AI Program Qorus-NTT DATA Innovation in Insurance Awards 2026

Concept & objectives

This initiative involved the design and deployment of a corporate‑wide Generative AI Program, creating a unified operating model to scale AI solutions across commercial, operational, digital and technology domains.

The main objectives were to:
• Move GenAI from isolated pilots to enterprise‑level, production deployments.
• Drive measurable impact on corporate AI indicators: employee adoption, customer usage and productivity.
• Standardize governance, security and responsible‑AI practices at scale.
• Capture structural efficiency and productivity gains without proportional increases in cost or headcount.

The program structured a roadmap of 24 GenAI initiatives, deployed across sales, service, operations, marketing, risk, legal and technology teams, supported by shared enablers (talent, governance, data, architecture and platforms).

Rationale

As AI adoption accelerated, the organization faced the risk of fragmented initiatives, inconsistent governance and limited visibility of enterprise‑level impact.

Without a centralized program, GenAI risked becoming a collection of disconnected experiments. The GenAI Program enabled a shift from experimentation to enterprise value creation, ensuring scale, consistency and measurable outcomes aligned with corporate objectives.

By 2025, the program generated USD 350K in measurable impact, validating GenAI as a value‑generating operational capability.

Competitive landscape

While many insurers still approach AI through isolated proofs of concept, this initiative established a corporate AI operating model, achieving:

• Enterprise‑wide adoption rather than siloed use cases.
• Clear ownership, governance and prioritization based on business value.
• Measurable impact across productivity, customer interactions and cost efficiency.

This positioned AI not as an innovation experiment, but as a core operational capability.

Sources of inspiration

• Internal analysis of productivity bottlenecks and repetitive processes.
• Learnings from large‑scale chatbot and automation deployments.
• Corporate benchmarks on enterprise AI adoption models.
• Best practices in responsible AI governance and decentralization through AI Champions.

Departments involved

• Digital & Technology
• Customer Experience & Operations
• Sales & Commercial Areas
• Data & Advanced Analytics

• IA Team
• Risk, Legal, Compliance & Security

More than 10 business and technology areas collaborated under a shared AI governance and execution model.