AI as a Competitive Lever in Revenue Leadership
I don’t use AI to play around. I use it to win.
Most people talk about AI like it’s a toy, a chatbot, or a shortcut for busywork. That misses the point.
The real advantage of AI is not simple automation. It is better judgment at scale.
I’ve built and used AI tools to do three things that matter in revenue organizations:
These are not science projects.
They are practical systems built to help a sales organization see clearer, act faster, and make better decisions.
Most people talk about AI like it’s a toy, a chatbot, or a shortcut for busywork. That misses the point.
The real advantage of AI is not simple automation. It is better judgment at scale.
I’ve built and used AI tools to do three things that matter in revenue organizations:
- improve pre-call intelligence and deal strategy
- strengthen forecast quality and pipeline inspection
- surface high-fit opportunities faster than manual methods ever could
These are not science projects.
They are practical systems built to help a sales organization see clearer, act faster, and make better decisions.
1. AI-Powered Prospect Dossier Generator
Turning a LinkedIn profile into a one-page selling brief
One of the biggest problems in sales is that reps walk into calls underprepared. They know the title, maybe the company, maybe a vague pain point, but not enough to shape a smart conversation.
So I built a Chrome Extension that works directly on the LinkedIn profile I’m viewing.
What it does
The extension scrapes and structures key information from the profile page, including:
It then uses AI to enrich that information by:
The final output is a clean, one-page dossier in Word format that a rep can annotate, bring to a meeting, or hand to leadership as evidence for why an opportunity is real and how it should be approached.
Why it matters
This is not about scraping for the sake of scraping. It is about reducing the distance between raw data and smart action.
Instead of a rep spending 30 to 45 minutes bouncing between tabs, guessing at outreach angles, and piecing together weak prep notes, this tool creates a structured point of view in minutes.
Competitive advantage created
The bigger idea
Most sales teams drown in information but starve for insight. This tool helps bridge that gap.
It gives salespeople a sharper blade before they walk into battle.
Turning a LinkedIn profile into a one-page selling brief
One of the biggest problems in sales is that reps walk into calls underprepared. They know the title, maybe the company, maybe a vague pain point, but not enough to shape a smart conversation.
So I built a Chrome Extension that works directly on the LinkedIn profile I’m viewing.
What it does
The extension scrapes and structures key information from the profile page, including:
- prospect name
- company name
- mutual contacts
- recent posts and visible activity
It then uses AI to enrich that information by:
- identifying likely email addresses and phone numbers
- scoring the prospect against a predefined sales scorecard
- generating tailored selling guidance based on role, company context, and profile signals
The final output is a clean, one-page dossier in Word format that a rep can annotate, bring to a meeting, or hand to leadership as evidence for why an opportunity is real and how it should be approached.
Why it matters
This is not about scraping for the sake of scraping. It is about reducing the distance between raw data and smart action.
Instead of a rep spending 30 to 45 minutes bouncing between tabs, guessing at outreach angles, and piecing together weak prep notes, this tool creates a structured point of view in minutes.
Competitive advantage created
- better first-call preparation
- more personalized outreach
- stronger deal strategy before the meeting even starts
- clearer internal communication on why an opportunity should close
The bigger idea
Most sales teams drown in information but starve for insight. This tool helps bridge that gap.
It gives salespeople a sharper blade before they walk into battle.
2. AI-Assisted Pipeline Inspection Tool
Helping managers separate real deals from fairy tales
Every sales leader knows the problem: a pipeline review can turn into a storytelling contest.
Deals sound good. Reps sound confident. Forecasts look polished. Then the quarter ends and the truth shows up with brass knuckles.
So I built an AI-assisted pipeline inspection tool for managers.
What it does
The tool helps a leader pressure-test an opportunity during a 1:1 by guiding the conversation through key diagnostic questions.
It is designed to help answer the question behind every forecast call:
Is this deal real, or are we just hoping?
Using structured prompts, the tool helps managers evaluate things like:
- strength of the business problem
- access to power and decision-makers
- competitive position
- timeline credibility
- next-step quality
- rep command of the account
- likelihood that the prospect will actually buy
Why it matters
Forecast accuracy is not built in the CRM. It is built in the conversation between a leader and a rep.
This tool improves that conversation.
It helps managers coach better, inspect smarter, and apply the same standard across every rep and every deal. Instead of relying only on gut instinct, it gives leaders a consistent framework for judging pipeline legitimacy.
Competitive advantage created
- stronger forecast discipline
- earlier identification of weak deals
- better coaching in 1:1s
- more consistent pipeline inspection across teams
- less time wasted on fantasy opportunities
AI should not replace leadership judgment. It should sharpen it.
A good manager still has to think. This tool simply helps them ask better questions and get to the truth faster.
3. AI-Powered Opportunity Discovery Engine
Using Python and AI to scan 2,100+ companies overnight for high-fit roles
During my job search, I decided I was not going to rely on job boards, luck, or whatever happened to show up in my inbox.
So I built my own system.
I created a Python-based job discovery engine that scans job postings across roughly 2,100 companies overnight and delivers a daily email of newly surfaced opportunities.
But the real value is not in finding jobs. The real value is in ranking them.
What it does
The system automatically:
Those criteria include:
It then emails me a prioritized summary so I can focus attention where the odds are best.
Why it matters
This is territory optimization applied to a job search.
The same principle works in sales: do not just build a bigger list. Build a smarter one.
By using AI and scoring logic to rank opportunities, I can spend time on the highest-value targets first instead of manually sorting through hundreds of weak-fit postings.
Competitive advantage created
The bigger idea
AI is most valuable when it helps you aim better.
Anyone can work harder. The win comes from working on the right things first.
Using Python and AI to scan 2,100+ companies overnight for high-fit roles
During my job search, I decided I was not going to rely on job boards, luck, or whatever happened to show up in my inbox.
So I built my own system.
I created a Python-based job discovery engine that scans job postings across roughly 2,100 companies overnight and delivers a daily email of newly surfaced opportunities.
But the real value is not in finding jobs. The real value is in ranking them.
What it does
The system automatically:
- searches company career pages and job postings overnight
- identifies newly posted roles
- filters for relevant leadership opportunities
- grades each opportunity against a set of weighted criteria
Those criteria include:
- location
- remote or hybrid fit
- compensation signals
- company quality
- role seniority
- relevance to my background
- other proprietary signals found in the job description
It then emails me a prioritized summary so I can focus attention where the odds are best.
Why it matters
This is territory optimization applied to a job search.
The same principle works in sales: do not just build a bigger list. Build a smarter one.
By using AI and scoring logic to rank opportunities, I can spend time on the highest-value targets first instead of manually sorting through hundreds of weak-fit postings.
Competitive advantage created
- faster identification of quality opportunities
- better prioritization of time and effort
- more signal, less noise
- more consistent targeting than manual review
- a repeatable framework for ranking fit at scale
The bigger idea
AI is most valuable when it helps you aim better.
Anyone can work harder. The win comes from working on the right things first.
My approach to AI
I’m most interested in applying AI where it creates durable commercial advantage.
That includes:
I do not view AI as a replacement for human selling. I view it as a force multiplier for strong operators.
The best sales organizations will not win because they used AI the most. They will win because they used it where it mattered most.
I’m most interested in applying AI where it creates durable commercial advantage.
That includes:
- predictive insight instead of hindsight reporting
- territory and target optimization instead of brute-force activity
- personalized outreach that is actually personal
- workflow support that improves judgment, not just speed
I do not view AI as a replacement for human selling. I view it as a force multiplier for strong operators.
The best sales organizations will not win because they used AI the most. They will win because they used it where it mattered most.