New opportunity

Lead AI Portfolio Management & Commercialization #AIDA

Singtel Group · Singapore

About this role

The Lead AI Portfolio Management & Commercialization role is a high-visibility position within Singtel's AIDA (Artificial Intelligence & Data Analytics) team. The role is responsible for managing the integrated performance of Singtel's enterprise AI portfolio, ensuring strategic alignment, and driving the commercialization of AI and Agentic AI assets. Responsibilities include: Enterprise AI Portfolio Leadership: Overseeing portfolio prioritization, governance, and resource allocation to ensure measurable business impact. Executive Narrative & Strategic Influence: Translating complex technical and portfolio data into decision-ready insights for the Chief AI Officer and senior leadership. Commercialization: Identifying AI assets for productization, developing business cases, and managing partnerships with hyperscalers and ecosystem participants. Required Qualifications: At least 10 years of experience in strategy, management consulting, enterprise transformation, or portfolio management. Proven track record of influencing senior executives in matrixed organizations and structuring ambiguous problems. Exceptional written and verbal communication skills for C-suite audiences. A working understanding of AI, data, and technology transformation is essential. Preferred Qualifications: Experience in top-tier management consulting or corporate strategy offices. Exposure to telecommunications, digital platforms, or AI commercialization. An MBA or relevant postgraduate qualification is highly regarded.

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

Skills for this role

Portfolio ManagementCommercializationStrategic PlanningManagement ConsultingEnterprise TransformationExecutive CommunicationStakeholder ManagementBusiness Case DevelopmentGo-to-Market StrategyAI StrategyData AnalyticsStructured Problem Solving

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

Enterprise AI Portfolio Governance
You must manage the integrated performance of the AI portfolio by balancing strategic impact, execution risks, and resource constraints. This requires establishing clear decision rights and an operating cadence that allows you to identify systemic issues and drive cross-functional interventions across business, technology, and finance units.
Executive Narrative Development
A core priority is translating complex technical and portfolio data into concise, decision-ready insights for the Chief AI Officer and senior leadership. You must synthesize multiple workstreams into a coherent enterprise story that frames progress, trade-offs, and recommended actions to accelerate executive alignment.
AI Asset Commercialization
You are responsible for identifying AI and Agentic AI assets with productization potential. This involves developing robust business cases, defining go-to-market strategies, and managing partnerships with hyperscalers and ecosystem participants to move concepts from exploration to structured validation.

Likely challenges to think through

Navigating Matrixed Ambiguity
The role requires driving outcomes without direct line authority in a complex, cross-functional environment. To prepare, focus on demonstrating how you have previously built consensus among competing stakeholders and managed escalations in large-scale transformation projects.
Translating Technical Complexity
You must bridge the gap between deep AI/data technical developments and business-level value realization. Prepare by practicing how to strip away technical jargon to focus on business implications, ROI, and strategic alignment for non-technical C-suite stakeholders.

Practice your answers

Technical practice

Portfolio Prioritization Frameworks

What to coverExplain how you would balance strategic impact, execution readiness, and resource constraints when managing a large-scale AI portfolio. Discuss the criteria you use to decide which initiatives to accelerate, pivot, or terminate.

Translating Technical Complexity for Executives

What to coverDescribe your process for synthesizing complex AI/data project data into decision-ready insights for C-suite leadership. Focus on how you frame trade-offs, risks, and value realization without losing necessary technical context.

AI Commercialization Strategy

What to coverWhat framework do you use to evaluate whether an internal AI asset is ready for external commercialization? Address how you assess market opportunity, go-to-market options, and the necessary business case components.

Managing Cross-Functional Dependencies

What to coverHow do you orchestrate alignment between disparate teams like legal, finance, product, and technology in a matrixed environment? Provide an example of how you resolve systemic issues that span multiple business units.

Structured Problem Solving in Ambiguity

What to coverWhen faced with an ambiguous strategic challenge in a transformation program, how do you structure your analysis to reach a recommendation? Discuss how you use fact-based analysis to challenge existing assumptions.

Ecosystem Partnership Management

What to coverHow do you evaluate the strategic value of partnering with hyperscalers versus building internal capabilities? Discuss the criteria for selecting partners to accelerate AI adoption or market reach.

Defining AI Value Realization

What to coverHow do you measure the success of an enterprise AI portfolio beyond simple ROI? Explain how you track and report on value realization to ensure leadership maintains confidence in the transformation journey.

Governance for Scaling AI

What to coverWhat governance mechanisms are most effective for scaling AI across a large organization while maintaining agility? Discuss how you balance the need for control with the need for innovation.

Behavioural practice

Influencing Without Formal Authority

What to coverDescribe a situation where you had to drive a critical decision or change in direction among senior stakeholders who did not report to you. How did you build consensus and ensure accountability?

Navigating Conflicting Stakeholder Priorities

What to coverTell me about a time you had to manage competing priorities between different business units. How did you negotiate a path forward that aligned with the broader enterprise strategy?

Handling Executive Pushback

What to coverProvide an example of when you presented a strategic recommendation that was met with resistance from senior leadership. How did you adapt your narrative or analysis to address their concerns and reach a decision?

Managing Through Transformation Ambiguity

What to coverDescribe a time you were tasked with leading a project where the scope or objectives were initially unclear. How did you define the path forward and establish the necessary structure to deliver results?

Sharpen your knowledge

Topics to prepare

Enterprise AI Portfolio Governance
Review frameworks for managing large-scale, cross-functional AI portfolios. Exercise: Create a hypothetical prioritization matrix that balances strategic impact, execution risk, and resource constraints for a multi-year AI transformation program. This is relevant to the role's requirement to oversee portfolio sequencing and governance.
AI Commercialization and Business Modeling
Study the lifecycle of moving AI assets from internal pilot to external productization. Exercise: Draft a high-level business case template for an Agentic AI asset, focusing on value proposition, go-to-market strategy, and partnership requirements with hyperscalers. This directly addresses the commercialization responsibilities mentioned in the job description.

Skills to revise

Executive Narrative Development
Practice synthesizing complex technical data into concise, decision-ready executive summaries. Exercise: Take a complex technical project status report and rewrite it as a one-page executive memo that highlights key risks, trade-offs, and recommended actions for a C-suite audience. This is critical for the role's focus on influencing senior leadership.
Structured Problem Solving
Apply consulting-style methodologies to ambiguous enterprise challenges. Exercise: Use a logic tree (e.g., MECE principle) to break down a hypothetical problem, such as 'Why is our AI portfolio failing to deliver measurable business impact?' and identify potential root causes and interventions. This aligns with the requirement to structure ambiguous problems in a matrixed organization.
Stakeholder Influence and Alignment
Develop strategies for driving outcomes without direct line authority. Exercise: Map out a stakeholder engagement plan for a cross-functional AI initiative involving Finance, Legal, and Product teams, identifying potential friction points and strategies to build consensus. This is essential for navigating the complex stakeholder environment at Singtel.

Your preparation checklist

Develop an Executive-Level Portfolio Framework
Prepare a sample framework or case study demonstrating how you would track and report on an enterprise AI portfolio. Focus on how you synthesize technical delivery, risk, and financial value into a single, decision-ready dashboard for C-suite stakeholders.
Draft a Commercialization Business Case
Create a one-page summary of a hypothetical AI asset commercialization strategy. Include your approach to evaluating market opportunity, go-to-market models, and the necessary cross-functional partnerships required to move an internal tool to an external product.
Prepare Examples of Influencing Without Authority
Identify three specific instances from your career where you navigated a complex, matrixed organization to resolve a conflict or secure alignment on a strategic priority. Be ready to explain the stakeholders involved, the trade-offs you negotiated, and the final outcome.
Formulate Strategic Questions for the AIDA Team
Prepare thoughtful questions regarding Singtel's current AI maturity, the primary bottlenecks in their existing portfolio governance, and the specific criteria they use to prioritize Agentic AI initiatives versus traditional automation projects.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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