New opportunity

Senior Director, AI Strategy & Support Innovation

Sprinklr · Gurgaon, India

About this role

Role Overview: Sprinklr is seeking a visionary Senior Director, AI Strategy & Support Innovation to lead the transformation of the Support organization through Artificial Intelligence. This leadership role serves as the primary connection point between Support, Product, Engineering, Data Science, and business stakeholders to drive AI-enabled growth and operational efficiency. Responsibilities: Define and execute a multi-year AI strategy and roadmap for the Support organization. Identify emerging AI technologies to improve customer outcomes and employee productivity. Partner with cross-functional teams to influence product roadmaps and accelerate AI innovation. Own the Support AI portfolio, including business requirements, adoption strategies, and operational readiness. Establish KPIs, executive dashboards, and ROI models to measure business impact. Develop governance frameworks for responsible AI, ensuring compliance and risk management. Required Qualifications: 10+ years of experience in Support Operations, Product Management, Customer Experience, Business Transformation, or AI Strategy. 5+ years of experience leading cross-functional programs at scale. Demonstrated success in defining and executing AI or automation strategies. Strong understanding of support operating models and metrics. Proven ability to influence senior executives. Bachelor's degree or equivalent practical experience. Preferred Qualifications: Experience in SaaS or enterprise software environments. Background in AI/ML initiatives. Familiarity with implementing solutions like Agent Assist, Generative AI, Conversational AI, Intelligent Case Routing, and Knowledge Intelligence. MBA or advanced technical degree.

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

Skills for this role

AI StrategySupport OperationsProduct ManagementDigital TransformationGenerative AIConversational AIIntelligent AutomationStakeholder ManagementROI ModelingResponsible AIChange 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

Define and Execute Multi-Year AI Roadmap
The role requires creating a long-term strategic vision for AI-powered support, including intelligent automation and self-service. You must demonstrate the ability to prioritize high-value initiatives that align with organizational goals while balancing immediate operational needs with future innovation.
Cross-Functional Influence and Alignment
As the primary connection point between Support, Product, Engineering, and Data Science, you must effectively advocate for support requirements. Success depends on your ability to influence product roadmaps and ensure enterprise AI investments directly address support-specific challenges.
Establish Governance and Responsible AI Frameworks
You are responsible for building frameworks that balance innovation with risk management and compliance. This involves partnering with Legal and Security teams to develop policies that ensure AI deployments meet organizational standards for safety and ethics.
Quantify Business Impact and ROI
You must define KPIs and build robust ROI models to measure the effectiveness of AI initiatives. This requires translating technical improvements in productivity and case resolution into clear, executive-level reporting that demonstrates tangible business value.

Likely challenges to think through

Managing Organizational Change and Adoption
Implementing AI-driven workflows often faces resistance from teams accustomed to legacy processes. Prepare by highlighting your experience in change management, specifically how you have successfully driven user adoption and operational readiness for new technologies.
Balancing Innovation with Operational Stability
Introducing AI into a live support environment carries the risk of disrupting customer outcomes. You will need to demonstrate a methodical approach to piloting and scaling solutions that maintains service quality while driving efficiency.
Navigating Complex Stakeholder Landscapes
Aligning diverse teams like Data Science and Product with Support operations can lead to conflicting priorities. Prepare to discuss strategies for building consensus among senior executives and ensuring that enterprise-wide AI initiatives do not overlook the nuances of support workflows.

Practice your answers

Technical practice

Developing an AI-Enabled Support Roadmap

What to coverDescribe your methodology for creating a multi-year AI strategy for a support organization. Focus on how you prioritize initiatives based on business value, technical feasibility, and impact on customer experience metrics like cost-to-serve and resolution efficiency.

Measuring ROI for AI Initiatives

What to coverExplain how you build ROI models for AI-driven support tools. Discuss the specific KPIs you track to demonstrate success, such as productivity gains, quality improvements, and the reduction of manual case handling.

Implementing Responsible AI Governance

What to coverHow do you establish governance frameworks that balance rapid innovation with risk management and compliance? Address how you collaborate with Legal, Security, and Product teams to ensure AI deployments meet enterprise standards.

Integrating AI into Support Workflows

What to coverDiscuss your experience implementing specific AI solutions like Agent Assist, Conversational AI, or Intelligent Case Routing. Focus on the operational readiness plans required to ensure these tools are successfully adopted by support teams.

Influencing Product Roadmaps

What to coverAs a bridge between Support and Product/Engineering, how do you translate complex support challenges into actionable product requirements? Provide an example of how you have successfully influenced a product roadmap to accelerate AI innovation.

Change Management for AI Transformation

What to coverWhat strategies do you use to drive organizational adoption of new AI-enabled tools? Explain how you manage resistance to change and ensure that support staff are effectively trained and integrated into new AI-driven workflows.

Assessing Emerging AI Technologies

What to coverHow do you evaluate the potential impact of emerging AI technologies on support operations? Describe your process for piloting and scaling new solutions while maintaining operational stability.

Optimizing Knowledge Intelligence

What to coverExplain how you leverage AI to improve knowledge management and self-service capabilities. Discuss the trade-offs between automated content generation and the need for human-verified accuracy in a support context.

Behavioural practice

Influencing Senior Stakeholders

What to coverDescribe a situation where you had to secure executive buy-in for a high-stakes AI or digital transformation project. Detail how you communicated the strategic value and addressed concerns regarding risk or investment.

Managing Cross-Functional Conflict

What to coverProvide an example of a time you had to align competing priorities between Support, Product, and Data Science teams. How did you navigate the conflict to ensure the project remained on track?

Leading Large-Scale Transformation

What to coverTell me about a time you led a significant organizational change at scale. What was the situation, what specific actions did you take to manage the transition, and what was the measurable result?

Navigating Ambiguity in AI Strategy

What to coverDescribe a time when you had to define a strategy in an area with rapidly evolving technology and high uncertainty. How did you determine the right path forward, and how did you adjust your plan as new information became available?

Sharpen your knowledge

Topics to prepare

AI Governance and Responsible AI Frameworks
Review frameworks for balancing innovation with risk management, compliance, and ethical AI deployment. This is critical for the role's requirement to establish governance policies for AI-driven support solutions.
Support Operating Models and Metrics
Study modern customer support KPIs such as cost-to-serve, case resolution efficiency, and customer satisfaction. This is essential for building the ROI models and executive dashboards required to measure AI impact.
AI-Enabled Support Technologies
Research the application of Agent Assist, Generative AI, Conversational AI, and Intelligent Case Routing in enterprise SaaS environments. This aligns with the preferred qualifications for implementing these specific solutions.

Skills to revise

Strategic Roadmap Development
Practice creating a multi-year AI strategy document that aligns technical initiatives with business outcomes. This is a core responsibility for leading the Support organization's transformation.
Cross-Functional Stakeholder Management
Prepare to demonstrate how you influence Product, Engineering, and Data Science teams to prioritize support-specific requirements. This is vital for the role's function as a primary connection point between departments.
ROI Modeling for AI Initiatives
Develop a template for calculating the business value of AI investments, focusing on productivity gains and operational efficiency. This directly addresses the requirement to build investment analyses and executive-level reporting.

Your preparation checklist

Develop an AI-driven support transformation framework
Prepare a high-level strategy document outlining how you would integrate AI technologies like Generative AI and Intelligent Case Routing into a support organization to improve KPIs such as cost-to-serve and resolution efficiency.
Create an ROI and KPI measurement model
Draft a sample executive dashboard or ROI model that demonstrates how you would track the business impact of AI initiatives, specifically focusing on productivity gains and customer satisfaction metrics.
Prepare a cross-functional influence case study
Structure a narrative from your past experience that highlights how you successfully aligned Product, Engineering, and Data Science teams to execute a complex, large-scale digital transformation project.
Draft a responsible AI governance policy
Outline a framework for balancing innovation with risk management, compliance, and security, demonstrating your ability to lead AI deployment in a way that adheres to enterprise standards.
Formulate strategic questions for the leadership team
Prepare thoughtful questions regarding Sprinklr's current AI-native platform challenges, the existing maturity of their support data infrastructure, and how they prioritize AI investments across their four product suites.

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