How to Qualify Leads with Automation (Without Guessing)
People keep asking the wrong question about AI.
They say, “Will AI replace my job?”
But the better question is: “How creatively can I use AI to multiply my own thinking?”
Because here’s the truth — AI only works when you bring your own insight to the table.
Let me show you what I mean.
Meet Alec: A Sales Strategy Consultant
One of my clients, Alex, specialises in helping SaaS companies hire their first salesperson — and do it right.
It’s a niche problem that many founders underestimate. Here’s how it usually goes:
- The founder makes the early sales.
- They decide to “scale” and hire a salesperson.
- The salesperson doesn’t perform.
- Six months later, the founder’s burned out, out of pocket, and convinced salespeople “don’t work.”
Alex exists to prevent that failure.
He helps founders:
- Build a clear sales process
- Define messaging and ICPs
- Document repeatable steps
- Hire their first sales rep with clarity and structure
But he had one big challenge:
How do I find the companies that need me right now?
How to Qualify Leads with Automation
Alex’s ICP was very specific:
- SaaS company
- Based in the UK
- £100K–£1M turnover
- Less than 20 employees
- Hiring their first salesperson
No CRM or ad platform could slice that cleanly — so we built our own workflow using creativity + automation.
Here’s how the system worked:
1. Scrape Live Sales Job Posts
Using Apify, we scraped LinkedIn and other sources for new job posts with keywords like:
- “Sales Executive”
- “Business Development Manager”
- “First Sales Hire”
We filtered out duplicates daily and focused only on UK-based roles.
2. Exclude Recruiters
Only direct employer listings were allowed — no agencies or recruiters.
3. Confirm SaaS Business
We checked their website and LinkedIn to confirm they were SaaS-based.
✅ First qualification pass.
4. Check Existing Sales Team
We scanned their LinkedIn employee list.
If no one else had a sales title, this was likely their first hire.
✅ Second qualification pass.
5. Check Size + Revenue
Using Apollo and public LinkedIn data, we confirmed:
- Less than 20 employees
- Revenue estimate between £100K–£1M
✅ Third qualification pass.
Why This Was So Effective
This strategy worked because Alex understood his niche.
Automation did the heavy lifting — but the logic came from Alex’s deep understanding of:
- Who his clients were
- What signals they gave off
- When they’d need him most
The result? A list of highly qualified, highly relevant leads — at scale.
Better Automation = Warmer Outreach
Because we knew they were actively hiring, Alex could send:
“Hey John, I noticed you’re hiring for your first sales role. That’s exactly where I help SaaS founders avoid mis-hires and set up their team for success.”
That’s not a cold message.
It’s personal. It’s timely.
And it’s based on real behaviour — not assumptions.
Plus:
- They clearly have budget (they’re hiring)
- They have intent (the job post is live)
- And they’re in the right range (company size + turnover)
It’s miles better than “spray and pray” outreach.
And it’s completely scalable.
Tools We Used
- Apify – For scraping job data from LinkedIn
- Apollo – For enrichment and company revenue estimates
- LinkedIn – For company data and job visibility
- n8n – For automation, logic flows, and orchestration
Could we do this for other industries? Absolutely.
This same structure works for:
- HR consultants looking for companies hiring HR roles
- Ops specialists monitoring first-time ops hires
- Legal/compliance teams watching for specific hiring trends
Final Thought: Automation Doesn’t Replace Insight — It Scales It
You can’t outsource your thinking to AI.
But once you’ve got the thinking, the logic, and the signals — automation makes it repeatable, scalable, and accurate.
This isn’t “AI doing the work for you.”
It’s you doing your best thinking — and AI multiplying it.
If you’ve got a clear ICP but no clear way to consistently reach them — let’s build a system that thinks the way you do.
Happy to show you what’s possible.
