Have you ever had a task that seemed so repetitive, you wished you could hand it over to a machine? Well, that’s precisely how I felt about creating client proposals. As a Business Analyst, I saw patterns emerging every time I began the process. Despite each client’s unique challenges, I realized that there were constants and variables in each proposal I made.
The variables that changed with every new proposal included comprehensive elements such as market research, user personas, competitor analysis, SWOT analysis, potential product enhancements, and many more.
These variables are based on constants, which are the key data points we collect from each client, such as the type of product, application stakeholders, key features, etc. I began to see this whole process as an equation, where specific values were fixed while others shifted depending on the project’s unique requirements.
The most crucial step was defining the product creation process and identifying the chains of thoughts I would have while crafting product proposals. This involved understanding how different aspects, constants, and variables formed a coherent and persuasive proposal.
I started by noting my thought process and how I approached each proposal section. I discovered that regardless of the uniqueness of each project, my approach would typically follow the same logic and thought progression. This became the blueprint for my AI agent.
With the process nailed down, the next step was to create different ‘prompt injections’, effectively programming the AI to think like me. Each prompt injection was a task based on the constants and variables representing a puzzle piece. For example, ‘Draft an Executive Summary based on the following key features…’
This allowed the AI to mimic the steps I took while creating proposals, making it capable of handling any new proposal with the same care and attention to detail I would give.
Now, this might sound straight out of a sci-fi movie, but I integrated my professional journey and experiences into the AI’s learning algorithm. It wasn’t just about imparting the ‘how’; I wanted to infuse ‘why’ into the system. Every decision I made as a Business Analyst, every lesson learned, every success and failure, now became a data point for the AI, enhancing its decision-making prowess.
Personalizing Shan involved a hefty load of my personality data, including my decision-making processes, problem-solving approach, communication style, and even my quirks. The goal was to create an AI agent that didn’t just replicate my knowledge but also my unique professional personality.
After countless late nights and countless cups of coffee, voila! Shan 1.0 was ready. A Business Analyst AI agent, trained to generate client proposals with the same diligence, detail, and dedication that I would.
I’ve managed to share my skills on a scale I couldn’t have achieved alone.
Turning myself into an AI agent might sound like a daunting task, and it was, but the results are genuinely astounding. If you’re passionate about what you do and find value in sharing your knowledge, consider creating your own AI agent. After all, isn’t it wonderful to think that we can all contribute to the future in our unique ways?