Choose one expensive, repeatable task
An artificial-intelligence investment should begin with a workflow you can measure. Pick one task, record its present cost and define what a useful improvement would look like. A broad ambition to “automate operations” makes it hard to price the work or judge whether it helped.
Order entry is a useful hypothetical example. Record the number of orders, handling time, correction rate and the time spent resolving exceptions. Include the work after the initial entry: a fast first pass can still create expensive errors downstream.
Separate capacity from cash savings
Suppose 600 orders each take six minutes to enter and check. That is 60 hours a month. At an illustrative loaded labour cost of $35 an hour, the activity consumes $2,100 of monthly staff capacity. These are assumed figures for the example, not an industry benchmark.
If a pilot reduces the task to three minutes per order, the gross time saving is 30 hours, worth $1,050 at the same assumed rate. Subtract software fees, ongoing review and exception handling before estimating the benefit. Do not label the full amount a cash saving unless payroll, overtime or another actual expense will fall. Redeployed time may instead support more orders or better customer service.
Price setup separately: data preparation, integration, testing, training and the cost of fixing a failed implementation. A low subscription price does not represent the complete investment.
Decide what the system must not do
Test ordinary orders and difficult cases, such as missing product codes, changed pricing or ambiguous quantities. Decide which results require a person’s approval and where uncertain outputs should go. Keep customer and commercially sensitive information within tools and permissions the business has approved.
Set the acceptance conditions before the pilot starts. Examples include handling time measured over a defined sample, an acceptable correction rate, an audit trail and a workable fallback. Use your own requirements; there is no universal percentage that makes an artificial-intelligence project worthwhile.
Fund the project you can explain
If pursuing an incentive, distinguish the business case from the funding case. Record the actual activities and quotes, then check them against the specific program’s rules. The presence of artificial intelligence does not by itself establish eligibility.
At the end of the pilot, choose to scale, revise or stop. Retain the baseline and results even if the answer is to stop. Avoiding a wider rollout that does not improve the workflow is a useful result, not a reason to hide the test.
See Grantuity’s advisory services for help connecting a defined investment, its evidence and potential funding routes.
