Building an AI-Powered Team in 90 Days

How to Rebuild Your Business with AI: Tools and Workflow

Introduction

Imagine rebuilding your business from scratch in just 90 days using AI. Inspired by a viral tweet, this guide shows you how to create a 5-member AI team that works 24/7 for a fraction of the cost of a single employee. We'll cover the tools, workflow, and strategies to make it happen, tailored for entrepreneurs with basic coding skills.

What is an AI Team?

An AI team uses tools like language models, data analysis platforms, and automation to handle tasks typically done by human departments. Think of AI as your Researcher, Strategist, Copywriter, Ops Manager, and Content Marketer, guided by precise instructions to deliver results fast and cheap.

The AI Team Roles

1. The Researcher

Finds market gaps, competitors, and customer pain points.

2. The Strategist

Defines KPIs, evaluates advantages, and mitigates risks.

3. The Copywriter

Crafts compelling landing pages and lead magnets.

4. The Ops Manager

Plans resources, SOPs, and regulatory compliance.

5. The Content Marketer

Creates engaging social posts and analyzes performance.

Workflow to Build Your AI Team

  1. Create a Vision Document: Use Grok 3 to draft a 50-75 page "lore document" outlining your business goals.
  2. Research: Analyze competitors and market gaps with Grok 3 and Perplexity AI. Store data in Google Sheets.
  3. Strategize: Define KPIs and risks with Grok 3, visualized in Tableau.
  4. Write Copy: Build landing pages and lead magnets with Grok 3 and Jasper AI.
  5. Manage Operations: Create SOPs and automate tasks with Zapier and Python.
  6. Market Content: Generate posts with Grok 3, design visuals in Canva, and schedule with Buffer.

Pro Tip: Use precise prompts like "List 10 competitors with pricing" to get actionable AI output.

Sample Automation Script

Here's a Python script to scrape competitor data and save it to a CSV:

import requests from bs4 import BeautifulSoup import pandas as pd # Scrape competitor pricing url = "https://example-competitor.com/pricing" response = requests.get(url) soup = BeautifulSoup(response.text, 'html.parser') prices = [p.text for p in soup.find_all('div', class_='price')] competitors = [{'name': 'Example', 'price': prices}] df = pd.DataFrame(competitors) df.to_csv('competitors.csv', index=False) print("Data saved to competitors.csv")

Next Steps