AI beyond the code: Master your organisational AI implementation.

What's the biggest threat to your AI project? It's not the code, but a manager who says, "This data is mine."

#1about 6 minutes

The challenge of optimizing tire production planning

Manual production planning for expensive machinery leads to inefficiencies and lost revenue, creating a clear business need for an AI-driven solution.

#2about 3 minutes

Assembling the team and building the initial concept

A cross-functional team of data engineers, scientists, and AI engineers is formed and successfully develops the core AI model concept in the initial sprints.

#3about 7 minutes

Distinguishing true AI from legacy rule-based algorithms

The project is challenged by a legacy system mistaken for AI, highlighting the organizational need to understand the difference between basic algorithms and self-learning systems.

#4about 7 minutes

Data silos are the enemy of machine learning

Being denied access to real production data reveals that organizational data silos and a lack of data governance will prevent any machine learning model from succeeding.

#5about 5 minutes

How C-level micromanagement creates organizational overhead

Escalating issues to the C-level results in a series of unproductive workshops and a bloated, unfunded task force, demonstrating how micromanagement stifles progress.

#6about 3 minutes

Six key strategies for successful organizational AI adoption

A summary of crucial lessons learned includes fostering AI understanding, building shared commitment, implementing a data strategy, removing silos, and empowering expert teams.