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Business Development Representative

Make cold calls and handle objections

Enhances✓ Available Now

What You Do Today

Dial 40-60 numbers, navigate gatekeepers, deliver a value prop in 30 seconds, handle 'not interested' and 'send me an email'

AI That Applies

AI provides real-time call coaching, surfaces objection-handling scripts, scores calls for quality improvement

Technologies

Real-time call AIObjection handling databasesCall scoring

How It Works

For make cold calls and handle objections, 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 — real-time call coaching — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

Real-time coaching makes every rep perform closer to the top performers. Better call prioritization

What Stays

Your energy and confidence on the phone, recovering from rejection 50 times a day, the human connection in a 30-second window

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 make cold calls and handle objections, understand your current state.

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Map your current process: Document how make cold calls and handle objections works today — who does what, how long it takes, where the bottlenecks are. You need this baseline to measure improvement.
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Identify the judgment points: Your energy and confidence on the phone, recovering from rejection 50 times a day, the human connection in a 30-second window. These are the boundaries AI won't cross.
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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 Real-time call AI 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 make cold calls and handle objections 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 make cold calls and handle objections?”

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 make cold calls and handle objections, 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 make cold calls and handle objections, 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.

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