Big Business, Big Barriers? Stress-Testing AI Initiatives.

What's the biggest barrier to AI success? It might not be the technology.

#1about 4 minutes

Introducing a case study of a failed AI project

A real-world example from a tire manufacturer is used to illustrate the organizational challenges of implementing an embedded AI for production planning.

#2about 2 minutes

Defining the AI project goals and initial team

The project aimed to use an embedded AI to optimize machine utilization and supply chains, starting with a motivated team of data engineers and scientists.

#3about 4 minutes

Distinguishing true AI from legacy rule-based systems

The project faced its first obstacle when a legacy software owner claimed their if-else system was already an AI, revealing a fundamental misunderstanding of the technology.

#4about 6 minutes

Why data silos and lack of governance kill AI projects

The project stalled when a plant manager refused to share essential data, highlighting that a strong foundation of data literacy and governance must precede AI development.

#5about 4 minutes

How executive micromanagement derails AI initiatives

An escalation to C-level resulted in an unproductive workshop with only managers, which failed to resolve data ownership and instead led to micromanagement.

#6about 5 minutes

The pitfalls of creating a top-heavy AI task force

The project was replaced by a dysfunctional AI task force with a large steering committee but no dedicated operational staff, creating massive overhead and no impact.

#7about 2 minutes

Seven best practices for successful AI implementation

Key takeaways from the failed project include ensuring stakeholder understanding, securing commitment, building a data strategy, removing silos, and focusing on delivery over management.