PPO AI Enablement & Vendor Experience

Damco Spain SL; Mumbai; On-site; INR 1,500,000 - 2,800,000; Full time

Job summary

Maersk is seeking an AI Enablement Leader to drive agentic AI across ASSP, from identifying use cases to data cataloguing and production deployment. You will prototype with Claude Code and Copilot, coordinating with enterprise architects and engineering managers to bring AI agents into production.

You will own data requirements, ensure data quality, and establish governance while partnering with business leads to demonstrate measurable value through defined KPIs and regular reporting.

Qualifications

• Experience in product ownership, business analysis, or process improvement within a procurement, supply chain, or finance operations environment — you understand the workflows these agents will be automating.
• Demonstrable hands-on experience using AI tools (Claude, Copilot, ChatGPT or equivalent) to solve real work problems — not just awareness, but active daily use.
• Ability to work with data: comfortable reading datasets, assessing quality, and having informed conversations with data and engineering teams without needing to be a data engineer yourself.
• Strong stakeholder communication — able to translate between business pain points and technical requirements, and to hold a conversation with both a process owner and an architect in the same day.
• Experience tracking and articulating business value — you know how to define a KPI, measure a baseline, and report progress credibly.

Responsibilities

• Identify AI use cases with owners and process leads.
• Define problem statements, business value, and adoption assumptions.
• Maintain AI use case pipeline aligned to roadmap and priorities.
• Prepare business cases and value evidence for investments.
• Define data requirements and catalogue entries for each use case.
• Assess data availability and quality, close gaps with data owners.
• Co-own technical design and interface requirements for AI use cases.
• Prototype and develop AI agents using Claude Code and Copilot.
• Ensure shared standards across platforms and prevent duplication.
• Own business readiness and change management for AI launches.
• Track KPIs for AI initiatives and report progress to leadership.
• Support cross-platform vendor and analytics initiatives.

Skills

Product ownership

Business analysis

Process improvement

Procurement workflows

Supply chain

Finance operations

AI tools usage

Stakeholder communication

Tools

Claude Code

Microsoft Copilot

ChatGPT

Job description

Develops AI Automation and Agents for ASSP

Role Purpose

As PPO AI Enablement, you will drive the delivery of agentic AI across ASSP — from identifying the right use cases and ensuring the data foundations are in place, through to building, launching, and tracking the value they create. You will work at the intersection of business, data, and technology: translating process pain points into well‑scoped AI opportunities, ensuring data is catalogued and ready before any agent is built, and coordinating with enterprise architects and engineering managers to bring agents into production. Critically, you will be expected to use tools such as Claude Code and Microsoft Copilot to prototype and develop AI agents directly, reducing dependency on scarce engineering capacity and enabling the team to move at pace.

Core Accountabilities

1. AI Use Case Development & Pipeline

• Partner with use case owners and Global Process Leads to identify, qualify, and size AI opportunities across ASSP workflows.
• Define clear problem statements, expected business value, and adoption assumptions for each use case, ready for investment decisions.
• Maintain and sequence an AI use case pipeline aligned to the ASSP AI Transformation roadmap and OP priorities.
• Support the Head of AI Enablement in preparing Operational Planning artefacts, ensuring business cases and value assumptions are credible and well‑evidenced.

2. Data & Catalogue Enablement

• For each AI use case, define the data requirements (sources, fields, quality thresholds, refresh frequency) needed for the agent to function reliably.
• Own the delivery of use‑case‑curated Data Catalogue entries as a mandatory gate before any AI agent proceeds to POC or build — ensuring field‑level definitions, data quality scores, known anomalies, and lineage documentation are in place.
• Assess data availability and quality against requirements, identifying gaps and working with system data owners to close them.
• Support the ASSP Master Data governance agenda, contributing to quality standards and evolution roadmap for priority domains.
• Maintain a live data dependency register across all active AI initiatives, surfacing risks to delivery timelines proactively.

3. Technical Delivery & Hands‑On AI Development

• Co‑own the technical design of AI agents with enterprise architects and engineering managers, ensuring alignment to Maersk's architecture standards.
• Define and document functional and non‑functional requirements (APIs, data flows, integration patterns) for each AI use case.
• Use Claude Code, Microsoft Copilot, and equivalent AI development tools to prototype, iterate, and develop AI agents directly — operating as a builder, not only a specification writer.
• Secure the infrastructure, tooling, and engineering capacity needed for each build phase, working through architect and EM relationships.
• Ensure that AI capabilities across Source‑to‑Contract, Procure‑to‑Pay, and Analytics platforms follow shared technical standards and avoid duplication.

4. Adoption & Change Readiness

• Own the business readiness plan for each AI agent launch, including stakeholder alignment, process change, training, and communication.
• Work alongside the respective productivity partner to ensure business teams are prepared and motivated to adopt new AI‑enabled ways of working.
• Identify and mitigate adoption risks early, translating these into actionable plans before go‑live.

5. Value Tracking

• Define and own both business KPIs (e.g. FTE hours saved, error reduction, cycle time improvement) and technical KPIs (e.g. model accuracy, automation rate, latency) for each AI agent.
• Build and maintain a value scorecard per AI initiative, ensuring progress is measurable and visible to senior stakeholders.
• Produce regular, transparent reporting on AI value realisation for the Head of AI Enablement.
• Lead post‑launch reviews to capture lessons learned and feed these back into future use case design.

6. Vendor Portal & Cross‑Platform Support

• Support the Vendor Portal product roadmap from a business and data engagement perspective, ensuring use case owners and external‑facing teams are aligned on incoming changes.
• Provide expertise to Vendor Portal and Analytics initiatives where AI enablement, data quality, or technical integration is a factor.

Key Interfaces

• Head of Vendor Experience & AI Enablement – Direct line manager; align on priorities, use case sequencing, and elevate blockers.
• Enterprise Architects & Engineering Managers – Technical counterparts for agent design, infrastructure, and delivery.
• Corporate Platforms Architects – Counterparts for corporate AI tools, governance, and standards.
• System Data Owners – Data access, quality, and governance.
• Use Case Owners & Global Process Leads – Business counterparts for each AI initiative.
• Productivity Partner – Collaborate on adoption, change readiness, and quantified productivity improvements.
• Platform PPOs (Source‑to‑Contract, Procure‑to‑Pay, Analytics) – Coordinate cross‑platform dependencies.

Skills & Experience

Required

• Experience in product ownership, business analysis, or process improvement within a procurement, supply chain, or finance operations environment — you understand the workflows these agents will be automating.
• Demonstrable hands‑on experience using AI tools (Claude, Copilot, ChatGPT or equivalent) to solve real work problems — not just awareness, but active daily use.
• Ability to work with data: comfortable reading datasets, assessing quality, and having informed conversations with data and engineering teams without needing to be a data engineer yourself.
• Strong stakeholder communication — able to translate between business pain points and technical requirements, and to hold a conversation with both a process owner and an architect in the same day.
• Experience tracking and articulating business value — you know how to define a KPI, measure a baseline, and report progress credibly.

Preferred

• Exposure to or curiosity about agentic AI concepts — prompting, retrieval‑augmented generation, tool use, agent orchestration. You don’t need to have built a production agent, but you should want to.
• Hands‑on experience with Claude Code, GitHub Copilot, or similar coding‑assist tools to prototype or develop lightweight solutions.
• Familiarity with data catalog concepts, data lineage, or master data governance.
• Experience in a transformation or platform product context within a large, matrixed organisation.

Mindset

• Comfortable operating in ambiguity — the AI landscape is moving fast and the playbook is being written as we go.
• Builder mentality: you look for ways to close gaps yourself before escalating, and you’re not waiting for a perfect brief to start.
• Collaborative by default — this role sits at the intersection of business, data, and tech, and succeeds through influence rather than authority.

Measures of Success

• A well‑maintained, credible AI use case pipeline with clear business value articulation.
• Data Catalogue entries completed as a gate for every AI agent before build commences.
• AI agents delivered with technically sound architecture, validated by architects.
• High adoption rates for launched AI agents, with business teams actively using new capabilities.
• Business and technical KPIs defined and tracked for every AI agent in production.
• OP artefacts and value cases meet quality standards and are submitted on time.

Growth Trajectory

Over an 18–24 month horizon, this role is expected to evolve toward a broader product leadership position — with potential ownership of a major platform, domain, or transformation programme as the individual builds a track record of delivering measurable AI‑enabled business value and demonstrates capability across the full delivery cycle.

Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements.

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