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VP of Customer Success

Design and optimize the customer journey

Enhances◐ 1–3 years

What You Do Today

Map and improve the end-to-end customer experience — onboarding, adoption, value realization, renewal. Identify friction points and design interventions that accelerate time to value.

AI That Applies

Journey analytics that track how customers actually move through your product and identify where they get stuck, drop off, or fail to achieve their goals.

Technologies

How It Works

The system ingests how customers actually move through your product and identify where they get stu as its primary data source. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.

What Changes

Journey optimization becomes data-driven. AI shows you exactly where customers struggle and what successful customers do differently.

What Stays

Designing the human touchpoints — the welcome call, the business review, the executive check-in — that build the relationships driving retention.

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 design and optimize the customer journey, understand your current state.

Map your current process: Document how design and optimize the customer journey works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Designing the human touchpoints — the welcome call, the business review, the executive check-in — that build the relationships driving retention. 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 Gainsight 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 design and optimize the customer journey 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

What's our current capability gap in design and optimize the customer journey — and is it a people problem, a tools problem, or a process problem?

They shape expectations for how AI appears in governance

your CTO or CIO

What's the risk if we DON'T adopt AI for design and optimize the customer journey — are competitors already doing this?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.