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World Congress 2024 • Aug 20, 2024 • Session details

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

Marin Niehues

A failed manufacturing AI project reveals a harsh truth: data silos and executive micromanagement kill innovation. Discover how to abandon steering committees, empower engineers, and successfully operationalize embedded AI.

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#1 about 1 min

Organizing data teams to deliver artificial intelligence initiatives

Structuring data teams effectively ensures technical experts can successfully deploy functional models.

#2 about 2 min

Defining generative versus embedded artificial intelligence systems

Distinguishing between conversational tools and autonomous systems integrated directly into physical operations enables accurate scoping.

#3 about 3 min

Visualizing production planning complexity in physical manufacturing operations

Visualizing massive scale and timing variations reveals why spreadsheet planning fails for complex material supply chains.

#4 about 3 min

Structuring a cross-functional initial project team for delivery

Assembling data scientists and business interfaces provides theoretical capability without necessarily guaranteeing actual production execution.

#5 about 5 min

Confronting legacy software and defining true machine learning

Differentiating static procedural rules from self-adjusting neural networks resolves foundational misconceptions originating from legacy system owners.

#6 about 2 min

Overcoming internal data hoarding with literacy and governance

Establishing an organizational data strategy prevents internal hoarding boundaries from starving a machine learning model.

#7 about 2 min

Bridging the operational gap between leadership expectations and reality

Communicating the hidden foundation of security and engineering prevents structural collapse when leaders treat machine learning like a simple product order.

#8 about 3 min

Breaking down internal data and isolated organizational silos

Connecting isolated business departments is crucial because restricted information boundaries directly mirror detrimental corporate communication failures.

#9 about 5 min

Surviving executive workshops and misaligned corporate leadership constraints

Mandating task forces without dedicated operational staff or direct data access inevitably paralyzes potential technological innovation.

#10 about 3 min

Eliminating micromanagement overhead to enable expert technical delivery

Removing systemic administrative interruptions heavily empowers operational engineering teams to actually generate tangible end-user value.

#11 about 2 min

Implementing best practices for successful corporate intelligence initiatives

Fostering technological comprehension and macromanaging autonomous teams ensures shared structural commitment and prevents repeating costly foundational conceptual errors.

Matching moments

2:21 min

Overcoming operational challenges in AI adoption

Björn Bringmann Björn Bringmann +3 · World Congress 2024

3:33 min

Crucial lessons for deploying generative AI in enterprises

Alexander Trusheim Alexander Trusheim +1 · World Congress 2025

1:35 min

Root causes of underlying AI initiative failures

Deivids Vilkinsons Deivids Vilkinsons +3 · World Congress 2026 Europe

1:41 min

Overcoming artificial intelligence silos in the enterprise

Kapil Gupta Kapil Gupta · World Congress 2025

3:48 min

Common reasons why enterprise artificial intelligence initiatives fail

Priyanka Vergadia Priyanka Vergadia · World Congress 2026 North America

4:35 min

Overcoming common barriers to implementing collaborative AI

Rudi Bauer Rudi Bauer +1 · Cappuccino with HR

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