volume and intake problems
| Worker Use Case | Email Triage and Response |
| What it does | An inbound shared inbox receives messages from customers, vendors, or staff. An AI worker reads each one, classifies it by intent, retrieves relevant context from connected systems (CRM, ticketing, knowledge base), drafts a response or routes the message to the right person, and flags anything that requires human judgment before anything is sent. |
| What triggers it | A message arriving in a monitored inbox: email, contact form, or support queue. |
| What it produces | A classified message with a draft response attached, a routing decision, or an escalation flag depending on what the message requires. |
| Where the human sits | A person reviews drafted responses before they go out, handles escalations, and approves any message that carries risk, commitment, or sensitivity. |
| Where it shows up | Customer support teams handling inquiry volume, HR shared services answering policy questions, sales teams responding to inbound enquiries, vendor management teams processing supplier communications. |
| Why it matters | This is the foundational pattern of the entire library: ingest an input, retrieve context, classify intent, produce a structured response, and escalate when needed. Almost every other worker use case is a variation of this sequence applied to a different domain. |
Examples
Customer inquiry response assistant — reads inbound customer emails, classifies intent, retrieves account history, and drafts a contextually appropriate response for agent review before sending
Shared inbox triage worker — monitors a team inbox, assigns each message to the correct owner based on content and workload, and flags urgent messages before they are missed in a high-volume queue
Sales enquiry classifier — reads inbound prospect emails, identifies buying intent and product interest, scores the enquiry, and routes it to the correct sales representative with a context summary attached
HR policy email responder — handles employee questions arriving by email about leave, benefits, and HR processes, drafts a policy-cited response, and escalates anything requiring HR judgment
Vendor email routing assistant — reads supplier and vendor communications, classifies them by type (invoice, query, dispute, update), and routes each to the correct team with relevant context retrieved from the procurement system
Complaint acknowledgement and handoff worker — identifies complaint emails, generates an acknowledgement response within a defined service window, flags the case for human review, and prepares a structured handoff summary for the agent taking ownership
| Worker Use Case | Finance Intake, AP, and Close-Cycle Work |
| What it does | Finance teams receive a constant stream of incoming documents: invoices, purchase orders, receipts, expense claims, and vendor statements. An AI worker reads each document, extracts the structured fields (vendor, amount, date, line items, VAT), matches it against purchase orders and receipts in the ERP, recommends the correct GL coding and tax treatment, identifies the right approver based on amount and cost centre, and flags anomalies such as duplicate invoices, mismatched amounts, unrecognised vendors, or missing PO references before they reach a human reviewer. |
| What triggers it | An email with an invoice attachment arriving in the AP inbox, a document uploaded to the finance processing queue, or a batch file arriving from a supplier portal. |
| What it produces | A structured invoice record with recommended coding, matching status, approver assignment, and an exception flag if anything is anomalous. |
| Where the human sits | Finance reviewers handle exceptions: anything flagged as anomalous, non-PO invoices above threshold, and coding decisions the system cannot make with confidence. Clean, matched invoices route to automated approval within defined parameters. |
| Where it shows up | Accounts payable teams in any organisation with significant invoice volume, shared service centres, and finance operations teams managing month-end close processes. |
| Why it matters | This is one of the clearest demonstrations of the full pattern: ingest a document, extract structure, compare against a record, apply policy rules, route exceptions to humans. The same logic appears in contract review, procurement comparison, and customer case resolution. Finance is just the context. |
Examples
Multi-channel invoice processing agent — receives invoices arriving by email, portal upload, or EDI feed, normalises them into a common structure, and routes them into the AP workflow regardless of source format
PO and receipt matching assistant — performs three-way matching between invoice, purchase order, and goods receipt automatically, flags discrepancies for finance reviewer attention, and passes clean matches to automated approval
AP coding and tax-check worker — recommends GL codes and VAT treatment for each invoice line based on the vendor category, cost centre, and applicable tax rules, with exceptions flagged for a finance reviewer to confirm
Approval routing for payables — identifies the correct approver for each invoice based on amount thresholds, cost centre ownership, and delegation of authority rules, and routes the invoice with full context attached
Ledger monitoring and explanation assistant — watches the general ledger for unexpected movements, produces plain-language explanations of significant variances, and surfaces anomalies before the month-end close review
Financial planning wizard — assembles budget inputs from connected systems, applies defined assumptions, produces draft financial models for review, and flags where data quality issues may affect forecast reliability
| Worker Use Case | Translation and Localisation |
| What it does | Global organisations produce content including legal documents, product materials, customer communications, training content, support articles, marketing campaigns, and internal policies that must be accurate and contextually appropriate in multiple languages. An AI worker handles the translation layer: reading the source document, applying domain-specific terminology drawn from an approved glossary or translation memory, producing a translated draft that preserves meaning, tone, and formatting, and flagging sections where cultural adaptation is needed beyond literal translation. For high-stakes content, it also produces a back-translation so a reviewer can verify accuracy without speaking the target language. |
| What triggers it | A document flagged for translation in a localisation workflow, a new market launch requiring content adaptation, a regulatory filing requiring a version in a second language, or a customer support interaction in a language outside the primary team’s capability. |
| What it produces | A translated document aligned to approved terminology and tone, cultural adaptation notes for sections requiring human review, a back-translation for verification where required, and a glossary update recommendation when new domain terms appear in the source material. |
| Where the human sits | A native-speaking reviewer or professional translator reviews the AI draft for accuracy, cultural appropriateness, and any sections the system flagged as requiring adaptation. For legal, medical, or regulatory content, this human review is not optional. It is a compliance requirement. |
| Where it shows up | Multinational corporations managing multilingual product documentation, legal teams processing cross-border contracts, customer support operations serving linguistically diverse markets, marketing teams adapting campaigns for regional launch, and compliance teams producing regulatory filings in multiple jurisdictions. |
| Why it matters | Translation appears simple but is one of the most infrastructure-intensive patterns in the library. It requires domain-specific glossaries, translation memory so approved phrasing is reused consistently, cultural context rules, and quality review workflows that vary by language pair and content type. Organisations that treat it as a simple text conversion task produce translations that are technically accurate but contextually wrong, which in legal or clinical contexts carries serious consequences. |
Examples
Multilingual support ticket translator — translates incoming customer support tickets into the agent’s working language, with one deployment turning around multilingual tickets in under 30 minutes
Product documentation localisation worker — translates technical product documentation into target languages using approved domain-specific glossaries and translation memory for consistency across versions
Legal document translation assistant — handles court documents, depositions, contracts, and client communications requiring certified accuracy and full audit trails
Marketing campaign adaptation worker — translates and culturally adapts campaign copy, product descriptions, and promotional content for regional markets rather than applying literal translation to creative material
Back-translation verification assistant — produces a back-translation of any high-stakes translated document so a reviewer can verify accuracy without speaking the target language, used particularly in clinical and legal contexts
Localisation workflow automation assistant — handles the repetitive preparation tasks in a localisation pipeline: file format conversion, pre-translation using translation memory, and routing to the correct reviewer, freeing linguists for higher-judgment work
| Worker Use Case | Proactive Monitoring and Alerting |
| What it does | All other worker use cases in this library are reactive: a human or a system event initiates them. This is the one fundamentally different pattern: an AI worker that watches continuously without being asked. It monitors a data stream, a set of systems, or a body of incoming information against a defined baseline or policy, identifies when something deviates from expected behaviour, assembles the relevant context around that deviation, and surfaces an alert with enough information for a human to act immediately rather than investigate from scratch. The alert includes what happened, when, how significant the deviation is, what similar events have looked like in the past, and what the recommended response is. |
| What triggers it | A defined threshold being crossed in a monitored data stream: a transaction pattern consistent with fraud, a compliance indicator moving outside acceptable range, a supplier delivery timeline slipping beyond tolerance, a system performance metric degrading, or a regulatory news feed publishing a change relevant to the organisation’s obligations. |
| What it produces | A structured alert containing the anomaly description, the severity assessment, the supporting evidence, a comparison to baseline or historical precedent, and a recommended immediate action, delivered to the relevant human through whatever channel they monitor most reliably. |
| Where the human sits | The alerted person reviews the context assembled by the AI, decides whether the alert represents a genuine issue or a false positive, and takes the appropriate action. The AI monitors and surfaces; the human investigates and decides. For high-stakes alerts covering fraud, safety, or regulatory breach, the human response pathway should be defined before the monitoring system is deployed, not improvised when an alert arrives. |
| Where it shows up | Financial services fraud and transaction monitoring teams, compliance teams tracking regulatory change, supply chain teams monitoring supplier and logistics performance, IT operations teams watching infrastructure and security signals, clinical teams monitoring patient vital signs or medication adherence, and retail operations teams tracking inventory and demand signals. |
| Why it matters | Every other worker use case in this library processes information when asked. This one watches information without being asked and tells you when something needs your attention. It is the pattern that makes AI feel less like a tool you use and more like infrastructure that works in the background. A monitoring system that alerts incorrectly, misses genuine signals, or escalates to the wrong person is worse than no monitoring system at all, which is why this pattern carries the most significant governance requirements of any use case in the library. |
Examples
Fraud and transaction anomaly monitor — watches payment and transaction streams continuously, flags patterns consistent with fraud, and assembles the supporting evidence for a human analyst to review before any action is taken
Cybersecurity threat detection agent — monitors network traffic, system logs, and user activity for anomalies, adapts to detect novel attack techniques, and alerts security teams with context before a breach occurs rather than after
Regulatory change monitoring assistant — watches regulatory news feeds, government publications, and compliance databases for changes relevant to the organisation’s obligations and alerts the compliance team with a summary of what changed and what it affects
Supply chain disruption alert worker — monitors supplier delivery timelines, logistics signals, and inventory levels against defined tolerances, and flags emerging disruptions early enough for the procurement team to act
AI agent behaviour monitor — watches deployed AI agents for unusual activity such as accessing data outside normal parameters or attempting unexpected external connections, and alerts security teams before a potential breach escalates
Clinical and patient monitoring assistant — tracks patient vital signs, medication adherence, or programme outcome data against defined thresholds and alerts clinical teams when a patient’s status requires immediate attention