Once a business starts running vendor and client conversations through Zoho Cliq's external chats and channels, a natural next question is whether artificial intelligence could take some of the manual effort out of managing them. What follows is a set of ideas worth exploring rather than a description of any specific product feature, so treat it as food for thought when planning your own tooling.
When several external channels and one-to-one chats are running at once, it is easy for an urgent message from a client to sit unread behind a dozen routine updates from a vendor. You could explore using an AI-based classifier to scan incoming external messages and flag the ones that look time-sensitive or that mention key terms your team cares about, such as a deadline, a complaint, or a specific order number, so a human can jump on those first rather than working through everything in strict chronological order.
External channels that run for weeks or months on a single project can build up a lot of history. You might look into whether an AI summarisation tool could be pointed at a channel's history to produce a short recap of decisions made and outstanding questions, which would be particularly useful for bringing a new team member up to speed without asking a colleague to manually retell the whole story.
Because external users are, by definition, outside your organisation, there is always a small risk of someone sharing something they should not, a customer's personal details, an internal price list, or a document that was meant to stay in-house. It could be worth investigating AI-driven pattern detection that scans outgoing messages in external chats for likely sensitive content, such as patterns resembling card numbers or attachments with certain file names, and prompts the sender to double-check before the message goes out. This kind of safeguard would need to be evaluated carefully against your own data protection obligations before being relied upon.
For routine, repetitive questions from vendors or clients, checking on a delivery status, confirming an appointment, restating a standard policy, you could consider whether an AI assistant might draft a suggested reply for a staff member to review and send, rather than typing one from scratch each time. The key word here is suggested, since a human should stay in the loop to make sure the response is accurate and appropriate for that particular external contact.