The AI Landscape: Key Technologies for Business

Not all AI is the same. Understanding the different types helps you match the right technology to the right problem.

The Three Categories That Matter for Leaders

1. Traditional Machine Learning (ML)

What it does: Learns patterns from historical data to make predictions or classifications.

Business applications:

• Credit scoring and risk assessment
• Customer churn prediction
• Inventory demand forecasting
• Quality defect detection
• Recommendation engines

When to use it:

• You have substantial historical data
• The problem involves prediction or classification
• Patterns in the past are likely to continue

Leader considerations:

• Requires clean, labeled data
• Models need retraining as conditions change
• Accuracy depends heavily on data quality

2. Generative AI (GenAI)

What it does: Creates new content—text, images, code, audio—based on patterns learned from training data.

Business applications:

• Content creation and editing
• Customer service automation
• Code generation and documentation
• Meeting summarization
• Translation and localization

When to use it:

• Tasks involve creating or transforming content
• Output can be reviewed and edited by humans
• Speed and scale matter more than perfection

Leader considerations:

• Outputs require human review for accuracy
• Can produce plausible-sounding misinformation
• Costs scale with usage

3. Robotic Process Automation (RPA) + AI

What it does: Automates repetitive tasks across applications, enhanced with AI for handling variations.

Business applications:

• Invoice processing
• Data entry and migration
• Report generation
• Employee onboarding tasks
• Compliance document processing

When to use it:

• Tasks are rule-based and repetitive
• Multiple systems need to work together
• Human workers spend time on low-value activities

Leader considerations:

• Best for structured, predictable processes
• Requires process standardization first
• Maintenance needed when underlying systems change

Matching Technology to Problems

Business Need	Best Fit Technology	Example
Predict future outcomes	Traditional ML	Sales forecasting
Create content at scale	Generative AI	Marketing copy
Automate repetitive tasks	RPA + AI	Invoice processing
Analyze customer feedback	GenAI + ML	Sentiment analysis
Personalize experiences	ML + GenAI	Product recommendations

Technology Selection Framework

When evaluating which AI technology to apply, ask:

1. Is the problem structured or unstructured?

   • Structured data → Traditional ML
   • Unstructured content → Generative AI

2. Do we need predictions or creations?

   • Predictions from patterns → ML
   • New content creation → GenAI

3. How critical is accuracy?

   • High-stakes decisions → ML with human review
   • Draft content → GenAI with editing

4. What data do we have?

   • Historical labeled data → ML
   • Examples and context → GenAI

Key Takeaway

Different AI technologies solve different problems. Traditional ML excels at prediction from data, Generative AI creates and transforms content, and RPA automates repetitive processes. Successful leaders match the right technology to the right challenge rather than applying one approach everywhere.

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Next: Assess your organization's readiness to adopt and benefit from AI. :::