AI Implementation Resources

Curated tools, frameworks, and resources to help you navigate AI implementation successfully.

Implementation Frameworks

AI Readiness Assessment

What it is: A comprehensive checklist to evaluate your organization’s readiness for AI implementation.
Why it matters: Helps you identify gaps before investing in AI solutions.
Key components: Data infrastructure, talent assessment, process maturity, change management readiness.

ROI Calculation Templates

What it is: Spreadsheet templates for calculating potential AI project returns.
Why it matters: Helps you make data-driven decisions about AI investments.
Includes: Cost modeling, benefit quantification, risk assessment, timeline planning.

Vendor Evaluation Criteria

What it is: Framework for evaluating AI solution providers.
Why it matters: Helps you cut through vendor marketing to assess real capabilities.
Covers: Technical capabilities, implementation track record, support quality, total cost of ownership.

Essential Reading

Books

– “Prediction Machines” by Ajay Agrawal – Economic framework for understanding AI value
– “The AI Advantage” by Thomas Davenport – Practical guide to AI implementation
– “Human + Machine” by Paul Daugherty – How humans and AI work together effectively

Research Reports

– McKinsey Global Institute AI Reports – Comprehensive industry analysis and trends
– MIT Sloan Management Review AI Studies – Academic research with practical applications
– Deloitte AI Institute Publications – Enterprise-focused AI implementation insights

Case Study Library

By Industry

– Financial Services: Fraud detection, risk assessment, customer service
– Healthcare: Diagnostic assistance, operational efficiency, patient outcomes
– Manufacturing: Predictive maintenance, quality control, supply chain optimization
– Retail: Demand forecasting, personalization, inventory management

By Use Case

– Process Automation: RPA, document processing, workflow optimization
– Predictive Analytics: Forecasting, risk modeling, maintenance scheduling
– Customer Experience: Chatbots, recommendation engines, personalization

Getting Started Guides

For Executives

– AI Strategy Development Checklist
– Building Your AI Team: Roles and Responsibilities
– Common AI Implementation Pitfalls and How to Avoid Them

For Technical Teams

– Data Infrastructure Requirements for AI
– Model Development Best Practices
– Production Deployment Considerations

All resources are regularly updated based on listener feedback and emerging best practices.