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VP of Customer Success

Manage the renewal process and forecasting

Enhances✓ Available Now

What You Do Today

Oversee the renewal pipeline, ensure timely outreach, and forecast renewal rates with accuracy. When renewals are at risk, coordinate resources to save the business.

AI That Applies

AI renewal forecasting that predicts renewal probability for each account based on engagement, health, and historical patterns, improving forecast accuracy.

Technologies

How It Works

For manage the renewal process and forecasting, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output is a forecast with confidence intervals, showing both the central estimate and the range of likely outcomes.

What Changes

Renewal forecasting becomes more reliable. AI's probability scores outperform gut-feel estimates for portfolio-level prediction.

What Stays

Negotiating renewal terms with a customer who has leverage, managing pricing conversations, and the creative problem-solving when a customer's needs have changed.

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 manage the renewal process and forecasting, understand your current state.

Map your current process: Document how manage the renewal process and forecasting works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Negotiating renewal terms with a customer who has leverage, managing pricing conversations, and the creative problem-solving when a customer's needs have changed. 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 manage the renewal process and forecasting 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 the current accuracy of our forecasting, and how would we know if an AI model is actually better?

They shape expectations for how AI appears in governance

your CTO or CIO

Which historical data do we have that's clean enough to train a prediction model on?

They own the technology infrastructure that enables AI adoption

a peer executive at a company further along on AI adoption

Which steps in this process are fully rule-based with no judgment required?

Their lessons learned are worth more than any consultant's framework

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.