Session 3

AI Technologies & Applications

Mastering the AI Technology Landscape

CEO AI Mentor Program - Torsten's Technology Journey

Duration: 55-70 minutes

AI Technology Stack

Application Layer

Chatbots & Virtual Assistants

Recommendation Systems

Predictive Analytics

Computer Vision Apps

AI/ML Models

Machine Learning Algorithms

Deep Learning Networks

Natural Language Processing

Computer Vision Models

AI Platforms

MLOps Platforms

Model Training Infrastructure

AutoML Tools

Model Serving Systems

Data Infrastructure

Data Lakes & Warehouses

ETL/ELT Pipelines

Real-time Streaming

Data Governance

Cloud Infrastructure

Compute Resources

Storage Systems

Networking

Security & Compliance

Industry-Specific AI Applications

Retail & E-commerce

Personalized Recommendations +35% conversion

Dynamic Pricing +20% margins

Inventory Optimization -25% waste

Manufacturing

Predictive Maintenance -40% downtime

Quality Control +95% accuracy

Supply Chain AI -30% costs

Financial Services

Risk Assessment +90% accuracy

Fraud Detection +99% detection

Algorithmic Trading +25% returns

Healthcare

Diagnostic Imaging +95% accuracy

Drug Discovery -50% time

Patient Monitoring +60% efficiency

AI Vendor Selection Criteria

Technical Capabilities

Model Performance 25%

Scalability 20%

Integration Ease 15%

API Quality 10%

Security & Compliance

Data Security 20%

Compliance Standards 15%

Audit Capabilities 10%

Data Privacy 15%

Business Factors

Cost & ROI 25%

Support Quality 20%

Vendor Stability 15%

Innovation Roadmap 10%

Vendor Selection Process

1 Define Requirements

2 Research Vendors

3 Evaluate Solutions

4 Pilot Testing

5 Final Decision

AI Integration Strategies

API Integration

Advantages:

Quick implementation
No infrastructure needed
Regular updates included
Pay-per-use model

Considerations:

Data privacy concerns
Internet dependency
Limited customization
Ongoing costs

On-Premise Deployment

Advantages:

Full data control
Customization flexibility
No internet dependency
Regulatory compliance

Considerations:

Higher upfront costs
Infrastructure requirements
Maintenance responsibility
Scalability challenges

Hybrid Cloud

Advantages:

Best of both worlds
Scalability benefits
Data security options
Cost optimization

Considerations:

Complex architecture
Integration challenges
Management overhead
Security complexity

Integration Best Practices

Start with pilot projects

Ensure data quality

Plan for scalability

Monitor performance

Train users effectively

Maintain security standards

Emerging AI Technology Trends

Generative AI

AI that creates new content, from text to images to code

Business Impact:

Content creation automation
Design and prototyping
Code generation
Creative problem solving

Edge AI

AI processing at the edge, closer to data sources

Business Impact:

Real-time decision making
Reduced latency
Lower bandwidth usage
Enhanced privacy

AutoML

Automated machine learning for non-experts

Business Impact:

Democratized AI development
Faster model deployment
Reduced technical barriers
Cost-effective solutions

Responsible AI

Ethical, fair, and transparent AI development

Business Impact:

Regulatory compliance
Trust and transparency
Bias mitigation
Risk management

Session 3 Summary

Technology Mastery

Technology Stack: Understanding the complete AI technology ecosystem

Industry Applications: Real-world AI use cases across sectors

Vendor Selection: Criteria and process for choosing AI solutions

Integration Strategies: Best practices for AI implementation

Your Action Items

1 Map your technology stack requirements

2 Research relevant industry applications

3 Evaluate vendor options

4 Prepare for Session 4: Data Strategy

Session 3 Complete!

You've mastered the AI technology landscape

Coming Up: Session 4

Data Strategy & Analytics

Data Strategy Analytics Capabilities Data Governance Data-Driven Culture

Start Session 4 View All Sessions