AI Use Cases
Use-case lists are easy to find and mostly useless, because they omit the things that decide whether a project works: data availability, exception rates, review burden and who owns the outcome.
Updated 3 September 2026
Each page here describes one workflow, where AI realistically helps, what it takes to run it, and how to measure whether it paid off.
Where AI genuinely helps in customer service — triage, drafting, knowledge retrieval and summarisation — with complexity, data requirements, risks, KPIs and ROI considerations.
Practical AI use cases in sales — lead qualification, follow-up, research, proposal drafting and CRM hygiene — with data requirements, risks, KPIs and ROI considerations.
AI use cases across finance operations — invoice processing, reconciliation, expense review, reporting and forecasting support — with controls, risks, KPIs and ROI notes.
AI in operations and back office: intake, scheduling support, exception handling, supplier communication and reporting, with complexity, risks, KPIs and ROI considerations.
How AI document processing works in practice — classification, extraction, validation and review — including accuracy expectations, data needs, risks, KPIs and ROI drivers.
AI across the claims lifecycle — intake, triage, document handling, fraud signals and status communication — with regulatory controls, risks, KPIs and ROI considerations.
The Merjora assessment maps your workflows, scores each opportunity and quantifies the value range before you commit budget.
Discover your AI opportunitiesEditorial standard. Merjora publishes analysis, frameworks and publicly documented examples. We do not publish invented statistics, unattributed benchmarks or unverified customer stories.