Driving Enterprise AI Adoption for an Indian NBFC with an Outcome-First AI Roadmap

Industry: Financial Services | MSME Lending
Client: An RBI-regulated NBFC focused on MSME lending across India

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Client Overview

A fast-growing, RBI-regulated NBFC operating in the MSME lending segment, with a large multi-state branch network serving tens of thousands of customers across urban and semi-urban India.

As lending volumes scaled, leadership sought to embed AI across the lending lifecycle to improve efficiency, risk management, and customer experience. The mandate was clear: enterprise-grade, responsible AI adoption tied to measurable business outcomes—not experimentation.

The Challenge

The Challenge: From Experimentation to Enterprise Scale

As AI ambitions scaled, the organization sought a clear path from experimentation to enterprise execution.

Key challenges included:

AI initiatives distributed across teams, with limited enterprise-level visibility into ROI
Multiple proof-of-concepts underway, with a need for clearer pathways to scale
Disconnected data, tools, and processes made enterprise AI operationalization challenging
The absence of a structured framework to prioritize AI use cases by value and feasibility
Evolving needs around governance, ownership, and execution sequencing in a regulated environment

Leadership needed clarity on where to start, what to prioritize, how to execute, and how to scale AI responsibly.

The Turning Point

The organization recognized that scaling AI successfully required more than models or tools, it needed a clear, outcome-driven AI roadmap aligned to business priorities, regulatory constraints, and execution realities.

Indexnine partnered with the leadership team to translate AI ambition into decision-ready clarity and an execution blueprint.

Our Approach

What Indexnine Delivered

An Outcome-First AI Roadmap aligned to measurable business outcomes:

Outcome-Linked AI Opportunity Portfolio
Identified high-impact AI opportunities across sourcing, underwriting, servicing, and collections—explicitly tied to growth, efficiency, risk, and customer experience.
Readiness & Prioritization Framework
Applied a four-lens assessment (Processes, Data, Systems, AI Capabilities) to evaluate feasibility, risk, and impact—enabling objective prioritization.
Execution-Ready AI Roadmap
Sequenced initiatives into short-term quick wins and longer-term scalable programs, with clear owners, milestones, KPIs, and dependencies.
Build / Buy / Hybrid Recommendations
Defined practical execution paths for each priority use case based on vendor maturity, integration complexity, and platform strategy.
AI Governance & Operating Model
Designed a lean AI operating model with clear decision rights, an AI Lab structure, and steering oversight—ensuring responsible and compliant adoption.

Business impact

Business Impact & Outcomes

What Leadership Gained

Clear visibility into where AI would deliver the highest business value
Confidence to fund and execute AI initiatives with defined ownership and governance
A repeatable model to scale AI responsibly across the organization

Key Outcomes

16 AI opportunities identified across 6 core business functions
Top 5 high-impact initiatives prioritized for immediate execution
6-month execution roadmap with a backlog of future opportunities
Structured 90-day pilot cycles to accelerate learning and adoption

Why Indexnine

Outcome-first approach directly linked to business KPIs
Structured readiness framework for precise prioritization
Balanced portfolio of quick wins and long-term differentiation
Deep understanding of financial services operations, risk, and regulation
Practical, execution-ready guidance grounded in real-world constraints

Enterprise Impact

From AI Intent to Enterprise Impact

By shifting from scattered experimentation to a structured, outcome-driven AI roadmap, the organization built a strong foundation for scalable, responsible AI adoption—aligned to both business priorities and regulatory realities.

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