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Business Development Representative

Research target accounts and build call lists

Enhances✓ Available Now

What You Do Today

Identify ideal customer profile accounts, find the right contacts, research their business challenges, prioritize your outreach list

AI That Applies

AI identifies and scores target accounts, finds decision-makers, surfaces relevant trigger events and talking points

Technologies

Account scoring AIContact discovery toolsTrigger event detection

How It Works

For research target accounts and build call lists, the system identifies and scores target accounts. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output — relevant trigger events and talking points — surfaces in the existing workflow where the practitioner can review and act on it.

What Changes

List building drops from hours to minutes. AI hands you a prioritized list with personalized talking points for each

What Stays

Gut feel for which accounts are worth your time, recognizing when an AI-scored 'low' account has a hidden angle

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 research target accounts and build call lists, understand your current state.

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Map your current process: Document how research target accounts and build call lists 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: Gut feel for which accounts are worth your time, recognizing when an AI-scored 'low' account has a hidden angle. 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 Account scoring 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 research target accounts and build call lists 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 research target accounts and build call lists?”

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 research target accounts and build call lists, 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 research target accounts and build call lists, 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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