Which business process should we try AI on first?
Choose a recurring task or process that costs the team time, has clear records and an owner, and lets you measure whether the result improves. Start with a small change that can be checked and corrected. Avoid beginning with work whose mistakes would be difficult to undo.
Illustrative example: An application waits eight days in a queue but takes twenty minutes to review. Making the review faster may save little if the case is really waiting for a document or an owner. Start by finding why it waits, then test a small change to that cause.
Score the first process
Choose work that repeats, costs time or money, has an owner and leaves records you can check. Prefer a step whose mistakes can be stopped or corrected. A safe first step might be identifying a missing document and routing it to the right person, while the consequential decision stays with its reviewer.
| Check | Evidence to collect |
|---|---|
| Owner and frequency | Who owns the result, and how often does the case occur? |
| Cost and baseline | How much staff time, rework, delay or cash does it consume today? |
| Usable evidence and access | Can the necessary records be read and the permitted action be tested? |
| Exceptions and reversibility | Which cases need judgment, and can a wrong step be stopped or corrected? |
Talk to the people doing the work. Record when cases enter, become ready, wait, receive review and finish. That map often reveals a better first change than the most polished demo. The problem-statement guide helps turn “we need AI” into a named job.
Can AI find what is delaying approvals and paperwork?
AI can compare timestamps, missing requirements and ownership across the steps. It can show whether a case waits for required review, a missing document, duplicate requests or an unclear handoff. Fix the cause of avoidable waiting before automating more steps.
In the eight-day queue example, keep the required approval. Test whether one clear document request and a named owner shorten the wait without skipping the review. Measure queue age, rework and the later business result, not only the number of tasks created.
Can AI find ways to cut operating costs?
It can identify repeated work, delays or spending that may be avoidable. The team must check the cause, estimate the cost of changing it and later measure savings before calling an opportunity a result. Staff time returned is useful capacity; it is not automatically cash saved.
At a Fortune 500 insurance carrier, the historical candidate-evaluation application was attributed $1.58M in Q1 2025 net savings, validated by the customer's CFO. That figure belongs to that hiring application. It does not establish savings for the cross-system paperwork example here. The evidence register and Decision Traces methodology provide its scope.
Move from one step to a working process
The difficult work often crosses systems and has exceptions. A newly hired producer, for example, may need an ATS record, HRIS start date, background check, license check, equipment and training. A handoff can be slow because each team sees only part of the case. Connecting the records can show what is waiting and which permitted step comes next; it does not remove a necessary review.
Nodes Connector supplies tested reads and actions, AI FDE helps configure and test the job, and Nodes Engine connects the evidence, investigates the delay and coordinates authorized work. A Decision Trace records the reason, approval where required, confirmed action and later observation. The broader process remains illustrative until validated for a customer's systems and rules.
After the first step works, inspect harder cases before expanding authority. Check conflicting records, failed writes and the cost of human exceptions. A completed administrative task is not the same as a shorter time to productive work or a measured saving.
For the published hiring application's evidence, see why hiring breaks at 10,000 applications per role. For the broader operating model, see the platform.
Saad Bin Shafiq is the founder of Nodes, serving data-sensitive enterprises. Methodology: Decision Traces.