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VP of Customer Success

Build and develop the customer success team

Enhances✓ Available Now

What You Do Today

Recruit, train, and retain CSMs who combine relationship skills with business acumen and technical understanding. Build career paths and a team culture focused on customer outcomes.

AI That Applies

AI tools that automate routine CSM tasks — health monitoring, email sequences, data gathering — allowing CSMs to manage larger portfolios while maintaining quality.

Technologies

How It Works

The system ingests customer interaction data — transactions, communications, behavioral signals, and profile information. The automation engine executes each step in the process sequence — validating inputs, applying business rules, generating outputs, and routing exceptions to human review queues. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context. The best CSMs build genuine relationships where customers view them as trusted advisors.

What Changes

CSM productivity increases as AI handles administrative work. A CSM can manage more accounts because AI flags which ones need attention right now.

What Stays

The best CSMs build genuine relationships where customers view them as trusted advisors. That emotional intelligence can't be automated.

What To Do Next

This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.

1

Establish Your Baseline

Know where you are before you move

Before adopting AI tools for build and develop the customer success team, understand your current state.

Map your current process: Document how build and develop the customer success team works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: The best CSMs build genuine relationships where customers view them as trusted advisors. These are the boundaries AI won't cross.
Assess your data readiness: AI tools for this area need data to work. Check whether your organization has the historical data, integrations, and data quality to support CS platforms tools.

Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.

2

Define Your Measures

What to track and how to calculate it

Time per cycle

How to calculate

Measure how long build and develop the customer success team takes end-to-end today, then after AI adoption.

Why it matters

The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.

Quality of output

How to calculate

Track error rates, rework frequency, or stakeholder satisfaction scores before and after.

Why it matters

Speed without quality is just faster mistakes. Measure both.

When to check: Check after 30 days of consistent use, then quarterly.
The commitment: Give new tools at least 30 days before judging. The first week is always awkward.
What NOT to measure: Don't measure AI adoption rate as a KPI. Adoption follows value — if the tool helps, people use it.
3

Start These Conversations

Who to talk to and what to ask

your board chair or lead independent director

How would we know if AI actually improved build and develop the customer success team — what would we measure before and after?

They shape expectations for how AI appears in governance

your CTO or CIO

How much of build and develop the customer success team follows repeatable rules vs. requires genuine judgment — and can we quantify that?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.