AI strategy & implementation.AI Use Cases
A plain-language reference to where AI and automation earn their place in a business. For each one: what it is, whether it's a strong fit, what to watch for, and how we'd approach it. Written to be useful on its own.
By function
It's organized the way a business runs. Pick the area that matches the work eating your team's time.
The repetitive money work: matching, entering, and reconciling numbers that already live in your systems.
Expense handling covers capturing receipts, checking them against policy, and routing them for approval and reimbursement. Most of it is repetitive checking that automation handles well, leaving your team the judgment calls.
Invoice matching checks each supplier invoice against its purchase order and receipt before you pay it. It's one of the strongest automation candidates in finance because it's high-volume, rule-based, and runs on data your systems already hold.
Keeping the pipeline clean and moving: capturing leads, routing them, and following up without the copy-paste.
Follow-up sequences send the right message at the right time after a lead or customer takes an action. Automation handles the timing and consistency; your team handles the real conversations it surfaces.
Lead capture and routing takes an inbound enquiry, records it, and gets it to the right person fast. It's a strong automation candidate because speed matters, the rules are clear, and the data is already coming in.
The glue work between tools: moving data around, generating documents, and the steps nobody owns.
Moving data between systems by hand is some of the most common hidden busywork in a business. When the tools can be connected, it's a strong automation candidate, and often the first place a team feels the time come back.
Document generation turns data you already hold into a finished contract, quote, or report. When the template is stable and the data is clean, it's a strong automation candidate that saves time and cuts errors.
The reports and cleanups that run on a schedule: pulling numbers together and keeping records tidy.
Data cleanup is the ongoing work of fixing duplicates, filling gaps, and standardizing records. The repetitive, rule-based parts automate well; the judgment calls stay with a person.
Recurring reports are the weekly and monthly roll-ups someone rebuilds by hand every cycle. Pulling the same numbers into the same format on a schedule is a strong automation candidate.
How to use it
Each entry reads a process against the same factors we use in an assessment, so you get an honest general read. To turn that into a plan for your business, score the process with the Automation Score, rank it against your other options on the AI Opportunity Matrix, and put a rough number on it with the ROI estimator. The Free AI Opportunity Assessment is where we do all of that on your real processes.
Ready when you are
The Free AI Opportunity Assessment is a no-cost look at how your business runs. We find the processes worth automating, estimate what they'd return, and tell you honestly which are worth building.