Real example · Automation & AI agents
Let AI sort customer text replies and close the routine ones
What they did
Stellantis &You UK, a dealer group with 30+ UK locations, built a Make automation that sends booking confirmations and reminders by SMS and uses OpenAI to read customer replies, flag the ones Aftersales must handle and close the rest.
How it went
Over 12 months it analysed 'over 47,000 messages' and automatically closed 'over 18,000', which the team says saved '151 hours' based on a 30-second review time; they were on 'version 13 of the prompt.'
Worth knowing
Vendor case study on Make's site; the time saving is calculated rather than measured, and this is an AI-assisted workflow, not a fully autonomous agent.
Try it yourself with Make
Replace anything in [brackets] with your details.
Design a Make scenario that: (1) sends an SMS confirmation and a reminder [24 hours] before each [appointment or booking] from [booking system]; (2) reads each customer reply; (3) classifies it as confirm, reschedule, cancel, question or other; (4) for reschedule, cancel and question, notifies [team] in [Slack or email] with the message and customer details; (5) closes the routine confirmations automatically.
List the modules in order, the AI prompt for the classification step, and how to test it with ten sample replies.Read the original
Stellantis &You UK Cuts 151 Hours of Mundane Work with Make ↗
make.com · · Vendor case study
Attributed to the source linked below; not our own test.
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