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Director of Sales

Review rep performance and develop talent

Enhances✓ Available Now

What You Do Today

Analyze activity metrics, win rates, average deal size, and ramp time for each rep. Identify who needs coaching, who's ready for promotion, and who's in the wrong role.

AI That Applies

Sales performance analytics — AI identifies the behaviors that distinguish top performers from average reps and creates personalized coaching plans.

Technologies

How It Works

The system ingests average reps and creates personalized coaching plans 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 output — personalized coaching plans — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

You see that your top rep asks 3x more discovery questions and spends 60% less time on unqualified leads. You can teach what works instead of generic 'make more calls.'

What Stays

Developing people — having hard conversations, making hiring/firing decisions, building a culture that attracts top talent — that's leadership.

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 review rep performance and develop talent, understand your current state.

Map your current process: Document how review rep performance and develop talent works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
Identify the judgment points: Developing people — having hard conversations, making hiring/firing decisions, building a culture that attracts top talent — that's leadership. 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 Gong 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 review rep performance and develop talent 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 VP Sales or CRO

What data do we already have that could improve how we handle review rep performance and develop talent?

They're evaluating AI tools that will change your workflow

your sales ops or RevOps lead

Who on our team has the deepest experience with review rep performance and develop talent, and what tools are they already using?

They manage the CRM and data infrastructure your AI tools depend on

a sales enablement manager

If we brought in AI tools for review rep performance and develop talent, what would we measure before and after to know it actually helped?

They're building the training and playbooks around new tools

4

Check Your Prerequisites

Confirm readiness before you invest

Check items as you confirm them.