New opportunity

Senior Product Manager, AI Portfolio, Product, & Value

Sensata Technologies · Pune, India

About this role

The Senior Product Manager, AI Portfolio, Product & Value (Focus: Commercial) will own AI products for Sensata's commercial organization, spanning Sales, Revenue Operations, Pricing, Marketing, and Commercial Finance. This role is responsible for mapping workflows, identifying pain points, and shaping AI solutions that drive business value from pilot to production. Responsibilities: • Drive actionable product roadmaps by integrating cross-functional stakeholder inputs. • Manage product positioning, pricing strategy, and portfolio optimization. • Translate business pain points into clear product requirements, user stories, and acceptance criteria. • Own the product backlog, prioritize features, and collaborate with AI engineers, data scientists, and architects. • Test solutions, run pilots, and iterate based on user feedback to ensure adoption. • Build business cases and track realized value (e.g., revenue growth, win rate, seller productivity) with Finance. • Ensure products clear governance gates and integrate with CRM, CPQ, and ERP systems. Required Qualifications: • 7+ years in product management, digital/AI product, sales operations, revenue operations, or commercial roles, including 3+ years owning data, analytics, or AI products. • Hands-on commercial background in areas like pricing, key account management, or commercial finance. • Strong product discovery skills including workflow mapping and user research. • Proven track record of taking products from idea to production with measured adoption. • Working knowledge of CRM (e.g., Salesforce), CPQ tools, and ERP systems. • Technical depth in GenAI, ML, forecasting, and AI agents to challenge technical scope and feasibility. • Experience with Agile delivery and tools like Jira or Azure DevOps. • Ability to influence senior leadership with evidence-based prioritization. Preferred Qualifications: • Experience in B2B manufacturing, automotive, or industrial environments. • Product certification (e.g., CSPO) or Salesforce certification.

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

Skills for this role

Product ManagementAI Product StrategySales OperationsRevenue OperationsData AnalyticsWorkflow MappingUser ResearchPain-point AnalysisUser StoriesProduct Backlog ManagementGenAIMachine LearningForecastingAI AgentsCRMSalesforceCPQERPAgileJiraAzure DevOpsBusiness Case Development

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

Commercial Workflow Transformation
The primary objective is to map current workflows across Sales, Revenue Operations, Pricing, and Finance to identify specific pain points where AI can automate manual tasks or improve decision-making. Success requires translating these operational gaps into actionable product requirements and user stories.
End-to-End AI Product Lifecycle Management
You must own the product from pilot to production, ensuring that AI solutions are not just technically sound but adopted in daily work. This involves defining scope, managing the backlog, and iterating based on user feedback to ensure the product delivers measurable business value.
Evidence-Based Value Realization
You are responsible for building business cases in collaboration with Finance and tracking post-launch metrics such as revenue growth, win rates, and seller productivity. You must be able to justify the ROI of AI investments to senior leadership to secure continued support.
Technical and Governance Integration
The role requires ensuring AI products clear governance gates and integrate seamlessly with existing enterprise systems like Salesforce (CRM), CPQ, and ERP. You must possess enough technical depth in GenAI, ML, and forecasting to effectively challenge engineers and vendors on feasibility and cost.

Likely challenges to think through

Balancing Technical Feasibility with Business Needs
A common challenge in AI product management is managing expectations between business stakeholders and technical teams. Prepare by demonstrating how you have previously challenged technical scope or cost assumptions while maintaining alignment with business objectives.
Driving Adoption in Established Commercial Teams
Introducing AI into sales and pricing workflows can face resistance from users accustomed to legacy processes. Prepare by discussing your approach to user research, pilot testing, and change management to ensure that new AI tools are actually integrated into daily routines.
Navigating Complex Enterprise Governance
Integrating AI into critical systems like CRM and ERP involves strict governance and compliance requirements. Prepare by highlighting your experience working with IT, security, and governance teams to ensure products meet enterprise standards without stalling development.

Practice your answers

Technical practice

Mapping Commercial Workflows for AI Integration

What to coverDescribe your methodology for mapping complex sales or revenue operations workflows to identify high-impact AI opportunities. Focus on how you distinguish between manual tasks that are ripe for automation versus those requiring human-in-the-loop decision support.

Defining MVP Scope for AI Products

What to coverExplain your process for defining the first version of an AI product, specifically how you determine the minimum viable scope while ensuring it delivers measurable value. Discuss how you establish acceptance criteria that balance technical feasibility with user adoption needs.

Prioritizing AI Features with Engineering Teams

What to coverHow do you manage trade-offs when prioritizing features with data scientists and AI architects? Provide an example of how you use technical constraints, such as data availability or model latency, to inform your product roadmap decisions.

Measuring Realized Business Value

What to coverDetail your approach to building a business case for an AI initiative, including how you establish a baseline and track post-launch metrics like seller productivity or win rates. Suggestion: Focus on your collaboration with Finance to ensure metrics are credible and aligned with business goals.

Integrating AI into Enterprise Systems

What to coverDiscuss the challenges of integrating AI solutions into existing CRM, CPQ, or ERP ecosystems. Address how you navigate governance gates and ensure data quality when connecting AI outputs to core commercial platforms like Salesforce.

Challenging Technical Feasibility

What to coverHow do you leverage your knowledge of GenAI, ML, and AI agents to challenge vendor proposals or internal engineering estimates? Explain how you assess the cost-benefit ratio of different technical approaches to ensure the solution is scalable.

Iterating Based on User Feedback

What to coverDescribe your strategy for running pilots and gathering feedback from commercial users to drive product adoption. Explain how you decide whether to scale, pivot, or stop a project based on pilot performance data.

Managing AI Product Backlogs

What to coverHow do you structure your product backlog in an Agile environment (e.g., Jira or Azure DevOps) to manage both new feature development and ongoing operational support? Discuss how you balance technical debt with the need for rapid delivery of commercial value.

Behavioural practice

Influencing Senior Leadership with Evidence

What to coverDescribe a situation where you had to convince senior stakeholders to support a specific AI product direction. Focus on how you used data and evidence-based prioritization to overcome skepticism or competing business priorities.

Navigating Cross-Functional Collaboration

What to coverProvide an example of how you aligned disparate stakeholders—such as Sales, Finance, and IT—around a shared AI product goal. Explain how you managed conflicting requirements or expectations to keep the project moving forward.

Handling Competing Demands

What to coverTell me about a time you had to manage multiple high-priority tasks in a fast-paced environment. How did you determine which tasks to prioritize and how did you communicate these decisions to your team and leadership?

Taking Accountability for Product Outcomes

What to coverDescribe a project where the initial results did not meet the expected business value. How did you identify the root cause, what corrective actions did you take, and what was the final outcome?

Sharpen your knowledge

Topics to prepare

AI-Driven Commercial Value Realization
The role requires tracking realized business value (e.g., revenue growth, win rate, seller productivity) post-implementation. Exercise: Draft a framework for measuring the ROI of an AI-based 'next-best-offer' tool, specifically defining baseline metrics, target KPIs, and the methodology for attributing improvements to the AI solution versus other market factors.
AI Product Governance and Integration
Products must clear governance gates and integrate with CRM, CPQ, and ERP systems. Exercise: Outline a checklist for evaluating a new AI feature against enterprise governance requirements, focusing on data privacy, model transparency, and technical compatibility with existing Salesforce and ERP architectures.
Technical Scoping for AI and ML
You must challenge engineers on the feasibility and scope of GenAI, ML, and AI agents. Exercise: Prepare a brief critique of a hypothetical AI agent project, identifying potential pitfalls in data quality, model latency, or integration complexity that could derail a pilot-to-production transition.

Skills to revise

Workflow Mapping and Pain-Point Analysis
The role emphasizes mapping current commercial workflows to identify where AI can remove manual work. Exercise: Select a common sales operation process, such as lead routing, and create a visual workflow map that highlights specific bottlenecks where AI intervention would provide the highest impact.
Evidence-Based Prioritization
You must influence senior leadership using data-driven arguments. Exercise: Create a mock prioritization matrix for a commercial AI portfolio, using criteria like 'potential revenue impact,' 'technical feasibility,' and 'user adoption effort' to justify a 'scale, pivot, or stop' decision for a specific feature.
Agile Backlog Management
The role requires translating business needs into user stories and acceptance criteria within Jira or Azure DevOps. Exercise: Write three high-quality user stories for a pricing intelligence tool, ensuring each includes clear acceptance criteria that define the 'first version' scope for a pilot launch.

Your preparation checklist

Prepare a commercial AI project case study
Develop a structured walkthrough of a past AI product you launched, specifically focusing on a commercial use case like sales operations, pricing, or revenue forecasting. Highlight how you mapped the workflow, identified the pain point, and measured the business value (e.g., revenue growth or productivity gains) post-launch.
Demonstrate technical fluency in AI and ML
Prepare to discuss your experience challenging technical scope with engineers. Be ready to explain how you evaluate the feasibility of GenAI, machine learning, and AI agents, and how you ensure these solutions integrate effectively with enterprise systems like Salesforce, CPQ, or ERP.
Draft a sample business case framework
Create a template or outline for a business case that you would present to Finance. Include sections for baseline metrics, target value, implementation costs, and a clear adoption plan, demonstrating your ability to justify AI investments to senior leadership.
Refine your workflow mapping methodology
Prepare to explain your process for conducting user research and workflow mapping with non-technical stakeholders like account managers or commercial finance teams. Show how you translate these qualitative insights into actionable user stories and clear acceptance criteria.
Formulate strategic questions for the interviewers
Prepare insightful questions regarding Sensata's current AI governance gates, the maturity of their commercial data infrastructure, and how they currently balance pilot projects with long-term product scaling to demonstrate your strategic mindset.

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