Use cases
The AI business use cases that pay are the recurring, rule-heavy tasks your finance and operations teams repeat every day inside SAP, NetSuite or Dynamics: keying orders, matching invoices, chasing payments, reconciling accounts. An agent can do that work end to end, and a person approves every consequential action until the agent has earned its way out of the gate.
01 · The work
Finding AI business use cases starts with a plain list: the tasks your team repeats every day, how often, and how long each takes. Most of a finance and operations day is the same work arriving again: orders to key, invoices to match, accounts to chase, balances to tie out. That recurring work is where an agent earns its keep.
02 · The knowledge
Most recurring finance tasks are deterministic: the same input should always produce the same outcome. What makes them hard to automate is that the rules are not written down. They live in the heads of the people who have done the work for years. Turning a task into an agent starts by writing those rules down.
Rules of thumb nobody has written down: which customers get a pass, which vendors always short-ship, which approver signs what. The work only gets done when that person is in.
Each rule is captured with the examples that prove it and stored in your Brain, the private layer we set up behind your firewall. Your rules stay yours.
The agent applies those rules to every transaction, logs each check, and sends anything outside them to a person with the rule it hit and a proposed resolution.
03 · The pick
Plot each recurring process on two axes: the hours it consumes every week and what it costs the business each year in labor, errors, leakage and cash tied up. Start where both are high. Then test the candidate: volume, rules you could write down, work inside your systems, and a person who already approves the outcome.
It arrives every day, in quantity, and someone re-keys it between systems.
Your pricing logic, thresholds and escalation paths decide the outcome, even if they only live in someone's head today.
The work lives in SAP, NetSuite or Dynamics and the mailboxes and spreadsheets around them.
Someone already approves the outcome, so the gate has an owner from day one.
04 · A worked example
Order entry is a common first use case: high volume, clear rules, and the cost of a wrong order lands on a customer. The agent reads each purchase order as it arrives, checks price, credit and stock against your rules, and builds the sales order in your ERP. Anything outside the rules goes to a person.
BeforeWith the agentIllustrative
05 · The catalogue
AI agent use cases in finance and operations fall into five flows: order to cash, procure to pay, record to report, quote to order, and operations. Each one below names the daily task, the rule that usually lives in someone's head, what an agent does, and what still waits for a person. Filter by flow, or read them all.
From the customer's PO to the cash in the bank.
05 use casesWaits for a personOff-contract pricing, credit holds and dates that cannot be met.
How Swift does it →Waits for a personEvery chase, until the agent has earned its way out of the gate.
How Sterling does it →Waits for a personEvery posting and every unexplained difference.
Waits for a personEvery dispute before it is filed, and every write-off.
Waits for a personEvery release above the limit.
From the request to buy to the payment that leaves.
04 use casesWaits for a personReleasing payment.
How Clark does it →Waits for a personThe award and the purchase order.
How Chandler does it →Waits for a personEvery query before it is sent.
How Chandler does it →Waits for a personAny vendor or bank detail change.
Reconciliation, close and the ledger you sign off.
03 use casesWaits for a personEvery journal that touches the GL.
How Tally does it →Waits for a personCommentary and journals.
Waits for a personEvery eliminating entry.
Inquiries priced and quoted inside your CRM.
02 use casesWaits for a personEvery quote before it goes to a customer.
How Quill does it →Waits for a personAnything that commits price or a date.
Stock, dates and shipments that have to agree.
03 use casesWaits for a personAny adjustment to stock.
Waits for a personAny new date given to a customer.
Waits for a personAny invoice that changes.
Every agent stops at an approval gate until it has earned its way out of one. Nothing releases cash, commits spend or touches a customer without a person saying so.
06 · One night
Picture a hypothetical distributor. A customer PO arrives after hours. By morning the agent has built the sales order, checked every line against the contract and held the one line it could not resolve. A person starts the day on that line, not on retyping the rest.
A 14-line PDF lands in the orders inbox.
Sales order drafted, prices checked against the contract, credit and stock confirmed.
Line 9's date cannot be met. The agent states the conflict and proposes the next ship date.
The order desk approves the proposal and releases the order and the acknowledgement together.
Illustrative example: a hypothetical distributor
07 · Where to start
Start from a proven agent when your workflow looks like one Ferrata Labs has already built: payables, receivables, reconciliation, sales orders, purchasing or quotes. Build custom when it does not. Either way the agent is built around your rules and runs inside your environment.
Proven agent
Six agents cover payables, receivables, reconciliation, procurement, sales orders and quotes. Each is shaped to your rules rather than installed as is.
See the six agents →Custom agent
A workflow that looks like none of them gets its own agent, including the parts your ERP was never built to do. It runs in your environment and is never rented back to you.
Bring us the workflow →08 · To production
Every use case follows the same four stages at Ferrata Labs: Assess ranks your candidates, Anchor sets up your Brain behind your firewall, the first agent goes live in under 90 days behind an approval gate, and then your own team becomes AI native and builds the next one.
Your candidate use cases ranked, with a number against each.
Your Brain set up behind your firewall, on your systems of record.
One use case live on real transactions, gated first.
Your team runs the loop for the next use case.
The loop is our agent development lifecycle (ADLC). The four stages are the AI implementation roadmap, and the whole arc is what enterprise AI transformation looks like in practice.
The way you price, route, approve and escalate is years of accumulated judgment, and it's a real advantage. It belongs to you and it stays with you, we're not asking you to hand over your playbook or fit yourself into someone else's template. We start from how your business already works, encode that logic into agents built for your environment, and leave you with an operation that runs faster with exactly the things that make you good at it still intact.
Every agent stops at an approval gate until it has earned its way out of one. Nothing releases cash, commits spend or touches a customer without a person saying so.
Thirty minutes, no deck. Pick the process that costs you most in people, errors or delay, and we'll pressure-test whether an agent is genuinely the right answer for it.