Every routine sales and support conversation ends the same way: the call wraps up, and then the practical work begins. Updating the CRM. Logging the ticket. Booking the follow-up. Chasing the next step. The conversation finishes in moments. The admin that follows can take twenty minutes.

Multiply that across a team of twenty reps and a few hundred calls a week, and the admin tax on your revenue and support operations starts to look a lot bigger than the conversations themselves.

That’s the gap AI Actions closes.

TL;DR:

Built directly from agent feedback, Helpdesk 2.0 fixes real workflow pain points. The redesign focuses on reducing friction and helping agents handle more context-heavy tickets.

A chat-style interface replaces the old email layout. Conversations are easier to follow and resolve in one view.

Customer context is shown beside the conversation in a right-side panel. Agents can view history, orders, and details without leaving the ticket.

AI handoffs come with clear summaries. Agents instantly see what happened, what was tried, and what to do next.

Navigation is simpler and faster across teams. Clean menus, structured queues, and multi-store access keep agents moving efficiently.

I have a challenge for anyone who thinks AI will replace their job entirely.
Try calling Booking.com's AI agent and ask to speak to a real person. Just that. I did ,multiple times ,for three very simple reasons:

1 I personally prefer it
2 I needed to cross-check multiple policies
3 It is literally my right as a customer

The AI didn't listen. It couldn't. It just kept looping back to the same question, no matter how many times or how clearly I answered.
So here's what I'd say to every customer service professional reading this: your job is safe as long as you can do four things the AI couldn't:

✅ Use your brain
✅ Apply common sense
✅ Put the customer first
✅ Know when to stop the script and actually help

AI is a powerful tool. But a tool with no judgement, no empathy, and no ability to hear "I want a human" is not a replacement for a great customer service rep.

Your seat at the table is secure. Don't let anyone tell you otherwise.

FYI this post was done by Claude to make the Irony of it.

#ai #costumerfirst #UAE
#CustomerExperience #AI #CustomerService #HumanFirst #CX

I recently created a short walkthrough on setting up a customer support AI agent for a website, and wanted to share the basic workflow here.

The setup process I followed was:

Create the AI agent

Configure the basic settings

Train the agent using website pages

Add specific webpages manually if needed

Use advanced crawling settings for better control

Add files or direct text content for extra knowledge

Customize the widget tabs

Preview the widget before publishing

One thing I noticed is that the quality of the agent depends a lot on how clean and specific the training content is.

If the website content is too generic, the agent gives generic answers. But if the content is structured well, the responses become much more useful.

For customer support use cases, I think the most important parts are:

- Clear FAQ content

- Product/service details

- Pricing or plan information

- Contact/support escalation rules

- Lead capture questions

- A proper fallback message when the agent does not know the answer

I also feel that businesses should not treat AI agents as just chat widgets. The real value comes when the agent is trained properly and connected to business outcomes like support, lead capture, booking, or qualification.

I recorded the setup process here in case it helps anyone:

https://youtu.be/eakbdcI6a0I?si=OtbsGFba46YjmJi_

Would love to know how others here are training AI agents for customer support. Are you mostly using website content, documents, API integrations, or a combination?

So I have been deep in this space for about a year now across our support queue and honestly the conversations I keep seeing online still feel too clean compared to what actually happens in production.

Here is what I have actually learned from running this:

Intercom Fin - strong at deflecting repetitive volume but the setup to get it talking properly about your specific product is more work than they make it sound

Zendesk AI - powerful if you are already deep in the ecosystem, felt clunky to configure outside of it

Ada - serious automation muscle but when it misses it misses confidently which is the worst version of wrong

Chatbase - been on this one the longest, about a year now. The Zendesk integration is what kept us on it. When the agent cannot resolve something the full conversation history transfers with the ticket automatically so agents never pick up cold. 71% resolution rate, CSAT held.

Freshdesk Freddy - fine for getting started, hit its ceiling faster than expected

The thing nobody talks about enough is the maintenance side. Every single one of these tools is only as good as what you feed it and how often you update it. The ones that fell apart on us fell apart because we treated them like infrastructure instead of something that needs a weekly 15 minute review.

The bar has shifted from can it reply to can it actually close the ticket. But I would add a third question now: can it stay accurate six months after you deployed it without someone actively maintaining it. That is where most of them quietly fail.

What are you all running? And genuinely curious if anyone else has had something work great in month one and then slowly fall apart.

Most customers don’t hate AI in support.

They hate feeling stuck in a polite, unhelpful loop with no way out.

Right now, a lot of Support leaders are under real pressure to “get AI in place.” Success gets measured in deflection, containment, and lower handle time. And yes—those metrics matter.

But here’s the risk: you ship a chatbot that looks great on a dashboard… while quietly eroding trust every time it blocks a frustrated customer from reaching a human.

On our team, AI now touches almost every ticket and the biggest shift in my thinking has been this: Once AI is everywhere, the most important question isn’t “What percent of tickets are automated?”

It’s: “Where did this interaction increase, or decrease, user trust?”

Because you can absolutely hit your automation goals and still deliver a brittle, bad experience. That’s where leadership comes in.

Every AI touchpoint in support is a product surface, whether you treat it that way or not. And that means making intentional decisions like:
• Where do we guarantee a fast, clear path to a human, even if the bot could keep going?
• What context does a rep receive so the customer never has to repeat themselves?
• Would we consider the bot response a “good answer” if it came from a teammate?

If you’re leading Support or CX this year, try this: Pick one AI-powered journey.
Audit it for trust—not volume. Read 20 transcripts.

Look for:
– Where customers try to escape the flow
– Where reps feel constrained by what the bot already said
– Where a faster path to a human would have changed the outcome

That’s your roadmap.

#CustomerSupport #CustomerExperience #AI #SupportLeadership #HumanFirst

I wanted to test a simple idea: what if you architected an AI support agent the same way? Give it a training manual instead of a workflow tree. Give it Google Search instead of a RAG pipeline. And use a single multimodal model so you don’t need separate systems for voice, text, and vision.

I built Cortado for the Gemini Live Agent Challenge to explore what that looks like in practice.

...

Hey everyone 👋

I'm Krupesh, and I just launched SiteSupport - an AI customer support chatbot that trains on your actual website content.

Why I built this:

Customer support was killing me. I'd spend 4-5 hours a day answering the same questions over and over:
- "How do I reset my password?"
- "What's included in the Pro plan?"
- "Do you support Shopify?"

I looked at existing chatbots, but they were either:

Generic AI (ChatGPT-style) that would hallucinate answers

Rule-based bots that felt robotic and broke constantly

Enterprise solutions that cost $500+/month and took weeks to set up

So I built something in between.

What makes it different:

✅ Trains on YOUR content - Just paste your URL. We crawl your docs/FAQ and train the AI on that ONLY. No generic answers, no hallucinations.

✅ Actually fast setup - Most users go live in under 5 minutes. No complex config.

✅ Affordable - Starts at $29/mo. Not $500/mo enterprise pricing.

✅ Works everywhere - WordPress, Shopify, React, plain HTML. One line of code.

Early results:
- One user said it handled 60% of their support tickets in the first week
- Average response time: under 3 seconds
- Customers actually prefer it for simple questions (they don't have to wait for email replies)

What it WON'T do:
- It's not perfect. Complex issues still need humans.
- It won't replace your support team (but it'll make them way more productive)
- If your docs suck, the chatbot will suck too (garbage in, garbage out)

Try it free: https://www.sitesupport.ai (14-day trial)

Live demo: There's an interactive demo on the homepage where you can chat with an AI trained on SiteSupport's own content. Ask it anything.

Would love your feedback! What features would make this actually useful for YOUR business?