Growth Ritual #60
📋 IN THIS ISSUE: 20 AI Ideas You’ll Actually Use (Because You’re Tired of the Hype Too)
🎙️ AUDIO DEEP DIVE OF THIS ISSUE:
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20 AI Ideas You’ll Actually Use (Because You’re Tired of the Hype Too)
Tired of AI hype but no real way to use it in your business?
You’re not alone.
Every day it feels like there’s a new AI tool promising to “revolutionize” your business, automate your life, and pick up your dry cleaning. But when you actually check them out, half are academic prototypes, and the other half are glorified to-do list apps wearing an AI sticker.
It’s exhausting.
What you need — what we all need — is a list of AI-powered workflows you can actually plug into your day-to-day business without a computer science degree or a $50k/month budget.
Good news: I’ve put that list together for you.
And these aren’t fluffy “ask ChatGPT to write a poem about your business” ideas either. These are multi-agent AI workflows.
Before we dive into the good stuff, let’s clear the air. Multi-agent AI is just a fancy term for tools that work together to tackle specific tasks—like drafting emails, analyzing data, or even coding for you. Think of it as a team of digital helpers who don’t need lunch breaks.
These 20 ideas are split into three buckets: marketing and sales, customer support, and development and operations.
I’ll throw in some real-world examples so you can see how this stuff actually works.
Marketing and Sales: Win More Customers with Less Effort
1. Prep for Meetings Like a Pro
Description: AI scans LinkedIn, company websites, and recent news to create a cheat sheet on a lead’s goals and pain points for meeting preparation.
Multi-Agents Used:
Data Aggregation Agent: Collects data from LinkedIn, company websites, and news sources to build a profile of the lead or company.
Insight Generation Agent: Analyzes collected data to identify goals, pain points, and relevant talking points for the meeting.
Content Drafting Agent: Formats the insights into a concise, readable cheat sheet or briefing document.
Appropriate Tools:
Cognism: Uses AI to pull contact details and company data, including recent activities, to enrich lead profiles. Ideal for B2B sales prep.
LinkedIn Sales Navigator with AI integrations: Extracts professional background and recent posts from LinkedIn profiles.
Google Alerts or Brand24: Monitors news and online mentions for real-time updates about the lead’s company.
Jasper AI: Drafts polished meeting briefs based on aggregated data, ensuring a professional tone.
Clay: Combines data enrichment with automated research to pull insights from multiple sources like LinkedIn and news sites.
2. Create One-Page PDFs That Wow
Description: AI generates professional one-page PDFs with branding, key points, and visuals for customer proposals or pitches.
Multi-Agents Used:
Content Drafting Agent: Writes compelling copy for the PDF, tailored to the lead’s needs or pitch context.
Image Generation Agent: Creates visuals like charts, icons, or branded graphics to enhance the PDF’s appeal.
Design and Formatting Agent: Combines text and visuals into a polished, branded PDF layout.
Appropriate Tools:
Canva AI: Uses Magic Studio to design visually stunning PDFs with branded templates and AI-generated graphics.
Jasper AI: Crafts persuasive, on-brand text for the PDF content, such as value propositions or key benefits.
DALL-E or Midjourney: Generates custom visuals or infographics to make the PDF pop.
Crello (VistaCreate): Offers AI-assisted design for quick, professional PDF creation with branding consistency.
PandaDoc: Automates proposal creation with AI-driven templates and e-signatures for a seamless workflow.
3. Write Personalized Marketing Emails
Description: AI crafts emails that sound human, tailored to each lead’s interests, with optimal send-time suggestions.
Multi-Agents Used:
Customer Segmentation Agent: Analyzes lead data to group recipients by interests or behaviors for targeted messaging.
Content Drafting Agent: Writes personalized email copy based on segment data, maintaining brand voice.
Scheduling Agent: Determines the best send time based on historical engagement data.
Appropriate Tools:
Reply.io: Builds AI-powered email drip campaigns with personalized outreach and response scoring.
Lavender: Enhances email writing with AI to make messages compelling and personalized, with coaching for better open rates.
HubSpot AI: Crafts emails using CRM data for personalization and suggests optimal send times.
Chatsonic (Writesonic): Generates on-brand email copy with AI-driven personalization for targeted messaging.
Mailchimp AI: Automates email creation and schedules sends based on open-rate analytics.
4. Track Job Changes
Description: AI monitors when leads or customers switch jobs, alerting you to new outreach opportunities.
Multi-Agents Used:
Data Aggregation Agent: Tracks job change signals from LinkedIn, company websites, or news.
Insight Generation Agent: Identifies which job changes are relevant (e.g., a lead moving to a decision-maker role).
Notification Agent: Alerts the sales team with actionable outreach suggestions.
Appropriate Tools:
UserGems: Detects job transitions in your network and highlights leads likely to need your product in new roles.
Cognism: Enriches contact data and flags job changes using AI-driven prospecting.
LinkedIn Sales Navigator: Monitors profile updates for job changes with manual or AI-assisted alerts.
ZoomInfo: Tracks career moves and updates contact records with real-time data enrichment.
Gem-E: Scans for job transitions and prioritizes outreach based on relevance to your product.
5. Get Content Marketing Ideas
Description: AI analyzes trends and suggests audience-relevant content ideas for blogs or campaigns.
Multi-Agents Used:
Trend Analysis Agent: Identifies trending topics or keywords in your niche using social media and search data.
Content Planning Agent: Suggests content ideas, formats, and strategies based on trend analysis.
Insight Generation Agent: Refines ideas to align with audience interests and business goals.
Appropriate Tools:
BuzzSumo: Analyzes trending topics and content performance to inspire campaign ideas.
Chatsonic (Writesonic): Generates content ideas based on niche trends and audience data.
Sprout Social: Uses AI to surface trending topics and consumer insights from social media.
Ahrefs: Identifies high-traffic keywords and content gaps for blog or campaign ideas.
ContentShake (Semrush): Suggests SEO-driven content ideas with detailed outlines and brand voice customization.
6. Create SEO-Optimized Content
Description: AI writes blog posts or product descriptions with keywords to rank higher on Google.
Multi-Agents Used:
SEO Analysis Agent: Researches keywords and analyzes competitor content for ranking opportunities.
Content Drafting Agent: Writes SEO-optimized blogs, descriptions, or pages with proper keyword placement.
Performance Monitoring Agent: Tracks content performance and suggests optimizations.
Appropriate Tools:
Surfer SEO: Optimizes content with keyword suggestions and competitor analysis for higher rankings.
Jasper AI: Generates SEO-friendly blogs or product descriptions with brand voice consistency.
ContentShake (Semrush): Creates SEO-optimized content with real-time keyword data and readability tweaks.
ZBrain: Automates SEO-optimized content creation, including blogs and product copy, with multi-agent coordination.
Ahrefs: Provides keyword research and content gap analysis to guide SEO strategy.
7. Run Competitor Analysis
Description: AI scrapes competitors’ websites and socials to create a report on their strategies.
Multi-Agents Used:
Data Aggregation Agent: Collects data from competitors’ websites, social media, and online mentions.
Competitor Strategy Monitoring Agent: Analyzes data to identify strengths, weaknesses, and strategies.
Insight Generation Agent: Compiles findings into a comprehensive report with actionable recommendations.
Appropriate Tools:
Chatsonic (Writesonic): Generates competitor analysis reports with a single prompt, covering strengths and weaknesses.
Brand24: Tracks competitors’ online mentions and sentiment for strategic insights.
SimilarWeb: Analyzes competitors’ website traffic, keywords, and marketing channels.
ZBrain: Includes a Competitor News Aggregation Agent to summarize competitor activities.
SEMRush: Provides detailed competitor analysis, including SEO, PPC, and content strategies.
8. Extract Social Media Feedback
Description: AI sifts through social media comments to find customer feedback or ideas.
Multi-Agents Used:
Social Media Monitoring Agent: Collects comments, mentions, and posts from platforms like Twitter or Instagram.
Customer Feedback Sentiment Analysis Agent: Analyzes feedback for sentiment (positive, negative, neutral) and key themes.
Insight Generation Agent: Summarizes feedback into actionable ideas or product improvements.
Appropriate Tools:
Sprout Social: Uses AI to analyze social media conversations and surface feedback with sentiment analysis.
Brand24: Monitors social media for brand mentions and applies sentiment analysis to identify customer opinions.
ZBrain: Includes a Customer Feedback Sentiment Analysis Agent for cross-channel feedback analysis.
Hootsuite: Tracks social engagement and uses AI to highlight feedback trends.
Mention: Aggregates social media feedback and provides sentiment insights in real time.
9. Build Product Comparison Reports
Description: AI compares your product to competitors’ based on features, price, and reviews to sway leads.
Multi-Agents Used:
Data Aggregation Agent: Collects product data, reviews, and pricing from websites and review platforms.
Insight Generation Agent: Analyzes data to highlight your product’s advantages and competitors’ weaknesses.
Content Drafting Agent: Creates a persuasive comparison report or table for sales use.
Appropriate Tools:
Chatsonic (Writesonic): Generates detailed comparison reports with a single prompt, ideal for quick turnarounds.
ZBrain: Aggregates competitor data and drafts comparison reports with AI coordination.
G2 or Capterra: Pulls user reviews and feature data for side-by-side product comparisons.
Jasper AI: Writes compelling report copy to emphasize your product’s strengths.
Tableau (with AI integrations): Visualizes comparison data in charts or tables for professional reports.
10. Craft Brand Positioning Reports
Description: AI analyzes your market to suggest how to position your brand for maximum impact.
Multi-Agents Used:
Market Analysis Agent: Gathers data on industry trends, customer preferences, and competitor positioning.
Insight Generation Agent: Identifies unique positioning opportunities based on market gaps or strengths.
Content Drafting Agent: Writes a positioning report with messaging strategies and taglines.
Appropriate Tools:
ZBrain: Uses multi-agents to analyze markets and draft positioning reports with strategic insights.
Chatsonic (Writesonic): Generates positioning reports with market analysis and messaging suggestions.
SEMRush: Analyzes competitor branding and market trends to inform positioning strategies.
Brand24: Tracks brand sentiment and competitor messaging for positioning insights.
Clarity (Microsoft): Provides market research data to refine brand positioning for B2B or B2C.
11. Find Key Buyer Contacts
Description: AI identifies decision-makers in target organizations via LinkedIn or company sites.
Multi-Agents Used:
Data Aggregation Agent: Scrapes LinkedIn, company websites, and databases for contact details.
Prospecting Agent: Filters contacts to identify decision-makers based on role, seniority, or influence.
Insight Generation Agent: Suggests outreach strategies for each contact based on their profile.
Appropriate Tools:
Cognism: Finds verified contact details and decision-maker profiles for B2B outreach.
ZoomInfo: Enriches contact data with AI to pinpoint key buyers in organizations.
LinkedIn Sales Navigator: Identifies decision-makers with advanced search and AI-driven recommendations.
Hunter.io: Locates email addresses for outreach to key contacts.
Breeze Prospecting Agent (HubSpot): Uses AI to prioritize CRM contacts matching your ideal customer profile.
Customer Support: Keep Your Customers Happy
12. Analyze Support Tickets
Description: AI identifies patterns in support tickets to uncover common issues, helping prioritize fixes and improve customer experience.
Multi-Agents Used:
Data Aggregation Agent: Collects and organizes support ticket data from customer support platforms (e.g., ticket descriptions, categories, timestamps).
Pattern Recognition Agent: Analyzes tickets to identify recurring issues, trends, or bottlenecks (e.g., frequent complaints about a specific feature).
Insight Generation Agent: Summarizes findings into actionable reports or recommendations, such as which issues to prioritize for product updates.
Appropriate Tools:
Zendesk AI: Analyzes ticket data to spot trends, predict escalations, and suggest resolutions based on historical patterns.
Freshdesk (Freddy AI): Uses AI to categorize tickets and identify common issues, with dashboards for trend visualization.
ZBrain: Includes a Customer Feedback Analysis Agent to process ticket data and generate insights on recurring problems.
Gorgias: Provides AI-driven ticket analysis for e-commerce, focusing on customer pain points and operational inefficiencies.
Tableau (with AI integrations): Visualizes ticket trends and patterns for deeper insights, especially for large datasets.
13. Answer Questions Using a Knowledge Base
Description: AI pulls answers from help documentation to respond to customer queries instantly, reducing response times and agent workload.
Multi-Agents Used:
Knowledge Base Retrieval Agent: Searches and retrieves relevant articles or answers from a knowledge base based on query keywords.
Natural Language Processing (NLP) Agent: Understands customer questions, even if phrased differently, to match them with the right response.
Response Drafting Agent: Formats answers in a conversational, brand-aligned tone for chatbots or email replies.
Appropriate Tools:
Intercom AI (Fin): Uses AI to pull answers from your knowledge base and deliver them via chatbot or email, with human-like responses.
Zendesk Answer Bot: Retrieves knowledge base content to resolve queries instantly, integrating with tickets and chat.
Freshdesk (Freddy AI): Powers chatbots to answer questions using help articles, reducing agent involvement.
Grok (xAI): Can be fine-tuned to query internal knowledge bases and provide conversational answers, ideal for custom setups.
Document360: Combines an AI-driven knowledge base with a chatbot that answers queries based on stored documentation.
14. Categorize Issues and Run Sentiment Analysis
Description: AI tags support tickets (e.g., “billing” or “tech issue”) and gauges customer sentiment to prioritize urgent or negative cases.
Multi-Agents Used:
Issue Categorization Agent: Assigns tickets to categories (e.g., “bug,” “refund”) based on keywords and context.
Sentiment Analysis Agent: Evaluates ticket language to determine customer mood (positive, neutral, negative) and urgency.
Prioritization Agent: Flags high-priority tickets (e.g., angry customers) for faster resolution and summarizes insights.
Appropriate Tools:
Zendesk AI: Automatically tags tickets and runs sentiment analysis to prioritize urgent cases, with AI-driven escalation rules.
Freshdesk (Freddy AI): Categorizes tickets and assesses sentiment to route critical issues to senior agents.
ZBrain: Includes a Customer Feedback Sentiment Analysis Agent to tag and analyze tickets across channels (email, chat, social).
MonkeyLearn: Offers AI-powered text analysis for ticket categorization and sentiment scoring, integrable with support platforms.
Sprout Social: While social-focused, its AI can analyze support-related messages for sentiment and tag issues, useful for omnichannel support.
Development and Operations: Streamline the Tech Stuff
15. Write Coding Documentation
Description: AI generates clear, comprehensive documentation for code, saving developers time and aiding onboarding.
Multi-Agents Used:
Code Analysis Agent: Scans source code to understand its structure, functions, and dependencies.
Content Drafting Agent: Writes clear, structured documentation (e.g., function descriptions, usage examples) in a developer-friendly tone.
Formatting Agent: Organizes the documentation into a consistent format, such as Markdown or HTML, compatible with tools like GitHub or ReadTheDocs.
Appropriate Tools:
GitBook AI: Generates documentation from codebases with AI-driven explanations and Markdown support for easy integration.
CodiumAI: Analyzes code and produces detailed documentation, including function-level comments and API guides.
ZBrain: Includes a Content Drafting Agent to create technical documentation from code analysis, customizable for team needs.
Swimm: Uses AI to auto-generate and update code documentation, syncing with repositories like GitHub.
Mintlify: Creates developer-friendly documentation with AI, offering sleek formatting and integration with IDEs like VS Code.
16. Review Pull Requests
Description: AI checks code in pull requests for errors, style issues, or bugs, ensuring quality before merging.
Multi-Agents Used:
Code Analysis Agent: Reviews code for syntax errors, logic flaws, or security vulnerabilities.
Style Enforcement Agent: Ensures adherence to coding standards (e.g., PEP 8 for Python) and team style guides.
Feedback Generation Agent: Provides actionable comments on issues, suggesting fixes or improvements.
Appropriate Tools:
GitHub Copilot: Offers AI-driven code reviews in pull requests, flagging issues and suggesting optimizations within GitHub.
DeepCode (by Snyk): Uses AI to analyze pull requests for bugs, vulnerabilities, and style violations, integrating with Git platforms.
CodeRabbit: Provides real-time AI code reviews with detailed feedback, focusing on readability and best practices.
SonarQube: Combines static code analysis with AI to detect issues in pull requests, ideal for large teams.
CrewAI: Can be configured with role-based agents (e.g., “Code Reviewer”) to review pull requests collaboratively, using LLMs for feedback.
17. Write Development Tests
Description: AI creates test cases for code to ensure functionality and catch bugs, streamlining testing.
Multi-Agents Used:
Code Analysis Agent: Understands code structure and logic to identify testable components (e.g., functions, endpoints).
Test Case Generation Agent: Writes unit, integration, or end-to-end tests based on code behavior and requirements.
Validation Agent: Checks generated tests for coverage and relevance, ensuring they align with project goals.
Appropriate Tools:
CodiumAI: Generates unit tests for multiple languages (e.g., Python, JavaScript) by analyzing codebases, with high coverage.
Testim: Uses AI to create and maintain automated tests, including UI and API tests, integrating with CI/CD pipelines.
Diffblue Cover: Automatically writes Java unit tests with AI, focusing on edge cases and code coverage.
Mabl: Provides AI-driven test automation for web apps, generating tests from user interactions and code analysis.
ZBrain: Can orchestrate test generation with multi-agents, customizing tests for specific frameworks like Jest or PyTest.
18. Build React Components
Description: AI generates React code snippets (e.g., buttons, forms) to speed up front-end development.
Multi-Agents Used:
Requirement Analysis Agent: Interprets user inputs or design specs to define component requirements (e.g., props, styling).
Code Generation Agent: Writes React component code, including JSX, CSS, and hooks, adhering to best practices.
Validation Agent: Checks generated code for errors, accessibility, and compatibility with React ecosystems.
Appropriate Tools:
ReactAgent (via Cognosys): AI-driven tool for generating React components, offering code suggestions and error detection.
GitHub Copilot: Generates React components in real-time within IDEs, with context-aware suggestions for hooks and styling.
V0 by Vercel: Creates React components from natural language prompts, with Tailwind CSS and Shadcn/UI integration.
ZBrain: Uses multi-agents to generate and validate React code, customizable for specific UI libraries.
Codeium: Produces React snippets with AI, focusing on performance and modern practices like TypeScript.
19. Design Architectural Diagrams
Description: AI creates visual diagrams of app architecture for planning or stakeholder presentations.
Multi-Agents Used:
System Analysis Agent: Parses codebases, APIs, or descriptions to map system components (e.g., databases, services).
Diagram Generation Agent: Produces visual diagrams (e.g., UML, flowcharts) using standard notations like C4 or AWS icons.
Formatting Agent: Ensures diagrams are clear, branded, and exportable in formats like PNG or PDF.
Appropriate Tools:
Diagrams.net (Draw.io) with AI plugins: Generates architecture diagrams from text or code, with cloud provider templates.
Lucidchart AI: Creates diagrams from natural language or data inputs, ideal for AWS or microservice architectures.
Mermaid Live Editor: Uses AI to convert text (e.g., Markdown) into diagrams, integrable with GitHub for dev workflows.
ZBrain: Orchestrates diagram creation with agents for system analysis and visualization, supporting custom formats.
Structurizr: Generates C4 model diagrams from code or DSL, with AI-assisted layout optimization.
20. Put Together Operational Reports
Description: AI compiles data into reports on performance, costs, or efficiency, optimizing operations.
Multi-Agents Used:
Data Aggregation Agent: Collects metrics from sources like logs, monitoring tools, or databases (e.g., Prometheus, AWS CloudWatch).
Insight Generation Agent: Analyzes data to identify trends, anomalies, or optimization opportunities (e.g., high server costs).
Content Drafting Agent: Creates polished reports with visuals, summaries, and recommendations in formats like PDF or dashboards.
Appropriate Tools:
Tableau (with AI integrations): Visualizes operational data into interactive reports, with AI-driven insights for trends.
Power BI AI: Generates reports from diverse data sources, using AI to highlight key metrics and anomalies.
ZBrain: Coordinates multi-agents to aggregate and analyze data, producing custom operational reports.
Sisense: Uses AI to create data-driven reports with natural language summaries, ideal for non-technical stakeholders.
CrewAI: Orchestrates role-based agents (e.g., “Data Analyst,” “Report Writer”) to compile reports collaboratively.