is your CS team really using AI? (8 capabilities to master)

"We're exploring AI for our CS team."

It's the typical response from CS leaders today.

But, when pressed, most fall back on vague descriptions of unorganized prompts or basic email assistance.

The truth? Most CS organizations are barely scratching the surface of AI, thinking incrementally instead of transformationally.

As Jacob Bank recently highlighted, CS leaders who understand eight specific AI capabilities will operate at a step-function above their peers. These aren't futuristic possibilities — they're concrete applications reshaping how elite CS teams operate.

Let's break down these capabilities...

The 8 AI Capabilities Reshaping Customer Success

1. Extraction

What AI does: Pulls structured information from unstructured sources.

How leading CS teams use it: Building a system automatically extracts key details from customer meetings, emails, and support tickets, then updates CRMs without CSM involvement. Frees CSMs from 5-7 hours/week of manual data entry, allowing your CSMs to focus on relationship-building while your CRM stays current (without human effort).

2. Summarization

What AI does: Condenses lengthy content into concise insights.

How leading CS teams use it: Creating quick "customer pulse" briefs that compile key signals from the last 30 days of interactions across all channels into a 2-minute read. Gives CSMs actionable insights from support, usage, and community signals before outreach.

3. Content Creation

What AI does: Generates high-quality original content from basic inputs.

How leading CS teams use it: Developing personalized drafts (QBRs, adoption guides, etc.) from templates. Lets CSMs review/refine customer-specific content, saving hours of manual creation.

4. Synthesis

What AI does: Combines multiple information sources into coherent insights, looking for patterns or trends.

How leading CS teams use it: Looking at open-text trends across NPS surveys to find meaningful insights that you could pass along to other teams.

5. Research

What AI does: Gathers relevant information about specific topics.

How leading CS teams use it: Creating executive briefings before strategic meetings. These briefings include company news, personnel changes, and industry trends, equipping CSMs as informed partners.

6. Analysis

What AI does: Examines data to identify patterns and make recommendations.

How leading CS teams use it: Analyzing product usage data and other signals to draw out key insights for your CSM to use on your next call. Enables genuinely prescriptive guidance beyond simple dashboards.

7. Grading

What AI does: Evaluates against criteria to make assessments.

How leading CS teams use it: Grading strategies or best practices that we want to present to customers — don’t put something in front of a customer until it gets an A.

8. Coaching

What AI does: Provides feedback and improvement suggestions.

How leading CS teams use it: Creating internal training tools that review CSM-customer interactions and provide personalized coaching on communication effectiveness.

Democratizes expertise, accelerating CSM skill development without heavy 1:1 manager time.

The Current Reality: Navigating the AI Tooling Landscape

The AI possibilities are clear. The real challenge? Navigating today’s fragmented tooling landscape to actually make them happen.

Most CS organizations are wrestling with:

Disparate AI tools across your stack: AI capabilities are scattered — some built into your existing CS tools (Gainsight, Salesforce, etc.), others standalone (think ChatGPT), and some requiring custom builds.

Integration gaps: Customer data exists across multiple platforms that don't easily talk, creating friction in building end-to-end AI workflows.

Varying levels of AI quality: Not all built-in AI features deliver the same quality — a summarization feature in your CRM might not match what a specialized AI tool can provide.

To cut through this and actually implement those eight AI capabilities, you need two foundational elements:

First, an LLM strategy. Whether leveraging APIs (OpenAI, Anthropic, etc.) or training custom models on your proprietary data, you need LLMs that understand your specific customer context, industry jargon, and product terminology.

Second, workflow automation tools. The "magic" isn't just the AI — it's connecting it to your existing systems. You need tools to orchestrate workflows across your tech stack (pull data from CRM, process via LLM, and push to your customer engagement platform).

The smartest companies aren't waiting for a perfect, all-in-one AI solution.

They're starting lean: picking one high-impact capability-journey intersection, using available tools to create initial value, and then expanding systematically.

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