The AI agent use cases that hold up in production share a shape. There is a repeated job with a knowable right answer, the systems involved can be reached through an API, and there is one point where a person checks the work before a mistake costs money or trust. Below are 20 of them, grouped by function. For each one we list the job, what the agent reads and writes, the human checkpoint, the metric that proves it is working, and a realism rating. Where Gigabit has a published case study for a use case, we link it and quote its numbers exactly as published. Where we do not, the row says nothing about results, because we have none of our own to show.
How to read the ratings
- Proven. The pattern is well understood, the failure modes are known, and a narrow version can go live in a normal build with confidence gates in place.
- Works with care. It works when the scope stays narrow and the human checkpoint is real. Widen either one and quality drops quickly.
- Still experimental. Run it in shadow mode, where the agent drafts and a person does the job as before, until your own numbers say it is ready.
The ratings are our judgement from building agents, not an industry survey. A chatbot answers questions, while an agent reads from and writes to your systems; AI agent vs chatbot covers the difference. Several jobs below are better done by plain rules with a model only on the messy step, and rules-based automation vs AI agents has the test for telling which.
Customer support
| Use case | Reads and writes | Human checkpoint | Metric that proves it | Realism |
|---|---|---|---|---|
| 1. Tier-1 ticket resolution | Reads tickets, order and shipping data, policies and product docs. Writes replies; can start returns, apply codes or change addresses | Below a confidence threshold the ticket goes to a person with the draft attached. Order changes ask the customer to confirm | Share of tickets resolved with no human touch, first response time, CSAT, reopen rate | Proven |
| 2. Agent assist: triage and drafts | Reads the ticket and customer history. Writes category, priority and a draft reply into the help desk | A person sends every reply | Handle time per ticket, share of drafts sent with light edits | Proven |
Published proof for row 1. In our D2C support agent case study, over the first 90 days of full deployment the agent resolved 3,149 tickets a month (64%) with no human involvement. First response time fell from 4.2 hours to 6 minutes for AI replies, CSAT went from 3.9 to 4.5, annual savings were $217,200 and payback took 5.1 months. The checkpoint was a pair of gates: the agent replied on its own only when intent confidence exceeded 0.85 and retrieval relevance exceeded 0.80. Below either, the ticket went to a person with the draft and context pre-loaded. Three of the human agents were moved to proactive outreach, VIP management and post-purchase work.
Row 2 is the safer first step when your help desk data is messy or your policies change weekly. It earns trust and produces the labelled data that row 1 needs. If you are weighing a custom build against a help-desk add-on, our custom support agent vs Intercom Fin comparison covers the trade.
Sales and revenue
| Use case | Reads and writes | Human checkpoint | Metric that proves it | Realism |
|---|---|---|---|---|
| 3. Inbound lead qualification and routing | Reads form fills, enrichment data and the CRM. Writes a score, an owner and a booked meeting | Reps see the reasoning behind each score. Disqualified leads are reviewed weekly | Time to first response, share of booked meetings that sales accepts | Proven |
| 4. Proposal and RFP first drafts | Reads past proposals, case material and the brief. Writes a draft document | A person owns every word that goes out | Hours per proposal, with win rate held steady | Proven |
| 5. Outbound prospecting | Reads target lists, enrichment and company sites. Writes personalised emails, handles replies, books meetings | Sequences and a sample of messages approved before sending. Hard caps on daily volume | Positive reply rate, meetings booked, bounce and spam-complaint rates | Works with care |
Published proof for row 4. In our consulting firm case study, a proposal assistant built on the firm's knowledge base cut proposal creation from 4–8 hours to 45 minutes of review. Row 5 is rated lower because the risk sits outside your systems: a badly tuned outbound agent can damage your sending domain faster than it books meetings, so deliverability limits matter as much as the writing.
Finance and back office
| Use case | Reads and writes | Human checkpoint | Metric that proves it | Realism |
|---|---|---|---|---|
| 6. Time entry suggestions | Reads calendar, email, documents and chat activity. Writes suggested time entries | Each person approves their own entries | Time capture rate | Proven |
| 7. Management reports with a written summary | Reads the warehouse or source systems. Writes scheduled reports with a short narrative | The numbers come from queries, never from the model. Someone reads the narrative before it goes to leadership | Report delivery time, errors found in the narrative | Proven |
| 8. Invoice capture and AP coding | Reads emailed invoices and purchase orders. Writes draft bills with GL codes in the accounting system | A person approves every bill before payment. Failed PO matches go to finance | Share of matched invoices needing no edits, exception rate, posting errors found at close | Works with care |
Published proof. In the same consulting firm case study, a time entry assistant that suggests entries each morning for one-tap approval lifted time capture from 80–85% to 94% (row 6). In our e-commerce operations case study, automated reports with AI-written narrative moved daily delivery from 10:30 AM to 7:00 AM (row 7). We have no published case for row 8. It is rated lower because a wrong GL code is quiet: nobody notices until close, so the checkpoint has to be a real approval step rather than a glance.
Operations and logistics
| Use case | Reads and writes | Human checkpoint | Metric that proves it | Realism |
|---|---|---|---|---|
| 9. Returns processing | Reads the order, return policy and the customer's free-text reason. Writes the return record, label, confirmation and refund on delivery | Hard stop and human review when a policy or confidence check fails. Likely defects flagged to QA | Share processed with no touch, minutes per return, refund errors | Proven |
| 10. Inventory reconciliation | Reads stock levels across channels and the system of record. Writes corrections for known discrepancy causes | Only true exceptions reach a person | Inventory accuracy, hours per week | Proven |
| 11. Supplier PO follow-up | Reads purchase orders and vendor reply emails. Writes structured ship dates, tracking numbers and chasers | Escalates when a date slips past the threshold you set | Hours per week on follow-up, late POs caught before customers notice | Proven |
| 12. Order exception handling | Reads failed orders: address, payment, split shipments. Writes a classification and a prepared fix | A person clicks the fix | Minutes per exception | Proven |
| 13. Freight quotes and orders from email | Reads quote requests and rate data. Writes quotes and orders into the TMS | Pricing rules and margin floors in code. A person checks unusual lanes | Quote turnaround, quote-to-book rate | Works with care |
Published proof for rows 9 to 12. In our e-commerce operations case study, 85% of returns processed with no human touch and per-return handling fell from 12–18 minutes to zero on that slice. Inventory accuracy rose from 94.2% to 99.1%, vendor PO follow-up dropped from 5 hours a week to 45 minutes, and exception resolution fell from 25 minutes to 6 minutes with a human approval deliberately kept on the final action. Those were n8n automations with GPT-4o-mini on the classification and email-parsing steps, which shows how much agent work is ordinary workflow with a model in the right place. The full write-up is in where AI agents pay off first in ecommerce ops. For row 13 we have no case of our own; AI in logistics covers the published freight-broker results.
HR and IT
| Use case | Reads and writes | Human checkpoint | Metric that proves it | Realism |
|---|---|---|---|---|
| 14. Internal knowledge assistant | Reads wikis, drives and past work, filtered by each user's access. Writes cited answers in Slack or Teams | Every answer cites its source. Low-confidence answers say so and name an owner | Repeat questions reaching HR, IT and ops, search time, accuracy on a fixed test set | Proven |
| 15. IT helpdesk triage and routine requests | Reads tickets, device and identity systems. Writes category and routing, and performs a short list of safe actions | Anything that grants access goes through your existing approval | Time to resolution, share resolved at first touch | Works with care |
| 16. Candidate screening | Reads CVs and applications. Writes summaries and a suggested shortlist | A person makes every decision, with regular checks for adverse impact | Recruiter hours, agreement between reviewers | Still experimental |
Published proof for row 14. In the consulting firm case study, a consultant asks a question in Slack and gets a sourced answer in 4 seconds, and the knowledge base recovered 280 hours in the first full quarter with all three phases live. Row 16 is experimental for legal as well as technical reasons. The EU AI Act lists AI used for recruitment and candidate selection among high-risk uses in Annex III, which brings obligations for whoever deploys it. If you hire in the EU, involve employment counsel before you pilot. Our AI governance service helps with the controls; it does not give legal advice.
Healthcare and regulated work
| Use case | Reads and writes | Human checkpoint | Metric that proves it | Realism |
|---|---|---|---|---|
| 17. Patient admin questions and prep instructions | Reads approved SOPs, prep guides and insurance FAQs. Writes cited answers on web, SMS and after hours | Scope limited to admin topics with no clinical advice, escalation triggers, staff handoff with a summary | Repetitive inquiries reaching the front desk, time to answer, after-hours self-service | Works with care |
| 18. Prior authorisation and claims preparation | Reads chart notes and payer rules. Writes a draft submission packet | A trained person checks and submits every packet | Packet completeness, denials for missing information, staff minutes per case | Still experimental |
Published proof for row 17. In our specialty clinic case study, after 8 weeks repetitive inquiries handled by the front desk fell 62%, median time-to-answer on common questions dropped from 4:10 to 1:20 (68%), and 40% of after-hours interactions resolved self-serve. It is still rated "works with care" because the narrow scope did the work: administrative and prep topics only, refusal rules, PHI redaction and human handling for edge cases. Anything touching PHI needs the controls in HIPAA-compliant AI agents, and intake that writes to the EHR is covered in AI patient intake and EHR integration.
E-commerce storefront
Rows 1 and 9 came from e-commerce clients. Two more storefront jobs come up often:
| Use case | Reads and writes | Human checkpoint | Metric that proves it | Realism |
|---|---|---|---|---|
| 19. Product questions and sizing help | Reads the catalogue, size guides, stock and reviews. Writes answers and add-to-cart links | It never states a spec it cannot find. Unknowns go to support | Conversion of chat sessions, returns citing wrong size | Works with care |
| 20. Catalogue enrichment | Reads supplier data sheets and images. Writes titles, attributes and descriptions as drafts in the store admin or PIM | A merchandiser approves before anything publishes | Attribute completeness, time to list a new SKU, error rate in sample audits | Works with care |
We have no published case for either. The common failure in both is the agent filling a gap with something plausible, which is why the checkpoint is about missing data rather than tone.
How to pick your first one
Pick a job where the hours are already measured, the right answer is knowable, the systems can be reached, one person owns the number, and the worst case is survivable. Those are the five conditions in five signs your business is ready for an AI agent, so score yourself there rather than here. Our free AI readiness assessment asks six questions, one each on data, process, tooling, team, alignment and the problem itself, and returns a score with a recommended first move.
Three filters from the catalogue above narrow the list further:
- Start with a Proven row. Your first agent should teach your team how to run one, not test whether the idea works at all.
- Prefer a job where mistakes are caught before a customer sees them. Drafts, prepared fixes and internal answers forgive more than outbound email or refunds.
- Measure the baseline for 30 days before you build. Without the before number, the metric column above proves nothing.
Then write a one-page brief. Copy this and fill every line; a line you cannot fill is the first thing to go and find out.
| Field | Example (row 9, returns) |
|---|---|
| Job, in one sentence | Process eligible returns from request to refund |
| Volume | Returns per month, and the share that are routine |
| Hours per week today | Staff time on returns, from a two-week log |
| Systems it reads | Store, OMS or ERP, carrier tracking, return policy |
| Systems it writes | Return record, label, customer email, refund |
| Human checkpoint | Which cases stop for review, and who reviews them |
| Metric and target | Share processed with no touch; minutes per return |
| Worst case | A refund issued in error: how much, how often, how it is caught |
For the checkpoint line, when should an AI agent stop and ask a human? has an escalation matrix you can copy. For the metric line, what to measure in an AI agent eval suite covers which numbers should gate a deploy. To check the payback before you commit, put your volume and hours into the AI ROI calculator.
What it costs to put one live
A Gigabit Agents build is one flat fee per agent, from $8,000. The service page puts the Standard build at chat, up to two integrations and your existing knowledge base. Voice, three or more integrations, custom back-end actions and HIPAA work are scoped higher. The fee covers the scoping workshop, design and build, integration, testing and QA, deployment, handoff and 30 days of tuning. After that, Managed AI Operations from $3,000 a month keeps it monitored, re-tested and updated. For running costs such as model tokens and hosting, see what an AI agent costs.
If the job turns out to be a fixed sequence of steps with little judgement, it may not need an agent at all. The AI Quick Win Sprint builds one workflow with up to three tools for $8,000 fixed in 3 weeks. If you know you want AI but not where to start, the AI Transformation Sprint is $25,000 for 2 weeks and ends in a scoped agent. And if your best candidate is a Still experimental row, or the job takes less than about 15 hours a week of paid time, the honest advice is to wait or run it in shadow mode first.
