Prospecting and Research with AI

The Researcher Who Never Sleeps

A sales rep at a SaaS company told me she used to spend Sunday evenings researching her Monday prospects. Two hours minimum—reading annual reports, scanning LinkedIn, checking news feeds—just to feel prepared.

Now she spends 15 minutes with AI on Monday morning and walks into every conversation better prepared than she ever was after two hours of manual research.

That’s not magic. It’s a better process. And that’s what we’re building today.

Before You Research: Define Your ICP

Before you ask AI to research anything, you need to tell it who you’re looking for. This is your Ideal Customer Profile—and it’s the foundation of everything else.

Your ICP should include:

Company size (employees, revenue range)
Industry (specific verticals, not just “tech”)
Geography (regions, markets)
Business stage (startup, growth, enterprise)
Common pain points your product solves
Decision-maker roles (titles you typically sell to)
Trigger events that signal buying readiness

Here’s an example prompt to build your ICP with AI:

I sell [your product/service] to [type of company].

My best customers tend to be:
- [Industry] companies with [size range] employees
- They typically struggle with [pain points]
- The decision maker is usually the [title]
- They buy after experiencing [trigger events]

Based on this, help me build a detailed Ideal Customer Profile.
Include: firmographic criteria, behavioral signals,
disqualification criteria, and a scoring framework
I can use to prioritize prospects.

Quick check: Can you describe your ICP in 2-3 sentences right now? If not, that’s your first exercise.

Building Prospect Profiles with AI

Once you know who you’re looking for, AI becomes an incredibly fast research machine. Here’s the framework for building a prospect profile:

The Five-Layer Research Stack

Layer 1: Company Overview Basic firmographics—what the company does, size, stage, market position.

Research [Company Name] and provide:
1. What they do (2-3 sentences, plain language)
2. Company size (employees, revenue if public)
3. Key products/services
4. Target market and customers
5. Main competitors
6. Recent news (last 6 months)

Layer 2: Pain Point Analysis Where they’re likely feeling pressure—and where your solution fits.

Based on what you know about [Company Name],
a [industry] company of [size]:

What operational challenges are they likely facing right now?
Consider: their growth stage, competitive pressures,
industry trends, and recent news.

I sell [your product]. Which of these challenges
does my solution address?

Layer 3: Stakeholder Mapping Who’s involved in the buying decision and what each cares about.

For a [size] [industry] company buying [your product category],
map the typical buying committee:

- Economic buyer (who controls budget)
- Technical evaluator (who assesses fit)
- End user (who uses it daily)
- Champion (internal advocate)
- Blocker (common objection raiser)

For each: typical title, key concerns, what they need to hear.

Layer 4: Trigger Events Recent changes that create urgency or a reason to reach out now.

I'm prospecting [Company Name]. Identify potential
trigger events from the last 6 months:

- Leadership changes
- Funding rounds or financial events
- Product launches or pivots
- Expansion (new markets, offices, hiring sprees)
- Regulatory changes affecting their industry
- Competitor moves they need to respond to

For each trigger, explain why it creates an opportunity
for [your product/service].

Layer 5: Personalization Intel Specific details about your contact that enable personalized outreach.

I'm reaching out to [Name], [Title] at [Company].
Help me find personalization angles:

- Recent LinkedIn posts or articles they've shared
- Conference talks or podcast appearances
- Professional interests or causes they support
- Mutual connections or shared experiences
- Their likely priorities based on their role

Give me 3 specific conversation openers based on this.

Prioritizing Your Pipeline

You’ve got research. Now you need to focus. AI can help you score and prioritize prospects so you spend time on the deals most likely to close.

The BANT+ Scoring Framework

Ask AI to evaluate prospects against expanded BANT criteria:

Score this prospect on a 1-10 scale for each criterion:

Prospect: [Name, Title, Company]
What I know: [paste your research]

Criteria:
1. Budget: Can they afford our solution? (signals: company size, funding, existing spend)
2. Authority: Is this person a decision-maker or influencer?
3. Need: How strong is the pain point our solution addresses?
4. Timeline: Are there trigger events creating urgency?
5. Fit: How well do they match our ICP?

Provide: Total score, tier (A/B/C), and recommended approach for each tier.

Tier A (35-50 points): High-priority. Personalized outreach, multi-channel approach, immediate follow-up.

Tier B (20-34 points): Medium-priority. Personalized email sequence, monitor for trigger events.

Tier C (below 20): Low-priority. Nurture sequence, revisit quarterly.

Batch Research: Working at Scale

The real power shows up when you need to research 20, 50, or 100 prospects. Here’s how to do it efficiently:

Step 1: Prepare Your Prospect List

Create a simple list with what you already know:

Research these prospects for my outreach campaign.
I sell [product] to [ICP summary].

For each, provide: company overview (2 sentences),
likely pain points, recent trigger events,
and a personalized outreach angle.

1. Sarah Chen, VP Operations, Meridian Logistics
2. Marcus Johnson, CTO, DataStream Analytics
3. Priya Patel, Head of Engineering, CloudFirst Solutions
[continue...]

Step 2: Create Research Templates

Build reusable research templates you can fill in quickly:

PROSPECT BRIEF: [Company Name]
================================
Contact: [Name, Title]
Company: [Size, Industry, Stage]
ICP Match: [Score/10]

PAIN POINTS:
- [Primary pain point related to your product]
- [Secondary pain point]

TRIGGER EVENTS:
- [Most recent relevant event]

OUTREACH ANGLE:
- [Personalized hook based on research]

TALKING POINTS:
- [If they respond, lead with this]
- [Backup topic]

COMPETITORS THEY MIGHT USE:
- [Current solution, if known]

Step 3: Validate and Add Your Expertise

AI research is a starting point. Before reaching out:

Verify key facts. Double-check company size, recent news, and contact details.
Add your knowledge. You know things AI doesn’t—previous interactions, industry gossip, mutual connections.
Trust your instincts. If something feels off about a prospect’s fit, investigate further.

Real Example: From Zero to Outreach-Ready

Let’s walk through a complete example. Say you sell HR software and you’ve identified a prospect: Jamie Rodriguez, VP of People at a 400-person fintech company called PayFlow.

Your prompt:

I sell HR management software to mid-market companies (200-1000 employees).
My prospect is Jamie Rodriguez, VP of People at PayFlow,
a 400-person fintech company based in Austin, TX.

Build me a complete prospect brief:
1. Company overview and current stage
2. HR-specific challenges for a fintech at this size
3. Trigger events (growth, hiring, compliance)
4. Personalization angles for Jamie
5. Recommended outreach approach
6. Potential objections to prepare for

AI produces a detailed brief. You review it, add what you know, and you’re ready to write outreach—which is exactly what we’ll cover in the next lesson.

Exercise: Research Three Prospects

Pick three real prospects from your current pipeline (or identify three new ones). For each:

Use the Five-Layer Research Stack to build a complete profile
Score them using the BANT+ framework
Identify the single best outreach angle for each
Note one thing AI found that you wouldn’t have discovered manually

Time yourself. Compare it to how long manual research would take.

Key Takeaways

Always start with a clear ICP—it makes AI research dramatically more focused
Use the Five-Layer Research Stack: company overview, pain points, stakeholders, trigger events, personalization intel
Score prospects with BANT+ to prioritize where you spend your time
AI research is a starting point—always verify facts and add your own expertise
Batch research at scale is where AI saves the most time: 20 prospects in the time it used to take for 3

Next: turning that research into cold outreach that actually gets replies.