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#87 How to Successfully Integrate AI into Your Business, with Tim Leers (Global Generative & Agentic AI Lead)

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Inhalt bereitgestellt von DataTopics. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von DataTopics oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

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What happens when AI hype collides with enterprise reality? Tim Leers, Global Generative & Agentic AI Lead at Dataroots, pulls back the curtain on what's actually working—and what's not—in enterprise AI deployment today.
We begin by examining why companies like Klarna publicly announced replacing customer service teams with AI, only to quietly backtrack months later when quality suffered. This pattern of inflated expectations followed by reality checks has become common, creating what Tim calls "AI theater" – impressive demos with minimal business impact.
The conversation tackles the often misunderstood concept of "agentic AI." Rather than viewing it as a specific technology, Tim frames agency as fundamentally about delegated authority – the ability to trust AI systems with meaningful responsibilities. However, this delegation requires contextual intelligence—providing the right data at the right time—which most organizations struggle to implement effectively.
"Models are commodities, data is your moat," Tim explains, arguing that proprietary business context will remain the key differentiator even as AI models continue advancing. This perspective challenges the conventional wisdom that focuses primarily on model capabilities rather than data infrastructure.
Perhaps most valuably, Tim outlines three pillars for successful enterprise AI: contextual intelligence, continuous improvement (designing systems that evolve with changing business contexts), and human-AI collaboration. This framework shifts focus from technology deployment to sustainable business value creation.
The discussion concludes with eight practical lessons for organizations implementing generative AI, from avoiding the temptation to build proprietary models to recognizing that teaching employees to prompt effectively isn't sufficient for enterprise-wide adoption. Each lesson reinforces a central theme: successful AI implementation requires designing for change rather than building rigid systems that quickly become obsolete.
Whether you're a technical leader evaluating vendor claims or a business executive trying to separate AI reality from fantasy, this episode provides the practical guidance needed to move beyond the hype cycle toward meaningful implementation.

  continue reading

Kapitel

1. Introduction to GenAI Discussion (00:00:00)

2. State of AI Today (00:04:15)

3. Understanding Agentic AI (00:11:00)

4. Designing for Anti-Fragility (00:21:35)

5. AI and Human Collaboration (00:36:30)

6. Seven Key Lessons for Enterprise AI (00:52:50)

7. Wrap-up and Final Thoughts (01:05:40)

87 Episoden

Artwork
iconTeilen
 
Manage episode 500220208 series 3332503
Inhalt bereitgestellt von DataTopics. Alle Podcast-Inhalte, einschließlich Episoden, Grafiken und Podcast-Beschreibungen, werden direkt von DataTopics oder seinem Podcast-Plattformpartner hochgeladen und bereitgestellt. Wenn Sie glauben, dass jemand Ihr urheberrechtlich geschütztes Werk ohne Ihre Erlaubnis nutzt, können Sie dem hier beschriebenen Verfahren folgen https://de.player.fm/legal.

Send us a text

What happens when AI hype collides with enterprise reality? Tim Leers, Global Generative & Agentic AI Lead at Dataroots, pulls back the curtain on what's actually working—and what's not—in enterprise AI deployment today.
We begin by examining why companies like Klarna publicly announced replacing customer service teams with AI, only to quietly backtrack months later when quality suffered. This pattern of inflated expectations followed by reality checks has become common, creating what Tim calls "AI theater" – impressive demos with minimal business impact.
The conversation tackles the often misunderstood concept of "agentic AI." Rather than viewing it as a specific technology, Tim frames agency as fundamentally about delegated authority – the ability to trust AI systems with meaningful responsibilities. However, this delegation requires contextual intelligence—providing the right data at the right time—which most organizations struggle to implement effectively.
"Models are commodities, data is your moat," Tim explains, arguing that proprietary business context will remain the key differentiator even as AI models continue advancing. This perspective challenges the conventional wisdom that focuses primarily on model capabilities rather than data infrastructure.
Perhaps most valuably, Tim outlines three pillars for successful enterprise AI: contextual intelligence, continuous improvement (designing systems that evolve with changing business contexts), and human-AI collaboration. This framework shifts focus from technology deployment to sustainable business value creation.
The discussion concludes with eight practical lessons for organizations implementing generative AI, from avoiding the temptation to build proprietary models to recognizing that teaching employees to prompt effectively isn't sufficient for enterprise-wide adoption. Each lesson reinforces a central theme: successful AI implementation requires designing for change rather than building rigid systems that quickly become obsolete.
Whether you're a technical leader evaluating vendor claims or a business executive trying to separate AI reality from fantasy, this episode provides the practical guidance needed to move beyond the hype cycle toward meaningful implementation.

  continue reading

Kapitel

1. Introduction to GenAI Discussion (00:00:00)

2. State of AI Today (00:04:15)

3. Understanding Agentic AI (00:11:00)

4. Designing for Anti-Fragility (00:21:35)

5. AI and Human Collaboration (00:36:30)

6. Seven Key Lessons for Enterprise AI (00:52:50)

7. Wrap-up and Final Thoughts (01:05:40)

87 Episoden

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