3227: Dataiku on Managing LLMs Without the Chaos

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about a year ago 30:38

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What does it really take to implement generative AI in a way that balances innovation, governance, and long-term value? In today’s episode, , I’m joined by Emma Irwin, Director of Sales Engineering at Dataiku. With a deep background in enterprise AI and experience supporting major organizations like AVIVA and the NHS, Emma brings a grounded, real-world perspective to one of the most hyped areas in tech today.

While most businesses are ramping up GenAI investments, few have the processes, controls, or workforce skill sets needed to scale safely and effectively. Emma and I dive straight into the challenges that IT leaders are facing right now—from managing LLM usage and controlling cost, to building secure frameworks that actually reduce risk rather than amplify it.

Emma unpacks the three key pillars every organization needs for sustainable GenAI success: access controls that keep your stack flexible, robust discovery mechanisms to document LLM usage across the enterprise, and value quantification to show the real return on AI initiatives. But what really stood out is the need for diverse teams and strong governance models to address bias in AI development. From sentiment analysis to internal chatbots and large-scale summarization use cases, Emma brings a mix of strategy and execution to the conversation.

We also explore the importance of secure sandbox environments, the value of audit-readiness through documentation automation, and why it’s time for every business to move beyond experimentation into a more structured, responsible phase of AI maturity. Emma is also a vocal advocate for women in tech and AI, and shares how mentorship, representation, and inclusive leadership can help shape a more equitable future for the industry.

So what guardrails do you have in place for GenAI? Are you really ready to move from pilot projects to enterprise-scale implementations? This episode is packed with insights for anyone building, managing, or scaling AI across the enterprise.

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