Use cases
AI agent use cases: the cross-industry shapes that pay back fastest
These are the five workflow shapes we ship most often. They are deliberately boring (structured input, repeatable shape, low individual judgement, clear human override path), which is precisely why they pay back in weeks rather than quarters. The same pattern adapts across industries; what changes is the binding constraint.
Why these five shapes, and not the customer-facing chatbot
Most teams want the demo-friendly project first: the customer-facing chatbot, the AI co-pilot, the dashboard that explains itself. Those look good on a slide and ship in twelve to eighteen months, if they ship. The five below ship in six to twelve weeks and pay back inside the first quarter. They share three properties: structured input, repeatable shape, low judgement per case. That combination makes them both agent-tractable and audit-tractable. The argument in full is in the unglamorous workflows pay back fastest.
Use case
Cross-system reconciliation
What it is
An AP clerk has eleven tabs open at 3pm to close one invoice. PO in SAP, goods receipt in the warehouse system, invoice in her email, supplier portal disagrees, freight charge in a colleague's spreadsheet. She is not deciding. She is gathering evidence so a decision becomes possible.
Where it applies
Logistics planning, where the agent reconciles carrier, customs, OMS, and customer vendor portal into one shipment record. Banking back-office, where it assembles regulatory-report data across core, trade, risk warehouse, and corporate-actions feeds. Manufacturing planning, where it stitches SAP, the MES, quality, CMMS, and supplier portals into one production view. Accounts payable, where it runs the 3-way match across PO, goods receipt, and invoice and surfaces only the mismatches.
What the agent does
The agent calls the same systems the human calls, normalises the answers, holds one consolidated view, and surfaces only the exceptions. The clerk moves from "find what's wrong" to "decide what to do about the twelve flagged cases." Read-only by default. AP reconciliation alone: 60 to 75 percent of the clerk's week back.
Use case
Intake and triage automation
What it is
A claims clerk picks up an FNOL call at 9am. By 11am she has typed the policyholder's account into Guidewire, pulled the policy, classified severity, requested the police report, and routed the file to an adjuster. She decided nothing about coverage. She built the file so the adjuster could. Intake is assembly, not adjudication.
Where it applies
Insurance FNOL, where the agent intakes the call transcript, pulls the policy from Guidewire, classifies severity, and assembles the claim file before the adjuster opens it. Healthcare referral intake, where it reads the referral, verifies eligibility, and pre-books the right slot. Customer support triage, where it categorises the ticket and routes it with the order history attached. Recruitment, where it reads each application, runs the screening rubric, and writes the shortlist note.
What the agent does
The agent intakes from email, phone transcript, web form or upload, extracts the structured fields, classifies severity against a written rubric, runs cross-system checks (policy lookups, conflict checks, eligibility), and either closes routine cases end to end or hands a built file over. Reduction on intake: 40 to 60 percent of clerk time, with cycle time falling from days to hours.
Use case
Document review and playbook-flagging
What it is
A contract associate gets a 47-page MSA at 7pm. The partner needs the redline by morning. She is not reading for comprehension; she is reading for deviation: every clause that walks from the playbook on liability caps, indemnities, IP, termination. She has done this two thousand times. Same standard every time.
Where it applies
Legal contract and NDA review, where the agent flags clauses that deviate from your playbook and quotes the precedent it would prefer. Commercial real estate lease abstraction, where it reads each lease into the abstract template and flags non-standard rent-escalation and option clauses. Banking vendor-agreement review, where it applies the firm's risk taxonomy clause by clause. Insurance underwriting-file assembly, where it reads the submission pack, extracts the rated fields, and flags the risk factors the underwriter needs to see.
What the agent does
The agent extracts the structured fields, flags every deviation from your playbook, classifies risk, quotes the precedent it would prefer, and routes by exception. Per-document review drops from 60 to 120 minutes to 5 to 15 minutes of associate review on top. The playbook is the IP. Capturing it in a form the agent applies the same way every time is the build.
Use case
Inbound lead qualification and triage
What it is
An inbound lead lands at 11pm Sunday on a real-estate listing. By 9am Monday it has either gone to the competitor who replied at 11.04pm or read three emails from a generic auto-responder that did worse than silence. First-response under five minutes roughly triples conversion. No human team covers that window across timezones and weekends.
Where it applies
Real estate inbound lead handling, where the agent qualifies on price band and timing, books a tour into the Salesforce calendar, and runs the follow-up sequence on what the lead asked. Recruitment candidate intake, where it screens against the role's criteria and books the screening call. B2B SDR triage, where it enriches against the CRM, qualifies on ICP fit, and routes hot leads with prior-touch history attached. Lending pre-qualification, where it runs soft-credit and eligibility checks before a human opens the file.
What the agent does
The agent responds inside five minutes, asks the qualifying questions, runs cross-system enrichment (CRM lookup, prior-history check, eligibility), books the tour, call or interview into the right calendar, and hands the qualified lead over with a context summary. Unqualified leads get a respectful exit. Conversion lift on speed-to-respond: 2x to 3x.
Use case
Returns, exchanges, and post-purchase resolution
What it is
A customer types "where is my order" into chat at 10pm Wednesday. The support agent picks it up next morning, opens Shopify, checks the carrier portal, sees the package went to the wrong address, checks alternative-size inventory, decides refund or replace, writes a four-paragraph reply. Twenty minutes of work the customer needed in twenty seconds. The inbox is 60 percent WISMO and returns.
Where it applies
Retail and e-commerce returns and exchanges, where the agent reads Shopify, the carrier feed, and the OMS, generates the return label, books the exchange, and processes the refund under the authority limit. Subscription pause and cancel, where it handles the request, offers the right save offer, and updates billing. Software licence transfers, where it verifies entitlements and re-provisions the seat. B2B replacement parts, where it verifies the part number against the install base and triggers dispatch.
What the agent does
The agent resolves 80 to 90 percent of routine post-purchase cases end to end: return label, exchange, refund under the authority limit, customer update. The remaining 10 to 20 percent (high-value, sentiment flags, refunds above limit) escalate with full context pre-written. Post-purchase cost-to-serve: 50 to 70 percent lower.
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