Marin Niehues
AI beyond the code: Master your organisational AI implementation.
#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.
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Matching moments
04:57 MIN
Increasing the value of talk recordings post-event
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01:32 MIN
Organizing a developer conference for 15,000 attendees
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03:28 MIN
Why corporate AI adoption lags behind the hype
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04:27 MIN
Moving beyond headcount to solve business problems
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Automating formal processes risks losing informal human value
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03:15 MIN
The future of recruiting beyond talent acquisition
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05:18 MIN
Incentivizing automation with a 'keep what you kill' policy
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04:22 MIN
Why HR struggles with technology implementation and adoption
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