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Program Overview & Objectives

Purpose

A demand-driven, modular internship program that builds client-ready, AI-literate talent pipelines aligned with Business Unit (BU) needs.

Guiding Principles

Business-led, ROI-driven

Intake and scope anchored in validated BU demand, not technology silos.

Custom over Standard

Modular format adaptable to BU/client context (duration, tracks, format).

AI-first foundation

Every intern achieves AI literacy; advanced tracks build deeper expertise.

Project-aware by design

Learning experiences and deliverables mirror real client/project needs.

Brand & CSR Impact

Interns act as ambassadors through CSR projects, tech talks and university engagement.

Operational clarity to BUs

Clear governance, decision gates and funding models to ensure efficiency and transparency.

Strategic Objectives

Demand alignment

Translate BU forecasts into capacity plans (headcount, location, timing).

Skill focus

Prioritize key domains — Frontend, Backend, Data/ML, QA Automation, DevOps, Cloud, Big Data.

Define success

Measured through conversion to FTE, client readiness and employer brand reach.

ROI & Success Metrics

Primary KPIs:

FTE conversion

55–70%

Time to billability

≤ 6–8 weeks

Retention

≥ 95% at 12 months

Secondary KPIs

Employer brand reach

Social media ambassadorship, events, academia partnerships.

Project-visible outcomes

POCs, reusable project capstones.

Innovation outputs

Global contest entries, tech articles.

Data cadence

Live SharePoint with program dashboards.

Governance Decisions

Intake & Skills

Approve demand-driven intake model and skill mix.

Framework & Reporting

Approve budget, KPI framework and reporting cadence.

Top Risks & Mitigations

Risk Mitigation
Demand mismatch Internship participants decided based on firm demands, targeting 55-70% conversion, project interviews in selection flow
Low conversion Raise selection criteria, embed project exposure
Mentor overload / cost creep Fixed mentor allocations, monthly cost reviews, BU co-funding
Client data risks Sandboxed datasets, pre-approved problem/POC templates