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Global IT Category- AI Transformation Manager

Novartis · Hyderabad, India

About this role

The Global IT Category- AI Transformation Manager will define and execute the global IT category strategy and roadmap for AI-related demand across services, software, and infrastructure. This role is responsible for translating business and technology AI needs into actionable sourcing plans and driving enterprise AI adoption. Responsibilities include leading end-to-end sourcing activities, evaluating AI solutions, establishing commercial models, and managing strategic supplier relationships. The role also focuses on cost optimization, financial governance, and embedding risk controls for AI sourcing in partnership with Legal, Risk, and Security teams. Required Qualifications: Bachelor’s degree in Engineering, Computer Science, IT, Business, or Supply Chain. Minimum 10 years of experience in global IT procurement, strategic sourcing, or category management, including 3+ years specifically leading sourcing or commercial initiatives for AI, GenAI, data and analytics, or automation. Preferred Qualifications: MBA or post-graduate qualification in Supply Chain, Procurement, or Strategy. Demonstrated depth in IT sub-categories including software and infrastructure, with exposure to cloud and managed services.

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

Skills for this role

IT ProcurementStrategic SourcingCategory ManagementAI ContractingSupplier Relationship ManagementCommercial NegotiationFinancial GovernanceRisk ManagementCloud ServicesManaged Services

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Get interview-ready for this role

A focused preparation guide, built around this job’s responsibilities and requirements.

✦ AI-generated guide
Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.
01

Understand the role

What matters most

  • Strategic AI Sourcing Roadmap

    Define and execute a global IT category strategy specifically for AI, GenAI, and automation. This requires translating complex business and technical requirements into actionable sourcing plans that align with enterprise-wide AI adoption goals.

  • Commercial Model Innovation

    Develop and operationalize non-traditional contracting models, such as value-based and consumption-based pricing, to ensure AI investments deliver measurable business value and cost-effectiveness.

  • Cross-Functional Risk and Governance

    Establish robust governance frameworks for AI procurement by partnering with Legal, Security, and Risk teams to ensure compliance, data protection, and responsible supplier engagement.

  • Strategic Supplier Ecosystem Management

    Manage and influence key supplier relationships to drive innovation, continuous improvement, and performance through rigorous KPI tracking, SLAs, and quarterly business reviews.

Likely challenges to think through

  • Balancing Innovation with Risk

    The challenge lies in enabling rapid AI adoption while adhering to strict corporate risk and security controls. Prepare by demonstrating experience in navigating complex compliance environments where you successfully balanced speed-to-market with rigorous internal governance.

  • Navigating Matrix Stakeholder Dynamics

    As a global role, you must align diverse business units and technical teams on AI priorities. Prepare by highlighting your ability to influence senior stakeholders across different cultures and functions to achieve consensus on sourcing strategies.

  • Defining Value in Emerging Tech

    AI solutions often lack standardized pricing or performance metrics. Prepare by discussing your methodology for defining 'value' in ambiguous technology categories and how you have previously negotiated contracts that protect the company while incentivizing supplier performance.

03

Practice your answers

Technical practice

Designing AI-Specific Commercial Models

What to coverExplain how you would structure a contract for a GenAI solution that moves beyond traditional seat-based licensing. Discuss the trade-offs between consumption-based, outcome-based, and subscription models in the context of enterprise AI adoption.

Managing AI Supplier Ecosystems

What to coverHow do you evaluate the maturity and risk profile of emerging AI vendors compared to established IT infrastructure providers? Suggest a framework for assessing supplier capabilities in data security, model transparency, and long-term viability.

Integrating Risk and Compliance in AI Sourcing

What to coverDescribe your approach to embedding governance controls into the procurement lifecycle for AI tools. Specifically, address how you would collaborate with Legal, Security, and Risk teams to ensure compliance with data privacy and ethical AI standards.

Strategic Category Management for AI

What to coverHow do you translate high-level business AI needs into a structured sourcing roadmap? Discuss how you prioritize initiatives based on enterprise fit, cost-benefit analysis, and technical feasibility.

Financial Governance and Value Realization

What to coverWhat metrics and KPIs do you use to track the ROI of AI investments? Explain how you maintain financial governance over AI-related spend while ensuring the flexibility needed for innovation.

Negotiating Cloud and Managed Services for AI

What to coverGiven the reliance of AI on cloud infrastructure, how do you negotiate service level agreements (SLAs) that account for the unique performance requirements of AI workloads? Focus on balancing cost optimization with service reliability.

End-to-End Sourcing Execution

What to coverWalk through your process for managing an RFI/RFP for a complex AI platform. How do you ensure technical stakeholders are effectively involved in the evaluation and award recommendation phases?

Supplier Performance Governance

What to coverHow do you structure QBRs (Quarterly Business Reviews) for strategic AI suppliers to drive continuous improvement? Provide examples of how you would address performance gaps or missed innovation targets.

Behavioural practice

Influencing Cross-Functional Stakeholders

What to coverDescribe a situation where you had to align technical teams and business leaders on a complex IT sourcing strategy. How did you manage conflicting priorities to reach a consensus?

Navigating Matrix Environments

What to coverProvide an example of how you have successfully delivered a global procurement initiative within a matrix organization. How did you ensure consistent communication and buy-in across different regions or business units?

Managing Supplier Conflict

What to coverTell me about a time you had to resolve a significant dispute with a strategic supplier. What was your approach to maintaining the relationship while protecting your organization's commercial interests?

Adapting to Emerging Technology

What to coverDescribe a time you had to quickly learn and develop a sourcing strategy for a new, unfamiliar technology category. What steps did you take to build your expertise and influence the business decision-making process?

02

Sharpen your knowledge

Topics to prepare

  • AI-Specific Commercial Models

    Review the differences between traditional software licensing and AI-specific models such as consumption-based, outcome-based, and token-based pricing. Practice articulating how to align these models with enterprise value realization and cost optimization goals.

  • IT Sourcing Governance and Risk

    Study the intersection of procurement, legal, and security in the context of AI. Focus on how to embed risk controls for data privacy, model transparency, and compliance into the end-to-end sourcing lifecycle for emerging technologies.

Skills to revise

  • Strategic AI Category Management

    Draft a mock roadmap for transitioning a legacy IT infrastructure category to an AI-enabled one. Focus on how to prioritize demand, segment suppliers, and integrate AI solutions into existing enterprise software and service architectures.

  • Complex Commercial Negotiation

    Prepare to discuss your experience in negotiating non-standard IT contracts. Practice explaining how you balance aggressive cost-saving targets with the need for long-term strategic supplier partnerships in the rapidly evolving AI market.

  • Cross-Functional Stakeholder Engagement

    Develop a communication strategy for aligning technical teams (IT architecture, data science) with business stakeholders during the RFI/RFP process. Focus on translating complex technical AI requirements into actionable sourcing plans.

04

Your preparation checklist

  • Prepare an AI Sourcing Case Study

    Develop a structured walkthrough of a past project where you led the procurement of AI, GenAI, or automation solutions. Focus on your methodology for evaluating supplier capabilities, aligning technical requirements with business needs, and managing cross-functional stakeholders like IT architecture and security.

  • Define AI-Specific Commercial Models

    Prepare to discuss your experience with non-traditional contracting models. Be ready to explain how you have implemented value-based or consumption-based pricing for AI services and how you balance these models with standard IT infrastructure and software procurement practices.

  • Map Risk and Governance Frameworks

    Create a summary of how you have previously partnered with Legal, Risk, and Compliance teams to embed controls into AI sourcing. Highlight your approach to managing vendor risk, data privacy, and ethical AI considerations within a large, regulated enterprise environment.

  • Formulate Strategic Questions for the Interviewer

    Prepare insightful questions regarding Novartis's current AI maturity, the primary challenges in their existing IT supplier ecosystem, and how the procurement team currently balances cost optimization with the need for rapid AI innovation.

  • Review Global Category Management Principles

    Refresh your knowledge on global IT category strategy, including supplier segmentation, performance governance (KPIs/SLAs), and QBR processes. Be prepared to explain how you would adapt these standard procurement practices to the rapidly evolving AI technology landscape.

AI-generated from the job information · Prepared Oct 11, 2026 (UTC)Useful preparation, not a guarantee of eligibility or an offer.

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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