Start with the task, not the technology
It's tempting to look for places to "add AI" once the idea is on the table. A more reliable starting point is the opposite: list the repetitive, time-consuming tasks your team already does, and only then ask which of them genuinely benefit from AI versus straightforward rule-based logic.
Tasks that are highly repetitive, follow a recognizable pattern, and don't require a judgment call every single time are usually good automation candidates — regardless of whether AI ends up being part of the solution.
Where AI adds real value
AI tends to earn its place in a workflow at the points where inputs are unstructured or variable: reading free-text messages, classifying incoming requests, drafting a first version of a reply, or extracting specific fields from a document that doesn't follow a fixed template.
In those spots, AI does the part that's hard to hard-code — interpreting messy input — while deterministic logic still handles the parts that need to be predictable and auditable, like validation, routing and record-keeping.
Where deterministic logic is still the better choice
Not every step needs AI. If a task always follows the same fixed rule ("if the invoice total is over X, send it to this approver"), a simple conditional is faster, cheaper, and more predictable than an AI call. Reserving AI for the genuinely ambiguous parts of a workflow keeps the whole system easier to test, debug and trust.
A simple way to prioritize
A useful filter is to rank candidate tasks by two things: how often they happen, and how much manual effort each occurrence currently takes. High-frequency, high-effort tasks are usually the best first targets — the return on getting them right is highest, and there's enough real-world volume to validate the automation quickly.
Low-frequency or highly judgment-dependent tasks are usually better left manual, at least until the process around them is stable enough to automate safely.