Every multimillion $ business I know is adapting to AI marketing. Every failing business is doing the opposite. People are falling behind without realising it. Somehow they are still ignoring AI... (In particular, AI search) And their businesses will suffer because of it. The best time to learn this new AI-powered funnel was yesterday... The second best time is today. I've already used this exact playbook to: ✅ Drive millions of impressions and thousands of leads every month. ✅ Build and scale multiple 7 and 8‑figure companies. ✅ Generate over $10M in revenue this year alone. Here's how it works: (Check out the full sheet for examples/details 👇) 📢 Awareness ↳ Make people aware you exist. Online Location: - Organic social (LinkedIn, TikTok) - AI search (AEO + GEO) - Traditional SEO (top‑of‑funnel content) KPIs: - Impressions - AI citations in answers - Organic traffic growth Strategy: - Optimise content for AI answers. - Use founder‑led content marketing. - Publish broad awareness content for search. 📚 Consideration ↳ Educate and nurture that audience. Online Location: - Organic blogs - Educational social content - Forums KPIs: - Session time, scroll depth - Returning visits - AI snippet appearances Strategy: - Build mid‑funnel SEO content - Engage in communities AI scrapes - Use gated content to capture leads 🎯 Intent ↳ Capture those evaluating your offer. Online Location: - Bottom‑funnel SEO (pricing, reviews) - AI‑powered chatbots on landing pages - Branded search + referral traffic KPIs: - Demo requests, calendar bookings - Form completions - Branded search volume Strategy: - Optimise bottom‑funnel pages - Showcase proof with real data - Use interactive tools to capture leads 💳 Conversion ↳ Turn intent into revenue. Online Location: - Landing pages and checkout flows - Email nurture sequences - Social DMs KPIs: - Conversion rate - Revenue per lead - Cart abandonment rate Strategy: - Personalise CTAs using AI insights - A/B test AI‑generated page variants - Layer urgency: time‑bound offers and bonuses 🤝 Loyalty ↳ Retain and grow customers. Online Location: - Email (loyalty programs, upsell flows) - Community platforms (Discord, LinkedIn groups) - Help articles and SEO‑driven support hubs KPIs: - Retention rate, repeat purchase rate - Referral sign‑ups - Engagement in community spaces Strategy: - Build personalised loyalty loops - Use AI for predictive churn alerts - Encourage community‑led growth This is the only funnel you need to get ahead in 2025.

Beyond Conversations: How AI Chatbots and Automation Are Redefining Human Interaction Artificial Intelligence (AI) has quickly evolved from being a futuristic concept to an essential tool powering industries across the globe. Among its most widely adopted applications are AI chatbots and automation systems, both of which are reshaping how businesses operate and how people interact with technology on a daily basis. For more details, Visit our website https://www.arsstech.com/

AI is still an untapped goldmine. 10 Ways To Make Money Using ChatGPT People are sleeping on the insane potential for their business. I'm not talking only about “prompt engineering.” It’s about using ChatGPT to unlock leverage and launch faster than ever. The opportunity window is still wide open, But it won’t be for long. Here are 10 ways to use AI to make money online: 💰 High-Ticket Sales Funnels Sales Page Copy → Write copy that turns browsers into $5K+ buyers. Email Sequences → Draft nurture flows that close leads without the calls. 💰 Content Repurposing Video → Turn a single podcast into 5 reels + 3 newsletters. Training → Turn a live session into a mini-course or evergreen funnel. 💰 Automated Client Onboarding Welcome Kits → Build AI-powered welcome docs, SOPs, and FAQ libraries. Chatbots → Set up onboarding bots inside Discord, Slack, or Circle. 💰 Market Research & Positioning Competitor Analysis → Summarize offers and find market gaps instantly. Content Angles → Generate pain points and hooks your audience actually cares about. 💰 Paid Ad Creative TikTok Ads → Script native-feeling TikToks that still convert. Facebook Ads → Spin up headline variants for split testing in seconds. 💰 Client Acquisition Lead Scraping → Use Apollo + AI to pull verified leads that match your ICP. Cold Campaigns → Personalize cold outreach sequences at scale. 💰 Community Engagement Automated Replies → Pre-write answers to FAQs inside coaching groups. Nurture Sequences → Re-engage inactive members with AI-drafted value emails. 💰 Micro-Products & Templates Mini-Products → Build pitch decks, swipe files, or sales scripts to sell. Mini-Courses → Launch a 2-3 module course in a weekend and sell on autopilot. 💰 AI Consulting Setup Services → Help others build AI-powered content or lead gen workflows. Training → Teach teams how to plug AI into their operations. 💰 Revenue Optimization LTV Boosting → Use AI to suggest upsells or bundle offers based on customer data. Pricing → Spot underpriced offers and revenue gaps in your funnel. This is what real AI leverage looks like: ➡️ Faster launches. ➡️ Better margins. ➡️ Less manual work. AI helps you build, and FanBasis helps you earn. That's why we put everything you need to monetize in one place: ✅ Smart checkout ✅ BNPL, upsells, failed payment recovery ✅ Auto-fulfillment for digital products, services, or community Are you using AI in your workflow yet? Let's chat in the comments.

Here's a list of AI and Data terms and jargons! Great Post By: Nicolas Boucher Original Post Below 👇 👇 👇 "Save this post to learn all AI & Data terms Everything you need to know in one page! 🙏 Is this post helpful and you learned something? Show your appreciation by liking, commenting or reposting! 1. GPT (Generative Pre-trained Transformer) • Definition: General-purpose language models • Finance Use: Automated Content Generation, Customer Support Chatbots 2. NLP (Natural Language Processing) • Definition: Enables computers to understand human language • Finance Use: Chatbots, Fraud Detection 3. API (Application Programming Interface) • Definition: Rules enabling software interaction • Finance Use: Data exchange, Real-time market data, Payment Processing 4. RPA (Robotic Process Automation) • Definition: AI automating rule-based tasks • Finance Use: Data Entry, Invoice Processing, Account Reconciliation 5. OCR (Optical Character Recognition) • Definition: Extracts text from images or scanned docs • Finance Use: Automated document processing, expense management 6. ASR (Automatic Speech Recognition) • Definition: Converts spoken language to text • Finance Use: Transcription, Customer Service Call Analysis 7. CL (Clustering) • Definition: Groups similar data points • Finance Use: Market Segmentation, Fraud Detection 8. TTS (Text-to-Speech) • Definition: Converts written text to spoken word • Finance Use: Audio Financial Reports, Customer Notifications 9. LLM (Large Language Model) • Definition: AI trained on vast text data • Finance Use: Sentiment Analysis, Document Summarization 10. DL (Deep Learning) • Definition: Specialized ML using deep neural networks • Finance Use: Analyze market data, predict patterns 11. ML (Machine Learning) • Definition: Enables learning from data • Finance Use: Credit Scoring, Algorithm Trading 12. RNN (Recurrent Neural Network) • Definition: Processes sequential data • Finance Use: Time-Series Analysis, Stock Price Prediction 13. SVM (Support Vector Machines) • Definition: Used for classification & regression analysis • Finance Use: Credit Risk Assessment, Portfolio Optimization 14. KNN (K-Nearest Neighbors) • Definition: Classifies data based on neighbors • Finance Use: Customer Segmentation, Anomaly Detection 15. TKN (Tokenization) • Definition: Replaces data with unique symbols • Finance Use: Information Extraction, Sentiment Analysis 16. PCA (Principal Component Analysis) • Definition: Reduces data dimensionality • Finance Use: Risk Management, Feature Extraction 18. GAN (Generative Adversarial Network) • Definition: Neural networks creating realistic data • Finance Use: Synthetic Data Generation, Anomaly Detection 19. Naive Bayes • Definition: Classification with predictor independence • Finance Use: Risk Management, Customer Segmentation 20. KM (K-Means) • Definition: Organizes data into k-clusters ______________

Machine learning is a subset of artificial intelligence (AI) focused on building systems that learn from data rather than relying on explicit programming. Instead of following static rules, ML algorithms recognize patterns in data to make predictions, perform tasks, and make decisions autonomously. This ability to “learn” makes ML systems highly adaptable, offering innovative solutions across industries. https://nav-it.com/understanding-machine-learning-types-methods-and-the-future-of-ai/ #MachineLearning #ArtificialIntelligence #AI #DataScience #DeepLearning #NeuralNetworks #FutureOfAI #AIinHealthcare #AIinFinance #AIinBusiness #chatbots