Marin Niehues
Big Business, Big Barriers? Stress-Testing AI Initiatives.
#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.
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