Author

Alex Dunlop

Introduction

When I moved from New Zealand to London in 2023, I faced the challenge of rebuilding my professional network from scratch. As a developer, I turned to what I knew best: technology. Here's how I used AI to revolutionise my networking approach.

The AI-Powered Process:

LinkedIn Scraper

I developed a custom command-line tool using Node to automate the process of gathering information from LinkedIn profiles. This tool not only extracted basic profile data but also used natural language processing to analyse job descriptions and company information. It could process up to 50 profiles an hour, extracting key details like work history, skills, and recent activity.

AI-Crafted Questions

Using the summaries as input, I used a Ollama machine learning model to generate tailored questions. This model was trained on a dataset of effective networking questions and industry-specific topics. It could produce a set of 5-10 unique questions for each potential connection, focusing on their specific experiences, industry challenges, and areas of mutual interest.

AI-Assisted Follow-ups

For post-meeting follow-ups, I created a system that combined meeting notes with pre-existing profile information. This hybrid approach used a combination of rule-based templates and a language model to craft personalised follow-up messages. The system could reference specific conversation points, suggest potential collaboration opportunities, and even recommend relevant articles or resources based on the discussion.

Results and Insights:

After implementing this AI-powered networking system over a period of three months, here are the results:

• 50+ people called: The AI-prepared questions led to an average call duration of 45 minutes, significantly longer than my previous average of 20 minutes.
• 100+ LinkedIn connection requests sent: With personalised messages based on AI insights, the acceptance rate was 68%, compared to my previous rate of 30% with generic messages.
• 30+ people agreed to catch up: The relevance of the AI-suggested topics resulted in a 60% meeting agreement rate, up from my previous 25%.
• 9 face-to-face meetings: These led to 3 potential job opportunities and 5 introductions to other valuable contacts.

Key learnings:

AI significantly reduced preparation time, allowing me to focus on relationship-building. The quality of conversations improved dramatically with AI-generated insights and questions. Follow-up consistency increased by 90% with the AI-assisted system. The personalised approach led to a 75% increase in reciprocal offers to help or connect.

Applications:

This AI-powered networking system has potential applications beyond individual use. It could be adapted for HR departments to improve candidate engagement, for sales teams to enhance client relationships, or for conference organisers to facilitate more meaningful attendee interactions. The key is to use AI as a tool to augment human interaction, not replace it, fostering deeper, more genuine professional connections in our increasingly digital world.