New opportunity

PRINCIPAL IT PROJECT MGMT

TE Connectivity · Bangalore, India

About this role

The Lead AI Program & Portfolio Manager is responsible for driving the end-to-end lifecycle of Artificial Intelligence initiatives across the business unit. This role serves as a strategic bridge between business stakeholders, technology teams, data experts, and leadership by identifying business challenges, evaluating AI opportunities, managing project portfolios, and ensuring successful delivery of AI-driven solutions that create measurable business value. Responsibilities include: AI Strategy & Opportunity Assessment: Identify, assess, and prioritize AI/ML and Generative AI use cases aligned with business objectives; conduct feasibility assessments and value realization studies; develop AI roadmaps. Business Consulting & Problem Definition: Engage with business units to understand processes and pain points; lead discovery workshops; define AI use cases, business requirements, and success criteria; build business cases and ROI analysis. Portfolio & Program Management: Manage a portfolio of AI initiatives; establish project prioritization frameworks and governance processes; monitor project health, timelines, budgets, and risks. Project Delivery Management: Lead end-to-end AI project execution from ideation through deployment and adoption; coordinate cross-functional teams including data scientists, engineers, and architects. Stakeholder Management & Executive Communication: Build relationships with senior leaders; translate technical AI concepts into business-friendly language; prepare executive presentations and status reports. Innovation & Transformation Leadership: Promote AI-driven culture; lead proof-of-concepts, pilots, and scaling initiatives; support enterprise-wide AI transformation programs. The position requires strong leadership, stakeholder management, business consulting, program governance, and AI transformation capabilities. Benefits include a competitive salary package, performance-based bonus plans, health and wellness incentives, an employee stock purchase program, and various community outreach programs.

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

Skills for this role

AI StrategyPortfolio ManagementProgram ManagementStakeholder ManagementBusiness ConsultingGenerative AIMachine LearningProject GovernanceROI AnalysisChange Management

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

Strategic AI Portfolio Alignment
The role requires establishing prioritization frameworks to ensure AI initiatives directly support business objectives. You must demonstrate the ability to evaluate feasibility and ROI for ML and Generative AI use cases to ensure resources are allocated to projects that deliver measurable value.
Cross-Functional Stakeholder Translation
Acting as a bridge between technical teams (data scientists, architects) and business leadership is critical. You must be prepared to translate complex AI concepts into business-friendly language to facilitate decision-making and secure buy-in for transformation programs.
End-to-End AI Lifecycle Management
You are responsible for the entire project lifecycle from ideation and proof-of-concepts to deployment and organizational adoption. This requires rigorous project governance, monitoring of project health, and managing dependencies across diverse business units.

Likely challenges to think through

Balancing Innovation with Operational Rigor
There is a inherent tension between the experimental nature of AI/ML pilots and the structured governance required for enterprise-wide scaling. Prepare by discussing how you maintain project momentum and budget discipline while allowing for the iterative discovery process necessary in AI development.
Driving Organizational Change and Adoption
Implementing AI solutions often requires shifting established operational processes. The challenge lies in overcoming resistance to change; prepare by highlighting your experience in change management and your approach to fostering an AI-driven culture within a large, global organization.

Practice your answers

Technical practice

AI Opportunity Prioritization Frameworks

What to coverExplain how you would build a prioritization framework to evaluate AI/ML use cases. Discuss the balance between technical feasibility, data availability, and potential ROI to ensure alignment with business objectives.

Translating AI Concepts for Executives

What to coverDescribe your approach to communicating complex AI or Generative AI concepts to non-technical senior leadership. Focus on how you translate technical risks and capabilities into business-friendly language and strategic value.

Managing AI Project Lifecycles

What to coverDetail the specific challenges of managing the end-to-end lifecycle of an AI project compared to traditional IT projects. Address how you handle uncertainty in model performance, data quality issues, and the transition from pilot to production.

ROI Analysis for AI Initiatives

What to coverHow do you define and measure success for AI projects where outcomes may be qualitative or long-term? Discuss your methodology for building business cases and tracking KPIs throughout the project lifecycle.

Cross-functional Team Coordination

What to coverHow do you manage the collaboration between data scientists, engineers, and business stakeholders? Explain your strategy for aligning these groups when technical constraints conflict with business requirements.

AI Governance and Risk Management

What to coverWhat governance processes do you implement to monitor AI project health, budget, and risks? Suggest how to maintain oversight while allowing for the iterative nature of AI development.

Scaling AI Pilots

What to coverDescribe your strategy for scaling a successful AI proof-of-concept into an enterprise-wide solution. Address the technical and organizational hurdles involved in moving from a pilot to a production-ready system.

Change Management in AI Transformation

What to coverHow do you drive adoption of new AI-driven solutions within an organization? Discuss the role of change management in overcoming resistance and fostering an AI-driven culture.

Behavioural practice

Managing Conflicting Stakeholder Priorities

What to coverDescribe a situation where you had to manage conflicting requirements from different business units regarding a shared AI portfolio. How did you facilitate a decision and maintain alignment with the overall business strategy?

Handling Project Failure or Pivot

What to coverShare an example of an AI or technology initiative that did not meet its initial objectives. How did you assess the situation, communicate the findings to stakeholders, and decide whether to pivot or terminate the project?

Influencing Without Direct Authority

What to coverProvide an example of how you influenced a cross-functional team or senior leader to adopt a specific AI strategy or process. What was the situation, and how did you build the necessary consensus?

Navigating Ambiguity in Innovation

What to coverDescribe a time you led an initiative with high levels of uncertainty or undefined requirements. How did you define the scope and success criteria to move the project forward effectively?

Sharpen your knowledge

Topics to prepare

AI Value Realization and ROI Analysis
The role requires building business cases and conducting feasibility studies. Exercise: Create a hypothetical framework for evaluating the ROI of a Generative AI implementation in a manufacturing context, focusing on how to quantify both direct cost savings and indirect productivity gains.
AI Governance and Portfolio Prioritization
The position involves managing a portfolio of AI initiatives and establishing governance processes. Exercise: Draft a prioritization matrix that balances technical feasibility, strategic alignment, and potential business impact to decide which AI projects should be fast-tracked versus those that should remain as proof-of-concepts.
Translating Technical AI Concepts for Executives
The role demands bridging the gap between data scientists and senior leadership. Exercise: Prepare a one-page executive summary for a non-technical stakeholder explaining the risks, benefits, and implementation timeline of a machine learning project, avoiding jargon while maintaining technical accuracy.

Skills to revise

AI/ML Use Case Discovery
You must lead discovery workshops to identify business pain points. Exercise: Design a workshop agenda aimed at uncovering operational inefficiencies in a business unit and mapping those inefficiencies to potential AI or machine learning solutions.
Change Management for AI Adoption
The role requires driving the adoption of AI solutions across the organization. Exercise: Develop a communication plan for a new AI-driven tool, identifying potential resistance points from end-users and outlining strategies to promote cultural buy-in and successful integration.

Your preparation checklist

Develop an AI Portfolio Prioritization Framework
Create a sample framework that demonstrates how you evaluate and rank AI/ML use cases based on business value, technical feasibility, and strategic alignment. Prepare a document or slide deck explaining how you would balance quick-win pilots with long-term enterprise transformation initiatives.
Prepare a Business Case and ROI Analysis Template
Draft a template for an AI project business case that includes cost-benefit analysis, success criteria, and KPIs. Be ready to explain how you quantify the impact of AI solutions, such as operational efficiency gains or cost reductions, for non-technical executive stakeholders.
Draft an Executive Communication Strategy
Prepare a sample executive summary or steering committee update that translates complex AI/ML technical concepts into business-friendly language. Focus on how you would report on project health, risks, and budget while maintaining alignment with senior leadership goals.
Outline a Change Management Plan for AI Adoption
Develop a brief plan outlining how you would drive the adoption of a new AI tool within a business unit. Include steps for stakeholder engagement, training, and overcoming resistance to ensure the solution delivers measurable value.
Formulate Strategic Questions for the Interviewer
Prepare thoughtful questions regarding TE Connectivity's current AI maturity, the primary business units driving AI demand, and the existing governance structures for cross-functional AI projects to demonstrate your strategic interest in the role.

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