How to Build Prospect Lists with AI: A Step-by-Step Guide

Building prospect lists with AI takes minutes instead of hours. You describe your ideal customer, and AI does the research—finding companies, verifying contacts, and surfacing buying signals automatically.

After helping hundreds of sales teams make this transition, here's the exact workflow that works.

Why Traditional Prospecting Is Broken

The math doesn't work anymore.

A typical SDR spends 6+ hours per week on manual research: scrolling LinkedIn, cross-referencing databases, verifying emails, copying data between tools. That's 300+ hours per year of repetitive work that AI can do in seconds.

Meanwhile, the data they're working from decays at 30% annually. By the time you've built a list, a quarter of it is already outdated.

The top-performing sales teams have figured this out. They're not working harder—they're working with AI.

The AI-Powered Prospecting Workflow

Step 1: Define Your Ideal Customer Profile

This is where most teams go wrong. They jump straight to tools without clarity on who they're looking for.

Get specific on these dimensions:

Company Attributes

• Industry and sub-vertical
• Employee count range
• Revenue range (if known)
• Geographic location
• Funding stage
• Technology stack

Buying Signals

• Currently hiring specific roles
• Recent funding announcement
• Leadership changes
• Expansion into new markets
• Technology adoption patterns

Contact Criteria

• Decision-maker titles
• Department focus
• Seniority level
• Required authority (budget, technical, influence)

Step 2: Translate ICP to a Query

Here's where AI shines. Instead of clicking through filters, you describe what you want in plain English.

Example queries:

"Series B SaaS companies in the US with 100-500 employees that are hiring for sales roles"

How Origami executes this:

1. Searches 69M+ LinkedIn companies for B2B SaaS firms
2. Filters by funding stage (Series B) and size (100-500)
3. Checks live job postings for sales hiring activity
4. Creates table with company data
5. Enriches with decision-maker contacts
6. Verifies emails

"E-commerce brands on Shopify with estimated revenue over $5M that recently added a new marketing executive"

How Origami executes this:

1. Searches Shopify store database
2. Filters by estimated revenue ($5M+)
3. Scrapes LinkedIn for new marketing executive announcements
4. Gets verified contact info for each executive
5. Returns enriched list with context for personalization

"Fintech startups that announced funding in the last 90 days and are based in New York or San Francisco"

How Origami executes this:

1. Searches news and funding databases for recent announcements
2. Filters to fintech industry
3. Filters by NYC/SF location
4. Enriches with company details and decision-maker contacts

"Manufacturing companies with 1000+ employees using Salesforce that have open IT positions"

How Origami executes this:

1. Searches for companies by tech stack (Salesforce users)
2. Filters by industry (manufacturing) and size (1000+)
3. Checks job postings for IT roles
4. Returns companies with contact data

The AI interprets your intent and orchestrates searches across 15+ data sources autonomously.

Step 3: Review and Refine Results

AI delivers a first pass of prospects. Now you add human judgment:

Quality check:

• Do these companies match your ICP?
• Are the contacts at the right level?
• Do the buying signals seem relevant?

Refine your query:

• Too many results? Add constraints
• Wrong industry mix? Be more specific about verticals
• Missing key companies? Check your exclusion criteria

This feedback loop trains the AI to better understand your preferences.

Step 4: Enrich with Context

Raw company and contact data isn't enough. You need context for personalized outreach:

• Company news: Recent announcements, press coverage, product launches
• Executive backgrounds: Career history, education, shared connections
• Technology stack: What tools they use that relate to your solution
• Competitive intelligence: Current vendors, pain points, switching signals

AI can gather this context automatically, saving hours of manual research per account.

Step 5: Export and Activate

Push enriched prospects directly to your workflow:

• CRM: Create leads or contacts in Salesforce, HubSpot
• CSV Export: Download for import into any tool
• CSV Import: Upload existing lists to enrich with Origami's data
• Sequences: Add to Outreach, Apollo, or Salesloft campaigns
• Google Sheets: Direct export for custom workflows
• Slack: Get notifications for high-priority prospects
• API: Programmatic access for custom integrations

Common Prospecting Use Cases

Use Case: Finding Fast-Growing Companies

Query: "Companies with 50-200 employees that have grown headcount by 40%+ in the last 6 months"

Why it works: Rapid growth indicates budget, urgency, and willingness to invest in new tools.

Use Case: Tracking Funding Rounds

Query: "B2B software companies that announced Series A or B funding in the last 60 days"

Why it works: Fresh funding means budget to spend and pressure to show results quickly.

Use Case: Identifying Leadership Changes

Query: "Companies where a new VP of Sales or CRO joined in the last 90 days"

Why it works: New leaders often reevaluate vendor relationships and bring their preferred tools.

Use Case: Technology-Based Targeting

Query: "Companies using HubSpot with 500+ employees in the healthcare industry"

Why it works: Technology signals indicate sophistication, integration requirements, and competitive positioning.

Use Case: Geographic Expansion

Query: "US-based companies that recently opened offices in Europe"

Why it works: Expansion creates new needs and buying windows for supporting tools and services.

Building vs. Buying: When to Use AI Prospecting

Use AI for:

• Net-new prospecting: Finding accounts you've never heard of
• Market expansion: Entering new verticals or geographies
• Signal monitoring: Tracking buying triggers in real-time
• Data enrichment: Adding context to existing leads
• Continuous refresh: Keeping prospect lists current

Manual research still makes sense for:

• Named enterprise accounts: Deep, strategic research for specific targets
• Relationship mapping: Understanding complex buying committees
• Competitive intelligence: Nuanced analysis of competitor customers
• Custom due diligence: Evaluating strategic partners or acquisitions

Metrics That Matter

Track these to measure AI prospecting effectiveness:

Metric	What It Measures	Target
List accuracy	% of contacts with valid emails	>90%
ICP match rate	% of companies matching criteria	>85%
Time to list	Hours saved vs. manual process	80%+ reduction
Conversion rate	% of AI-sourced leads becoming opportunities	Compare to other sources
Data freshness	Age of company/contact information	<30 days

Best Practices for AI Prospecting

1. Start Narrow, Then Expand

Begin with a tightly defined ICP. It's easier to broaden criteria than to filter through noise.

2. Iterate Quickly

Don't spend hours perfecting your first query. Get results, review, and refine. The feedback loop is where value compounds.

3. Combine Multiple Signals

The best prospects match multiple criteria. A company that just raised funding AND is hiring for your target role AND uses complementary technology is far more valuable than one signal alone.

4. Keep Lists Fresh

Set up recurring searches to catch new companies entering your ICP. Markets change constantly—your prospect lists should too.

5. Personalize at Scale

AI gives you the context needed for personalization. Use it. Reference their funding, congratulate them on new hires, mention their technology stack. Generic outreach wastes good leads.

Getting Started Today

The transition from manual to AI-powered prospecting doesn't require a massive overhaul. Start with one workflow:

1. Pick your highest-volume prospecting activity
2. Define clear criteria for what "good" looks like
3. Run your first AI-powered search
4. Measure results against your baseline
5. Iterate and expand

The sales teams winning today aren't working harder on prospecting. They're delegating the research to AI and focusing their human energy on conversations that close deals.

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