Cross-industry use case library
Practical AI and Automation Opportunities for Real Operations
Setiap use case perlu divalidasi berdasarkan workflow, data, risk, human role, approval, dan KPI—lalu disesuaikan dengan konteks industrinya.
Portfolio view
Start with a process, not a model
HR
HR onboarding workflow
Masalah: checklist, dokumen, akses, dan reminder tersebar. Peran automation: orkestrasi task dan notifikasi. Peran manusia: approval dan exception handling.
KPI: completion time, overdue tasks · Risk: incorrect access · Data: employee master, role, checklist
IT
Internal IT helpdesk assistant
Masalah: pertanyaan berulang dan knowledge tersebar. Peran AI: retrieval dan draft response. Peran manusia: menangani incident, akses, dan jawaban berisiko.
KPI: first response, resolution, escalation · Risk: inaccurate guidance · Data: approved knowledge articles
Quality
Policy and SOP knowledge assistant
Masalah: pencarian dokumen lambat dan versi membingungkan. Peran AI: menemukan kutipan dengan sumber. Peran manusia: memastikan dokumen berlaku dan menginterpretasikan konteks.
KPI: search time, cited-source rate · Risk: obsolete policy · Data: controlled document repository
Finance
Reimbursement workflow
Masalah: submission tidak lengkap dan approval sulit dilacak. Peran automation: validation, routing, reminder, dan status. Peran manusia: review dan approval finansial.
KPI: cycle time, return rate · Risk: incorrect routing · Data: claim form, policy, approval matrix
Procurement
Procurement request automation
Masalah: kebutuhan dan approval tidak konsisten. Peran automation: intake, completeness check, routing, dan evidence trail. Peran manusia: specification, evaluation, approval.
KPI: request completeness, approval time · Risk: bypassed authority · Data: form, approval matrix, budget references
Management
Management performance narrative
Masalah: laporan tersebar dan waktu analisis panjang. Peran AI: menyusun ringkasan dengan referensi data. Peran manusia: verifikasi, interpretasi, dan keputusan.
KPI: preparation time, correction rate · Risk: unsupported conclusion · Data: governed KPI dataset
Operations
Meeting summary and action tracking
Masalah: action item hilang dan follow-up tidak konsisten. Peran AI: draft summary dan action extraction. Peran manusia: konfirmasi keputusan, PIC, dan due date.
KPI: action completion, correction rate · Risk: missed nuance · Data: approved transcript or notes
Patient Experience
Appointment reminder workflow
Masalah: reminder manual dan status tidak tercatat. Peran automation: pesan terjadwal dan response routing. Peran manusia: menangani perubahan, keluhan, dan exception.
KPI: confirmation, no-show trend · Risk: wrong recipient · Data: appointment data with consent and access control
Manufacturing
Quality incident intelligence
Masalah: catatan defect dan tindakan koreksi tersebar. Peran AI: klasifikasi, pencarian pola, dan draft ringkasan. Peran manusia: root-cause analysis dan keputusan quality.
KPI: investigation time, recurrence trend · Risk: false pattern · Data: governed quality and production records
Retail & Distribution
Inventory exception workflow
Masalah: stock anomaly terlambat diketahui dan follow-up tidak konsisten. Peran automation: alert, routing, dan evidence trail. Peran manusia: validasi penyebab serta keputusan replenishment.
KPI: stockout trend, response time · Risk: inaccurate alert · Data: inventory, sales, lead-time references
Customer Operations
Customer inquiry classification and routing
Masalah: pertanyaan masuk bercampur dan response lambat. Peran AI: klasifikasi, prioritas, dan draft response. Peran manusia: menangani complaint, exception, dan komunikasi sensitif.
KPI: first response, routing accuracy · Risk: wrong classification · Data: approved categories and service knowledge
Have a workflow in mind?
Validate value, feasibility, and risk before building
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