When should one think of AI?

Strategia per l'Intelligenza Artificiale (AI)

Vidhi Chugh

AI strategist and ethicist

Separate hype from reality

AI hype can lead to overly high expectations, which could be be misleading and can cause a high failure rate for ML business deployments

¹ Harvard Business Review

Separate hype from reality

AI hype can lead to overly high expectations, which could be be misleading and can cause a high failure rate for ML business deployments

Traditional vs AI software development

                  Traditional software

• Predefined rules
• Logic in code
• Code-driven
• Deterministic
• Tax Calculator

                  AI software

• No explicit instructions
• Data-driven
• Shifting data patterns
• Probabilistic
• ChatGPT

Traditional vs AI software maintenance

Traditional vs AI software implementation

• Beyond traditional software development lifecycle

Traditional vs AI software implementation

• Beyond traditional software development lifecycle

Traditional vs AI software implementation

• Beyond traditional software development lifecycle

Traditional vs AI software implementation

• Beyond traditional software development lifecycle

Traditional vs AI software implementation

• Beyond traditional software development lifecycle

Additional strategic considerations for AI

Additional strategic considerations for AI

Additional strategic considerations for AI

Additional strategic considerations for AI

Additional strategic considerations for AI

Additional strategic considerations for AI

Key business drivers

Stakeholders

• Priority

 

 

Key business drivers

Stakeholders

• Priority but at the expense of what?

 

 

Key business drivers

• Teams, business units or processes

Key business drivers

• Benefit vs. magnitude of impact

Key business drivers - alternatives

 

• Time since existence
• Recently identified?

 

• Alternative non-AI solution
• Business rules
• Confidence in the solution

Key business drivers - dimension

• Addresses all business units or a specific vertical?

Key business drivers - problem complexity

 

• Challenging problem?
• Additional steps or processes?

 

• Unforeseen challenges
• Impede progress

Fear of automation

 

• Covered so far - hype and design thinking

 

• Next - fear of losing jobs to automation
• Lesser demand for cognitive and manual skills
• Example: data collection and processing

¹ McKinsey report on "Generative AI and the future of work in America"

Change management

Change management

Change management

Change management

Change management

Let's practice!

Strategia per l'Intelligenza Artificiale (AI)