Rethinking Customer Experience in the Age of AI

One company used AI to perfectly predict and pre-fill a customer's shopping cart. Users hated it.

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Why companies now compete on AI-driven customer experience

Companies are shifting their competitive focus from price and product to delivering superior customer experiences powered by AI.

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How eBay uses AI to simplify the seller experience

eBay's AI-powered tool simplifies the product listing process by auto-populating details from a photo, saving sellers time and improving listing quality.

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Addressing common client concerns about AI adoption

Many companies are eager to adopt AI for customer experience, but a primary technical concern is how to prevent hallucinations in chatbots.

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Innovating logistics and returns with AI at Picnic

Picnic uses AI to innovate its business model by optimizing its supply chain and offering a reverse logistics service for returning items from other retailers.

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Integrating AI expertise into product and business teams

Instead of creating siloed AI labs, companies are embedding AI specialists into cross-functional teams to ensure innovations solve real customer problems.

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Hiring analytical generalists for the age of AI

Companies are now prioritizing hiring critical and analytical thinkers over deep domain experts, as these generalist skills are more effective for applying AI.

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Navigating the practical impact of the EU AI Act

The EU AI Act has minimal direct impact on companies applying AI, leading to a call for collaborative and agile regulation that fosters trust and innovation.

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Creating personalized customer journeys with AI

AI enables deep personalization, from suggesting weekly meal plans based on health goals to allowing natural language search for products with specific attributes.

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The promise and pitfalls of implementing agentic AI

While agentic AI can automate entire processes like customer service and logistics, a key challenge is preventing performance from flattening without robust feedback mechanisms.

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Learning from common failures in AI projects

Successful AI implementation requires overcoming challenges like poor data quality, building a culture that accepts failure, and understanding unexpected user behavior.

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