Technical Lead & BPMN Educator·8 min read
Three roles AI plays in a process
Long lists of "AI use cases" are easy to find and hard to act on. A more useful lens is to ask what role AI plays on a given task. Almost every reliable use case is one of three.
AI drafts
AI produces a first version a human reviews
Example: Draft a contract summary, a response, or a process model from a transcript.
AI decides
AI classifies or routes within clear guardrails
Example: Categorise a ticket, flag an anomaly, route an invoice for approval.
AI acts
An agent completes a task end-to-end, with checkpoints
Example: Reconcile an exception, chase a missing document, update records across systems.
AI use cases by function
| Function | Common use cases | Where AI fits |
|---|---|---|
| Finance & accounting | Invoice capture & coding, exception reconciliation, anomaly detection, close-process drafting | High volume, rule-heavy - strong fit for AI-decide + AI-act. |
| HR & people ops | CV screening drafts, onboarding orchestration, policy Q&A, ticket triage | Judgement-sensitive - keep humans on hiring calls; AI drafts and routes. |
| Customer service | Response drafting, intent classification, deflection, summarising long threads | High volume, fuzzy inputs - AI-draft + AI-decide with human escalation. |
| Operations & supply chain | Demand-signal summarisation, exception handling, document extraction | Mixed - automate the repetitive, keep judgement human. |
| Procurement | Supplier-doc extraction, PO matching, contract clause review | Rule-clear extraction is a strong AI-act candidate; approvals stay human. |
| Sales & marketing | Lead enrichment, first-draft outreach, meeting-note capture, CRM hygiene | AI-draft everywhere; a human owns the relationship and the send. |
AI use cases by industry
Banking & financial services
KYC document checks, fraud/anomaly flags, dispute-handling drafts, regulatory-report assembly.
Healthcare
Clinical-note summarisation, prior-authorisation drafting, coding assistance, intake triage.
Manufacturing
Quality-defect classification, maintenance-log summarisation, SOP drafting, supplier-doc extraction.
Insurance
Claims intake extraction, first-notice-of-loss triage, policy Q&A, subrogation flagging.
Professional services
Proposal drafting, meeting summarisation, knowledge search, timesheet/records hygiene.
"A use-case list is only a starting point. The teams that get value do not pick a use case off a slide - they map the process, find where the friction and volume actually are, and let that point to the use case. The process tells you the use case, not the other way around."
From use case to implementation
A use case is a hypothesis. To turn it into value you still have to map the process, baseline the numbers, design the human-AI collaboration, and measure the result against that baseline. That is the job of a proper implementation method - see our guide on how to implement AI in business. Mapping the process quickly is where AI-native tools such as Crismo help - they draft the model from a transcript so you can get to the decision faster.
Related guides
How to Implement AI in Business
The 5-stage framework these use cases plug into.
What to Automate with AI
Score each task: rule, assist, agent, or human.
How to Measure AI ROI
Baseline, value delta, and the metrics that matter.
What is Process Automation?
Types, benefits, and how BPMN connects to automation.
Frequently asked questions
What are the most common AI use cases in business?▼
The reliable ones cluster into three roles: AI drafts (a first version a human reviews), AI decides (classification and routing within guardrails), and AI acts (an agent completes a task end-to-end with checkpoints). Concretely that means things like invoice coding, ticket triage, document extraction, response drafting, and exception handling.
How do I find AI use cases in my own organisation?▼
Look at your processes, not the technology. Map how a process runs today, then for each task ask whether AI should draft, decide, act, or stay human - based on volume, rule-clarity, data, and error cost. The high-volume, rule-clear, low-judgement tasks are your first use cases.
Which AI use cases give the fastest ROI?▼
High-volume, repetitive tasks with clear inputs and a tolerable error cost - invoice coding, document extraction, ticket classification. The payback comes from volume, and a wrong call is cheap to correct. Judgement-heavy or high-stakes tasks give slower, riskier returns.
What AI use cases should you avoid?▼
Rare, high-stakes, context-dependent decisions where a mistake is expensive and automation maintenance costs more than it saves. Keep those human. Also avoid automating a broken process - fix the process first, or AI just produces the wrong outcome faster.