Not everything “AI” needs to feel like rocket science! Especially in Customer Success, where AI automation should solve real pain, not create new ones.

If you’ve read one of those viral posts talking about “AI Agents vs LLM Workflows vs Agentic Architectures”… and thought:

“Cool visual… but how does this help me reduce churn, scale onboarding, or clean up our Jira chaos?”

You’re not alone.

Let’s break this down in a way Customer Success leaders actually need to hear it.

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🧠 Step 1: LLM Workflows

What it is: AI that summarizes, rewrites, or answers based on a single input. Think: ChatGPT “inside your tools”.

🔧 Use Cases:

• Automatically summarize customer call transcripts and add to CRM
• Turn support chats into helpful FAQs for your Help Center

👉 Great for: saving time on repetitive content work.

This is easy and this is where you should start - now!

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🤖 Step 2: RPA (Robotic Process Automation)

What it is: Rules-based automation that does a fixed job when triggered.

🔧 Use Cases:

• When a renewal is marked at risk in your CRM, automatically assign a Jira task to the account’s CSM
• Auto-send NPS follow-up emails based on score thresholds in your customer feedback system

👉 Great for: cutting manual busywork and building repeatable processes.

These features exist in some SaaS platforms, but they’re also easy to implement without committing to a specific tool.

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🕹 Step 3: AI Agents

What it is: AI that decides which tools to use, takes action in sequence, and remembers context.

🔧 Use Cases:

• Monitor support tickets, flag product bugs, file detailed Jira issues, and notify the right Slack channel
• Detect onboarding friction in usage data and trigger a personalized email with tailored recommendations and resources (e.g. branch-specific use cases)

👉 Great for: automating cross-functional problem-solving and proactive success actions.

This definitely is not sci-fi but just a smart combination of tech and process mapping along the customer journey map.

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🌐 Step 4: Agentic AI (still emerging)

What it is: A team of AI agents working together with coordination and memory.

🔧 Use Cases:

• One agent analyzes churn patterns
• Another drafts an individual success strategy for each customer
• Another implements email campaigns across all customer channels, autonomously

👉 Great for: long-term scale and strategic automation.

Nice to know, but not your focus right now 😉 Let the R&D in LAMs (Large Action Models) evolve, and focus on the steps that drive impact today.

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🎯 TLDR

Start where it helps your team today. Your CS tech stack is ready.

👉 It's about knowing what to automate, and how.

And if the explanations you're getting from providers or tech colleagues sound more like backend architecture than business value... Without ever mentioning something essential like the customer journey map…

You're asking the wrong people. 👀

Start with your problems, not with the tech.

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💡 We help CS teams bring real AI automation to life.

DM me, comment or join our Lunch&Learn Fridays if you're curious how we help CS teams implement AI that actually reduces churn, increases adoption, and makes onboarding 10x smoother.