, engineering degree and any relevant Agentic AI certifications. The posting specifies 14+ years and experience in FMCG, CPG, retail or manufacturing; distinguish documented experience from areas you would need to discuss as gaps.
Build a transformation project walkthrough
Select one relevant example, if available, and prepare a short account of the supply chain problem, your leadership decisions, AI approach, stakeholder involvement and measured outcome. The role emphasizes leading transformation and improving cost, service, resilience or agility.
Draft an Agentic AI opportunity roadmap
For a hypothetical supply chain, rank autonomous planning, self-healing operations and intelligent order management by business value, data readiness and implementation risk. Label assumptions; this is a practice exercise based on the posting’s use cases, not a stated interview assignment.
Sketch an end-to-end agent architecture
Draw a practice design connecting demand and supply planning or S&OP to logistics, orders or manufacturing. Show agent-to-agent coordination, human decision points, ERP or planning-platform integration, and data, model and governance controls—the design areas named in the posting.
Prepare a quantified business-case exercise
Create a small illustrative analysis using operational and high-level financial inputs to estimate a baseline, potential cost or service impact, implementation costs and key uncertainties. The posting calls for opportunity analysis and business cases; identify every invented figure as an assumption.
Develop a concise solution point of view
Outline a reusable demo or solution artifact for one supply chain AI use case, including where GenAI, forecasting, optimization or simulation adds value and where it does not. This is preparation for the role’s stated work on offerings, deal pursuits and thought leadership.
Write questions for the employer
Ask which supply chain use cases and client industries are priorities, how agent decisions are governed alongside human operators, what ERP and planning integrations matter most, and how success is measured across transformation and deal pursuits.

Availability last checked: 5 Nov 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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