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domain	gen-ai.bot
summary	Okay, here's a summary of the website content and a workflow design: According to a McKinsey analysis, generative AI’s greatest value creation (approximately 75%) is primarily focused on four key areas: 1) enhancing customer engagement, 2) utilizing virtual experts synthesized from content, 3) generating content, and 4) assisting with coding and software development. Workflow for Generative AI in Customer Experience (CX): Goal: Enhance customer interactions, personalize engagement, and optimize overall customer satisfaction using generative AI. Phase 1: Data Foundation & Model Training (Weeks 1-4) 1. Data Audit & Preparation: Identify and cleanse relevant customer data – CRM, support tickets, chat logs, surveys, website behavior, social media interactions. 2. Model Selection: Choose appropriate generative AI models (e.g., large language models) – Consider pre-trained models and fine-tuning options. 3. Initial Training: Train the models on the prepared customer data, focusing on understanding customer language, intent, and common queries. Establish key performance indicators (KPIs) for the model’s accuracy and effectiveness. Phase 2: CX Application Development (Weeks 5-8) 1. Customer Engagement Bots: Deploy AI-powered chatbots for initial interactions, handling FAQs, basic troubleshooting, and routing complex issues. 2. Personalized Content Generation: Utilize AI to generate tailored email marketing content, product recommendations, and website copy based on customer profiles and behavior. 3. Virtual Expert Integration: Build virtual experts accessible through chat or voice, synthesizing content from knowledge bases and FAQs to answer complex questions in real-time. 4. Coding & Software Support (Pilot): Explore use cases for AI-assisted coding for support ticket resolution or documentation generation (initially for a smaller, well-defined project). Phase 3: Continuous Optimization & Expansion (Ongoing) 1. Performance Monitoring: Track KPIs like customer satisfaction scores (CSAT), Net Promoter Score (NPS), resolution rates, and agent efficiency. 2. Human-in-the-Loop: Implement a system where human agents can seamlessly intervene in AI-driven interactions, particularly when complex or sensitive issues arise. 3. Model Retraining: Regularly retrain the AI models with new data and feedback to improve accuracy, adapt to evolving customer needs, and incorporate new product/service information. 4. Expansion of Use Cases: Explore new applications of generative AI within CX (e.g., proactive support, sentiment analysis, predictive issue resolution). Would you like me to elaborate on a specific aspect of this workflow, such as model selection or KPIs?
title	GEN-AI – AI with Heart, With Human Oversight
description	GEN-AI – AI with Heart, With Human Oversight
keywords	services, cases, contact, customer, more, content, human, build, read, design, updates, skip, home, value, engagement, singapore, step
upstreams
downstreams
nslookup	A 35.213.139.207
created	2026-02-14
updated	2026-02-14
summarized	2026-02-15

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