Introduction to AI technologies relevant for support operations (Claude, ChatGPT, Zoho AI, etc.)

Understanding the evolution of customer support and the role of automation

Key terminology and concepts in AI-powered support systems

Ethical considerations and human-AI collaboration principles

Overview of automation opportunities across the customer journey

Industry Focus: Introduction to the top 5 industries embracing AI in operations (Retail/E-commerce, Financial Services, Healthcare, Manufacturing, and Travel/Hospitality)

How to audit customer support tickets using AI tools

How to audit inbound and outbound support calls with AI

How to export support ticket data through tools, APIs, or Python script extraction without knowing any coding

Creating sample datasets and defining ticket categorization systems

Drafting Standard Operating Procedures (SOPs) and audit parameters

Performing AI-based quality audits to evaluate consistency and identify improvement areas

Generating comprehensive audit reports and visualization dashboards

Identifying high-opportunity automation targets based on audit findings

Industry Case Study: Comparing audit practices in financial services vs. e-commerce support operations

Building and maintaining AI-powered knowledge bases

Using best call transcripts to identify top customer queries

Creating AI-generated response templates based on human interactions

Integration with self-service portals (Zoho, Freshdesk, Zendesk)

Designing knowledge architecture for both agent training and chatbot knowledge

Implementing feedback loops to continuously improve knowledge systems

Industry Application: Building healthcare-specific knowledge bases with compliance and privacy considerations

Implementing AI-driven ticket categorization, prioritization, and routing

Using sentiment and complexity analysis for ticket assignment

Bulk processing techniques using large language models

Designing automated response suggestions and escalation pathways

Integration with CRM tools for seamless customer data flow

Creating performance dashboards to monitor automation effectiveness

Setting up exception handling for complex cases requiring human intervention

Industry Workshop: Designing automated workflows for manufacturing and supply chain support issues

Designing effective chatbot conversation flows for B2B and B2C contexts

Using tools like Landbot or Botsonic

Creating decision trees and logical pathways for common customer scenarios

Implementing sentiment-triggered escalation protocols Developing personalized chatbot experiences based on customer data

Building referral engine campaigns triggered by positive sentiment

Testing and optimizing chatbot performance metrics

How to implement rollouts in a phased manner

Human-in-the-loop: Facilitating smooth handoffs between AI and human agents

Industry Project: Creating retail and e-commerce chatbots with product recommendation capabilities

Designing voice-based customer service flows using platforms like Google Dialogflow and Amazon Lex

Implementing Natural Language Processing for conversational voice support

Using Twilio and similar tools to deploy voice bots for inbound/outbound interactions

Creating voice recognition systems for routing, query resolution, or escalation

Analyzing voice support performance with AI-powered tools

Designing multilingual voice support capabilities for global operations

Industry Deep Dive: Voice support systems for travel and hospitality booking and customer care

Using AI tools to forecast customer needs, ticket volume, and operational demands

Building predictive models for anticipating customer behavior and support trends

Forecasting staffing requirements and identifying potential bottlenecks

Implementing proactive support strategies based on predictive insights

Creating actionable forecasts to optimize workflows and team allocation

Developing early warning systems for potential service disruptions

Using historical data analysis to identify seasonal patterns and prepare accordingly

Industry Application: Predictive analytics for financial services customer behavior and transaction support

Implementing sentiment analysis across multiple communication channels

Using tools like MonkeyLearn, Thematic, and Chattermill for detecting customer emotions

Creating automated triggers based on sentiment scores (escalation, follow-up, etc.)

Building real-time sentiment dashboards for team performance monitoring

Using sentiment trends to improve customer experience strategies

Designing intervention protocols for negative sentiment detection

Leveraging positive sentiment for testimonial and referral opportunities

Industry Focus: Sentiment analysis for healthcare patient interactions and experience improvement

Implementing AI-driven personalization throughout the customer journey

Using tools like Salesforce Einstein and Zendesk AI for dynamic response generation

Building predictive customer profiles to anticipate needs and customize service

Creating customer success playbooks and automation triggers for renewal, upsell and cross-sell

Designing automated but personalized check-in systems for customer retention

Developing success metrics and monitoring systems for personalization effectiveness

Building referral workflows triggered by customer satisfaction signals

Industry Use Case: Personalization strategies for retail and e-commerce customer interactions

Converting technical SOPs into engaging microlearning modules using AI

Creating multimedia training materials with tools like Loom and Descript

Using visual SOP mapping tools like Tango, Scribe, and Trainual

Designing automated assessment systems to evaluate agent understanding

Implementing continuous learning systems that adapt to product changes

Creating AI-driven simulation environments for agent skill practice

Developing frameworks to automatically update training when products change

Industry Tutorial: Building process documentation for complex manufacturing support scenarios

Designing consistent customer experiences across multiple support channels

Building integrated support ecosystems with seamless transitions between channels

Implementing unified customer data views across touchpoints

Creating channel-specific automation strategies while maintaining brand consistency

Developing automated handoff protocols between channels

Building measurement frameworks to evaluate omnichannel effectiveness

Using AI to identify optimal channel routing based on query type and customer preference

Industry Case Study: Omnichannel support implementation in travel and hospitality

Designing effective collaboration models between AI systems and human agents

Creating clear escalation pathways and handoff protocols

Developing supervision frameworks for AI-generated responses

Building performance metrics that evaluate both AI and human contributions

Training human agents to effectively supervise and complement AI systems

Implementing continuous improvement cycles based on collaboration data

Designing agent interfaces that maximize productivity with AI assistance

Industry Workshop: Human-AI collaboration in financial services compliance-sensitive environments

Implementing robust testing frameworks for AI response accuracy

Designing monitoring systems for chatbot and automated response performance

Creating feedback loops to continuously improve AI response quality

Developing exception handling protocols for edge cases

Building comprehensive dashboards for AI system performance

Setting up automated alerts for quality issues or anomalies

Creating audit trails for compliance and performance improvement

Industry Deep Dive: Quality assurance for healthcare AI support with privacy and accuracy requirements

Using AI to extract actionable insights from support interactions

Building comprehensive analytics dashboards using tools like Google Data Studio or Tableau

Identifying improvement opportunities through pattern recognition

Creating automated reporting systems for stakeholders

Developing product feedback loops based on support data

Implementing A/B testing frameworks for automation improvements

Designing ROI measurement systems for automation initiatives

Industry Application: Analytics use cases for manufacturing support optimization and supply chain issue prevention

Retail and E-Commerce:

Financial Services:

Healthcare:

Manufacturing and Supply Chain:

Travel and Hospitality:

Comprehensive audit of a sample support operation in one of the five key industries

Identification of high-value automation opportunities specific to the chosen industry

Design of integrated automation solution (knowledge base, chatbot, sentiment analysis)

Implementation planning with clear metrics and success criteria

Creation of monitoring and optimization frameworks

Presentation of business case and implementation roadmap

Peer review and expert feedback on implementation strategy