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VP of Revenue Operations

Compensation plan design and administration

Enhances◐ 1–3 years

What You Do Today

Design sales compensation plans that drive desired behaviors — new logo acquisition, expansion, multi-year commitments. Model plan economics, manage SPIFs, and handle the inevitable compensation disputes.

AI That Applies

AI simulates compensation plan outcomes across historical deal scenarios, identifying unintended consequences (gaming behaviors, perverse incentives) before plans go live.

Technologies

How It Works

For compensation plan design and administration, 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 recommended plan or schedule that accounts for the identified constraints and optimization criteria.

What Changes

Plan modeling becomes more sophisticated — testing against real deal data rather than hypothetical scenarios.

What Stays

Designing plans that motivate without creating gaming, managing the politics of compensation changes, and the judgment about which behaviors to incent.

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 compensation plan design and administration, understand your current state.

Map your current process: Document how compensation plan design and administration 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 plans that motivate without creating gaming, managing the politics of compensation changes, and the judgment about which behaviors to incent. 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 Xactly 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 compensation plan design and administration 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 compensation plan design and administration — 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

How would we know if AI actually improved compensation plan design and administration — what would we measure before and after?

They own the technology infrastructure that enables AI adoption

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.