Role Purpose: The AI Transformation Lead will drive the enterprise-wide AI agenda, translating the organization’s strategic ambitions into scaled, high-impact AI interventions. The role serves as the primary conduit between Business and IT — collaborating with the Data Infrastructure and AI Infrastructure teams to convert prioritized business opportunities into deployed, value-generating AI solutions. Partnering closely with the Transformation team, this position ensures the enterprise focuses on the right areas of AI intervention and delivers each initiative on time and in full (OTIF), with measurable business outcomes.

Role Accountability:

Enterprise AI Strategy & Prioritization:

1. Own the enterprise AI roadmap, aligning AI investments to strategic priorities and quantified business value.
2. Build and maintain a prioritized pipeline of AI/GenAI use cases across acquisition, engagement, risk, collections, service, and cost efficiency.
3. Partner with the Transformation team to sequence interventions by impact, feasibility, and readiness, ensuring scarce capacity is directed to the highest-value opportunities.
4. Work jointly with the Data Infrastructure and AI Infrastructure teams to shape data pipelines, model platforms, and deployment architecture required for each use case.
5. Ensure business intent is carried through design, build, and deployment, resolving trade-offs between ambition, feasibility, and time-to-value.

Delivery Governance – On Time & In Full

1. Drive timely and in-full (OTIF) implementation of prioritized AI initiatives through disciplined planning, milestone tracking, and issue resolution.
2. Establish delivery cadence, dependency management,



and escalation mechanisms in partnership with the Transformation PMO.
3. Manage cross-functional risks, remove blockers, and hold owners accountable for committed timelines and outcomes.

Data & AI Infrastructure Partnership

1. Collaborate with the Data Infrastructure team to ensure availability, quality, lineage, and governance of the data assets that power AI models.
2. Work with the AI Infrastructure team on model platforms, ML Ops, deployment pipelines, monitoring, and scalability of production AI.
3. Ensure AI solutions are engineered for reliability, reusability, and enterprise scale rather than one-off pilots.

Value Realization & Performance Tracking

1. Define baselines, benefit estimates, KPI targets, and measurement methodologies for every AI intervention.
2. Track realized impact on customer acquisition, CLTV, risk reduction, cost efficiency, and productivity, linking each initiative to quantified value.
3. Provide data-driven narratives on progress, gaps, and interventions for leadership reviews and strategic forums.

Governance, Risk & Responsible AI

1. Institutionalize model governance, documentation, versioning, and monitoring in line with regulatory expectations (RBI, DPDPA etc.).
2. Embed responsible-AI principles — fairness, explainability, data privacy, and security — across the AI lifecycle.
3. Partner with Risk, Compliance,



and Information Security to ensure AI deployments meet legal, regulatory, and contractual requirements.

Measures of Success

Number and business impact of AI use cases prioritized, deployed, and scaled across the enterprise.

1. On-time & in-full (OTIF) delivery of committed AI interventions.
2. Measurable impact on acquisition, CLTV, risk reduction, cost efficiency, and productivity driven by AI.
3. Strength of the Business–IT partnership, and maturity of data and AI infrastructure enabling scale.
4. Continuity of implementation with minimal downtime and risk discovery; robustness of AI governance, model reliability, and responsible-AI compliance.
5. Technical Skills / Experience / Certifications
6. Robust understanding of AI/ML and GenAI concepts, use-case design, and end-to-end AI solution delivery.
7. Working knowledge of data engineering, ML Ops, model deployment, and cloud/AI infrastructure fundamentals.
8. Strong program delivery, dependency management, and stakeholder governance experience.

Competencies critical to the role

1. Advanced analytics, GenAI solutioning, or digital transformation
2. Program governance
3. Delivery management and OTIF execution for multi-workstream initiatives
4. Model governance
5. Regulatory expectations (RBI, DPDPA), and Enterprise data governance

Qualification:

1. Bachelor’s degree in Engineering, Computer Science, Data Science, Mathematics, Statistics, or related technical discipline.
2. Master’s degree (MBA/Analytics/AI/ML/Data Science) preferred.
3. Certifications in AI/ML, GenAI, Data Engineering, Cloud (AWS/Azure/GCP), or ML Ops are an added advantage.

Preferred Industry

BFSI, Fintech etc.

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