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80: "Helpful, not creepy: personalization that earns trust," says Kevin McCurdy, Global CPG Partner Lead, AWS

Data: Heaven or Hell? (Adastra Podcast)

Listen to Adastra’s podcast dedicated to the latest trends in data management, AI, and analytics.

This engaging podcast series showcases the visionaries reshaping the tech landscape. Featuring strategic leaders and boardroom heroes who persuade decision-makers to back new technologies, each episode dives into stories of ambition, innovation, and collaboration.

Join us to witness the evolution of technology across various sectors.

<div><p>Kevin McCurdy, Global Partner Lead, Consumer Goods, AWS, shows how Gen AI, trusted data, and risk-based guardrails turn experiments into repeatable CPG value. He highlights AWS and partner capabilities (Amazon Bedrock, SageMaker, secure integrations) with real wins such as demand forecasting, planogram automation, and Adastra’s Mark Anthony Group solution that scales assortment optimization and auto-generates seller scripts, plus quick-win assistants, cost controls, and an enterprise AI program with clear budgets, ownership, and accountability across product, employee, and customer use cases. </p></div><ul>
<li>What does it take to move from quick wins with Amazon Q to custom, domain-aware agents on Bedrock that scale across the enterprise? </li>
<li>When is “good enough” data enough to start, and how can AI assistants surface gaps while improving data quality over time? </li>
<li>Which operating model and risk-based guardrails help leaders control cost and compliance while accelerating adoption? </li>
</ul>

<p></p><p>Kevin McCurdy, Global Partner Lead, Consumer Goods, AWS, shows how Gen AI, trusted data, and risk-based guardrails turn experiments into repeatable CPG value. He highlights AWS and partner capabilities (Amazon Bedrock, SageMaker, secure integrations) with real wins such as demand forecasting, planogram automation, and Adastra’s Mark Anthony Group solution that scales assortment optimization and auto-generates seller scripts, plus quick-win assistants, cost controls, and an enterprise AI program with clear budgets, ownership, and accountability across product, employee, and customer use cases. </p><ul>
<li>What does it take to move from quick wins with Amazon Q to custom, domain-aware agents on Bedrock that scale across the enterprise? </li>
<li>When is “good enough” data enough to start, and how can AI assistants surface gaps while improving data quality over time? </li>
<li>Which operating model and risk-based guardrails help leaders control cost and compliance while accelerating adoption? </li>
</ul>

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80: "Helpful, not creepy: personalization that earns trust," says Kevin McCurdy, Global CPG Partner Lead, AWS

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