It is easy to be impressed by an AI demo. It is harder to know whether an automation is worth the money. Before any project, we build a simple business case in rupees, and after launch we check whether it delivered.
Step 1: Measure the current cost
For the task you want to automate, note:
- How many times it happens per month
- Minutes spent each time
- Who does it and their approximate hourly cost
- Error rate and what each error costs to fix
Example: 1,200 invoices a month × 4 minutes = 80 hours of staff time.
Step 2: Estimate the improvement
Be realistic. Automation rarely removes 100% of the effort. If 85% of invoices are processed automatically and the rest need review, the time saved might be around 65–70 hours a month.
Step 3: Count the costs
- One-time setup cost
- Monthly software or API cost
- Hosting and maintenance
- Staff time for review
Step 4: Include revenue effects
Some automations increase revenue rather than cutting cost. Faster lead response, fewer abandoned carts and fewer missed follow-ups all bring in more sales. Track conversion rates before and after.
Step 5: Calculate payback
Payback period = setup cost ÷ (monthly savings + monthly revenue gain − monthly running cost). Many simple automations pay back within a few months; complex AI projects may take longer.
Step 6: Review after launch
Compare the real numbers 30, 60 and 90 days after go-live. Adjust or stop automations that do not deliver.
FAQs
What if the benefit is hard to measure?
Use proxy measures such as response time, customer ratings or errors caught, and agree them before starting.
Should we automate everything at once?
No. Start with one or two high-impact tasks, prove the value, then expand.
Are AI API costs predictable?
They are usage-based. We estimate them from your volumes and set alerts to avoid surprises.
