AI use cases by business function and industry

An AI use case is one defined job that AI does inside a business process, with a measurable outcome. Examples on this page include qualifying sales deals from call transcripts and emails, answering routine support email, summarizing loan files and masking Aadhaar numbers across a document archive.

Agentic AI use cases are tasks an agent carries through by planning steps and calling systems. At iSEEit, agents on Amazon Bedrock extract deal signals, assess MEDDPICC qualification, flag deal risk and suggest next actions, and complete qualification coverage rose from 42% to 85% within 6 months. At Unaric, Agentforce runs record creation, status updates and lead qualification from plain English instructions.

Generative AI use cases produce content a person uses: Amazon Bedrock summaries of loan applications for Aavas Financiers, deal summaries and stage reports for iSEEit sellers, captions and hashtags for Memorizz event photos, and support answers from a knowledge base with ProGPT.

Start with readiness: business goals, the workflows worth improving, and the state of your data and infrastructure. HabileLabs runs a free AI readiness assessment, ranks candidate use cases with clear KPIs, and validates the top one with a proof of concept before full-scale development.

Most of our published BFSI AI cases are document use cases: Amazon Bedrock summaries of loan files at Aavas Financiers, and AI masking of Aadhaar numbers and QR codes for Utkarsh Small Finance Bank (99% accuracy, 2.8 lakh documents in one week) and Choice Finserv (95% accuracy across 1.5 million documents).

Yes, for some workloads. For Utkarsh Small Finance Bank, the masking pipeline ran fully offline inside the bank and processed 2.8 lakh documents in one week, and Wonder Home Finance masks documents in an air-gapped environment. Generative AI can also run on-premises, in the cloud or in a hybrid setup.

Each use case gets a business target, agreed before any model is built. At iSEEit, a golden-dataset evaluation showed 92% agreement between agent MEDDPICC assessments and expert human review, and results were reported as qualification coverage, forecast accuracy, weekly usage and inference cost per deal.

The iSEEit, GradientLabs and Aadhaar masking figures are reported results from published case studies. The Kiro programmes at SAS Tech Studio, CloudiQS and Macfos report targets, and this page labels them as targets.