Meet my team of AI assistants and skills: Hunter, Clarice, the Board, and a few other tools I use every day.

I explain how I set each one up, and what's in it for you, whether you're a CSM or you sit in any revenue-generating, customer-facing role.

Still figuring this out myself, so I'd genuinely love to know how you're using AI.

https://lnkd.in/eyE4MaPs #CustomerSuccess #ArtificialIntelligence #CS

61 percent of your users will leave for a competitor after exactly one bad experince.

I was biting my tongue today during a hallway conversation so I would not scream this number out loud. A colleague asked why we even need human Customer Support anymore. "Why not just automate everything with AI and close the tickets?"

I love tech people. I really do. But sometimes they think human frustration can be resolved with a version update.

Do not get me wrong, AI is a fantastic sidekick. I use it to answer simple tickets. To summarize past chats, analyze sentiment, and pull up user history. But AI has absolutely zero judgment.

Take a classic edge case. A user buys a time-based package, but real life happens and they cannot use it. The database shows the purchase was delivered successfully. If an AI handles this, it looks at the policy, sees a successful delivery, and politely tells the user to go away.

A human looks at the exact same ticket, realizes we want this person to actually like our brand, bends the rules, and restores the package.

Trap a frustrated user with a bot that makes a mistake, give them no way to reach a real human, and trust is instantly broken. People just want to know there is an actual person behind the system.

Use AI to read the data. Use humans to read the room.

The Jeffri Profile

That's my customer support persona and connects to HelpScout.

Every morning at 6AM he scans the inbox and closes out spam or junk from our inbox. As new emails come in he writes draft, we say yes, he sends. Closes tickets once solved.

Writes a new “customer page” for every customer in Gbrain so we can track feedback and issues.

Used to be a manual task someone had to remember.

Now it just... happens.

Also helps scrub PII with customer support.

A new automation potential report analyzes your customer conversations and identifies requests that can be automated with AI agents. This report provides brand-specific insights and sample ticket data, showing you exactly how an AI agent would respond to customer inquiries.

(Advanced) Agentic AI for advanced email AI agents is now generally available. This enables AI agents to understand emails, answer questions, automate procedures, and escalate when needed, reducing back-and-forth with customers.

Copilot now includes macro content suggestions and trust and safety recommendations. These new recommendation types help you improve agent productivity and account security without spending time going through complex settings.

Generative search in help center now supports a follow-up question for customers using the Web Widget. This enhancement creates a smoother path from self-service search to a conversational experience with an AI agent, reducing repetition and keeping context from the original search.

Does anyone else ignore emails or dms that look like they came right out of ChatGPT? We’ve reached a point where people and companies are losing their individuality because they think having AI write their communications is a time saver. It can be, but most people are doing it wrong. If you haven't noticed, AI has its own voice (“Let’s be real…” and “rIcH tApEsTrY”), its own punctuation style (RIP emdashes), and it feels cold. Not only does it remove all semblance of your personality, but it also threatens retention. When customers feel like they’re talking to a chatbot instead of a brand they trust, they start feeling less connected to it. No trust means no loyalty, and a high potential for churn. “But I’m just having AI build on my original thought!” You can spend an hour writing a marketing email, but the second you ask AI to “make it sound better,” you’ve lost some of that originality. The idea might be yours, but if it doesn’t sound like you, people won’t connect it to you. It will feel lazy to your audience, and they’ll assume you had AI write it for you. A former employer would send ChatGPT emails on my behalf that were just cheesy. As a Senior Customer Success Manager and CX leader, that can impact a relationship I’ve built. My job is to help clients get the most out of a product, not to send them spam. By no means do you have to give up AI, you just have to train it. I use Gemini Gems for this. I fed mine my past webinars, emails I’ve actually written, and success stories to create a "Voice Rules" doc. After that, I kept testing the Gem until it actually sounded like me. You can do this for an entire company brand voice too. Invest an hour into uploading your mission, your pitch, and past marketing. Even when you can get it to sound 90% like you, the AI suggestion is just a first draft. Take one extra minute to reread it and rewrite that 10%. A customer, a lead, or a hiring manager is more likely to respond to something that feels human. With all of the AI spam people get in DMs and emails, a human sounding email is the only way to actually stand out. #ai #customersupport #customersuccess

I recently completed a hands on training on AI, and it genuinely shifted how I think about my work especially in customer support and content management.

One question from the session stayed with me:
“If AI could take one task off your plate today, what would it be?”

For me, it’s content ideation and scheduling.

Not because I can’t do it, but because AI helps me do it faster, more consistently, and with better structure which matters when you’re balancing customer support and content responsibilities.

During the training, I saw practical ways AI can support:
• Faster response drafting and customer communication
• Understanding customer behaviour and preferences
• Content planning and workflow organisation

What stood out to me is this AI isn’t replacing people in support roles. If anything, it helps us show up better:
More responsive. More organised. More intentional.

As someone working in customer service and social media management, I’m already applying this to improve response time, content flow, and overall user experience.

No certificate for this one, but the value is in the implementation and I’m already using it.

Curious if AI could take one repetitive task off your plate today, what would it be?

Most people who talk about using AI in customer success are either selling something or haven't actually shipped anything real. I've been running customer support for a B2B SaaS company for about four years, and I want to share what genuinely changed things for us, because the early experiments were a mess.

When we first started plugging AI tools into our support workflow, we made the classic mistake of trying to automate too much too fast. We had this idea that we could reduce ticket volume by 60 percent in three months and free up the team to focus on strategic account work. What happened instead was that customers got looped in weird automated conversations, reps got confused about what the AI had already said, and handoffs were a disaster. One enterprise client nearly churned because the AI gave them a technically correct but completely unhelpful answer to a billing question, and no human caught it in time.

Here is what we changed and what actually stuck.

First, we stopped thinking about AI as a replacement for the first touch and started thinking about it as a tool for the boring repeatable layer underneath everything else. The questions that come in fifty times a day, the ones your most experienced rep could answer in their sleep, those are fair game. Password resets, how to export reports, what the cancellation policy is, how to add a new seat. Get that list of your top twenty recurring tickets and build your AI layer around those specifically. Do not try to make it generalist from day one.

Second, we got ruthless about handoff signals. The moment a customer uses words like frustrated, escalate, urgent, cancel, or mentions a specific dollar amount, the system flags it for a human immediately. No exceptions. The AI is allowed to acknowledge the message and say someone will follow up shortly, but it does not attempt to resolve anything beyond that. This alone saved us two near-churns in the first quarter after we implemented it.

Third, and this one took us a while to figure out, we started feeding the AI our actual documentation rather than generic training data. Sounds obvious but we were not doing it at first. Once we connected it to our real help articles, our internal runbooks, and even our onboarding FAQs, the accuracy went from about 60 percent satisfactory to around 85 percent in a few weeks. The tool still gets it wrong sometimes, but now it is wrong in explainable ways rather than random ones.

For tooling specifically, we went through a few iterations. We started with a well-known support platform's built-in AI, which was fine but limited. We eventually moved to a setup where we use a dedicated video tool to create short explainer clips for common issues, which we attach to AI responses for anything procedural. So instead of the AI writing out six steps to configure a webhook, it just sends a sixty-second screen recording. Customers love that. For creating those clips at scale without needing our design team involved every time, we have been using atlabs, which lets us batch-produce short instructional videos from scripts pretty quickly. That is not the centerpiece of our stack, but it plugs a real gap.

For B2C, the calculus is a little different. Volume is higher, questions are simpler, and customers have less patience for anything that feels robotic. The key there is tone calibration. Your AI responses need to sound like a human typed them even when they are templated. Run every AI response through a basic tone check before it goes live. Friendly, direct, no corporate fluff.

For enterprise B2B, the priority is not speed, it is accuracy and escalation clarity. Enterprises will forgive a slower response if it is correct. They will not forgive a fast wrong one.

The honest truth is that AI in customer success is not magic. It is infrastructure. You build it carefully, you instrument it properly, and you keep humans in the loop for anything with real stakes. Do that and it is genuinely useful. Skip any of those steps and you are just creating new problems faster than you were before.

Most support teams think they're AI-fluent.

Zapier's new rubric says otherwise.

They just published an AI fluency framework for support, and it's the clearest bar I've seen for measuring where your team actually stands.

Four levels:

🔴 𝗨𝗻𝗮𝗰𝗰𝗲𝗽𝘁𝗮𝗯𝗹𝗲: "AI is a slightly faster Google, nothing more."

🟡 𝗖𝗮𝗽𝗮𝗯𝗹𝗲: "I use AI to operate at a meaningfully higher level."

🔵 𝗔𝗱𝗼𝗽𝘁𝗶𝘃𝗲: "I orchestrate AI and build systems that elevate how I work."

🟣 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝘃𝗲: "I re-engineer how work happens."

That last one is the bar. Not just faster work. Different work. Categories of tasks that no longer exist or run without human involvement.

Most teams I talk to are somewhere between Unacceptable and Capable. Using AI for drafts and lookups, but not changing how work actually gets done.

And here's the part managers need to hear: a leader who's personally AI-fluent but whose team is still doing things the old way doesn't meet Zapier's bar. You have to prove you've led your team there too.

This is exactly why I just opened a new role focused on internal AI enablement. Not customer-facing AI. Internal. Helping my team level up, build workflows, and move from Capable to Transformative.

More on that this week.

Where does your team fall on this rubric? 👇

I posted something similar a few months ago asking for recommendations on AI chatbots to pair with Zendesk Support. Got some good replies, did a lot of testing, and wanted to share what I actually found because I had trouble finding real-world comparisons when I was searching.
...

My team was spending 40 hours a week on something AI now does in 3. Let me explain 👇

We had a customer service triage process eating one full-time employee's entire week.

Every inquiry: manually categorized, routed, logged. Slow. Error-prone. Expensive.

So we built an AI workflow in 3 days:
→ Incoming messages auto-categorized by type and urgency
→ AI Agents answered 80% of requests within seconds
→ Other 20% escalated to response templates pre-drafted for human approval
→ Human reviews and sends — still in the loop, just 10x faster

Results after 60 days:
→ Response time: 6 hours → 22 minutes
→ Team member now focused on higher-value work
→ Customer satisfaction up 24%
→ AI Agent costs: ~$200/month

This isn't science fiction. This is a basic use-case any operator can build.

The founders who win the next 5 years won't be the ones who "wait and see."

They'll be the ones who implemented while everyone else was reading articles about it saying AI isn't 'ready' yet

What process is eating your team's time right now?