AI-assisted reporting, QA insights, daily briefings, trend analysis, and pattern spotting across support issues

Riding along on customer calls to coach my CSMs was one of the most well-intentioned things I did as a leader.

But if I’m being honest it was actually a disaster.

Here’s the reality, scheduling was a nightmare, my presence made customers question the CSMs credibility and authority, and I could tell my CSMs were nervous all day, not just during the call.

And when I tried to give helpful feedback after the call was over, it almost always landed as criticism. Even if that wasn’t my intention.

I wanted to develop my team, but what I actually did was create friction for my customers, my CSMs, and myself.

When call recordings came along they felt like the answer, and they were, in theory.

But in practice, all I had were folders full of calls and zero time to actually listen to them. Something always got prioritized over it. That's on me. But it was the reality for a lot of us.

The tools just weren't there yet.

That's changed.

I've been digging into Aircall's AI Assist Pro and honestly, it's the thing I wish I'd had when I was still managing a team.

Here's what actually caught my attention:

1️⃣ It surfaces call frameworks automatically. I'm a MEDPPICC leader, but you can build out whatever framework your team uses. It ensures your CSMs are hitting the right moments in every conversation, not just the ones they remember.

2️⃣ It brings competitive intel and product tips up in real time. Your CSM doesn't have to fumble through an objection, they have the answer right there. This is huge with product innovation moving so fast.

3️⃣ Every call gets scored automatically based on the criteria that matter to you. No more listening to 47 recordings to find the three that need attention. This also removes any bias and ensures everyone is being scored across the same criteria, the same way.

4️⃣ It spots trends across all your calls. So instead of guessing what customers are asking for, you actually know and have the data and insights to back it up.

I'm not running a CS team right now. But one of my clients is using this, and watching it in action made me a little jealous of the resources CS leaders have today.

The tools have come a long way since 2012.

Are you still riding along on calls or listening to recordings? Would you try this?

If you’re interested in checking this out, you can read more about the AI Assist Pro and their other AI features that are worth exploring.
https://bit.ly/4fRfow3

In partnership with Aircall.

Did we just launch the first real AI QA assistant that serves data, actions, patterns of your entire support team on demand? Yes, we did! That data was always there but it was locked down requiring days or weeks of processing, cleaning, formatting while it should be readily available and within immediate reach to any CX leader. There is still a lot more to build on top of this but the direction is that CX and QA leaders shouldn't have to wait for days or weeks to get critical insights about their team's performance and customers' experience. Real problems happen in real time and need to be dealt with the same level of urgency. That's why we're building Intryc (YC S24) , to give the superpower to every CX leader to take action when it matters the most - right when the issue is happening! Hit me up if you're curious to take our AI QA Assistant for a spin! We'd love your feedback as you try it! 🔥

Customer success is undergoing a significant transformation due to AI, and many are underestimating and underreacting to the extent of this disruption. Are you doing enough?

This question opened a gathering of women customer success leaders in New York yesterday, and the experience exceeded my expectations.

Here are a few key takeaways:

Women remain underrepresented in tech C-suite roles, particularly in SaaS, to the point where this metric is no longer even tracked.

C-suite leaders generally are masters of their own business area, but the differentiator of those who get to and succeed at that level is how well they are able to communicate the impact of their work to high-level stakeholders. TLDR; figure out how to map your work to the impact on the bottom line.

While AI can fundamentally alter how we approach the work, it can’t replace human interactions or touch (yet). How can we continue to identify those specific “human” aspects of the work while embracing AI to lighten the load?

I've been reflecting on how to help my team leverage AI in their work, but I also need to better understand how can I use AI to aggregate insights, uncover trends, and identify opportunities to work smarter to ultimately help my team.

The event was designed to foster a safe environment for open connections with partocipants who came to the table ready to share and learn. There was no ego in the room — even though I was sitting next to brilliant, accomplished, senior leaders. This atmosphere encouraged participants to share both challenges and successes, with others jumping in to share insights or suggestions- all among women meeting for the first time. It was incredible and inspiring to have a seat at the table.

I was fortunate to have my fellow Pluralsight leader, Jennifer Driggins , join me for the day. Together, we networked and built connections, synthesizing information that relates specifically to our work and the teams we lead.

I can’t wait for the next event! Thank you to Women of Customer Success for the invite to such a fabulous day.

As an AI enabled Head of of Customer Success I use the following stack to build small micro SaaS solutions for specific departments situations along with AI tools for the bread and butter of reporting, deal and client management.

Chatgpt - Plus (monthly) + API + Codex
Claude - Pro (annual) +API + Claude Code
VS Code
WSL
n8n (on occasions)
Zapier (simple)
Docker (Advanced)

#headofcustomersuccess
#ai
#customersuccess
#successbycs

How support quietly became engineering

I used to think “technical support” was a temporary stop. Something you do while you are figuring out what you really want. Then I blinked and realized I had spent years inside the most honest part of a product: the part where it breaks, where people panic, where they misunderstand something that felt obvious to the builder, and where the business either earns trust or quietly loses it.

That is when it clicked for me. Support was never small. It was just mislabeled.

And somewhere along the way, a new kind of role started forming. Not quite support. Not quite engineering. Not quite product. But somehow it touches all of them.

Customer Experience Engineer.

It sounds fancy until you realize it is basically: “the person who can translate chaos into clarity, and then fix the system so it stops generating chaos.”

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