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.
Related jobs
Jobs that call for the skills explored in this talk.
Matching moments
03:16 MIN
Six key strategies for successful organizational AI adoption
AI beyond the code: Master your organisational AI implementation.
02:39 MIN
Overcoming enterprise AI silos with a unified strategy
Beyond GPT: Building Unified GenAI Platforms for the Enterprise of Tomorrow
03:33 MIN
Key lessons for enterprise AI tool implementation
AI Pair Programming with GitHub Copilot at SAP: Looking Back, Looking Forward!
02:46 MIN
Overcoming legal and security roadblocks for AI adoption
The AI Skills Gap: What Tech Leaders Must Get Right
04:04 MIN
Learning from common failures in AI projects
Rethinking Customer Experience in the Age of AI
07:30 MIN
Organizational strategies for successful AI adoption
Leading efficiency, empathy, and the human experience with AI
02:44 MIN
Understanding the current barriers to AI adoption
Bringing AI Everywhere
05:57 MIN
Adopting a holistic AI strategy across business functions
Fireside Chat with Werner Vogels, VP & CTO, Amazon.com & Daniel Gebler, CTO at Picnic
Panel Discussion: Responsible AI in Practice - Real-World Examples and ChallengesIntroductionIn the ever-evolving landscape of artificial intelligence, the concept of "responsible AI" has emerged as a cornerstone for ethical and practical AI implementation. During the WWC24 Panel discussion, three eminent experts—Mina, Bjorn Brin...
Chris Heilmann
Exploring AI: Opportunities and Risks for DevelopersIn today's rapidly evolving tech landscape, the integration of Artificial Intelligence (AI) in development presents both exciting opportunities and notable risks. This dynamic was the focus of a recent panel discussion featuring industry experts Kent...
Daniel Cranney
Stephan Gillich - Bringing AI EverywhereIn the ever-evolving world of technology, AI continues to be the frontier for innovation and transformation. Stephan Gillich, from the AI Center of Excellence at Intel, dove into the subject in a recent session titled "Bringing AI Everywhere," sheddi...
30:49
31:15
29:00
28:19
26:25
30:28
29:35
24:07
