Unlike other high-risk AI applications, customer service AI can be deployed rapidly in enterprises. The existing infrastructure for escalating issues to human agents provides a natural, low-risk safety net, giving leaders confidence to go live.

To avoid failure, launch AI agents with high human control and low agency, such as suggesting actions to an operator. As the agent proves reliable and you collect performance data, you can gradually increase its autonomy. This phased approach minimizes risk and builds user trust.

To discover high-value AI use cases, reframe the problem. Instead of thinking about features, ask, "If my user had a human assistant for this workflow, what tasks would they delegate?" This simple question uncovers powerful opportunities where agents can perform valuable jobs, shifting focus from technology to user value.

Begin your AI journey with a broad, horizontal agent for a low-risk win. This builds confidence and organizational knowledge before you tackle more complex, high-stakes vertical agents for specific functions like sales or support, following a crawl-walk-run model.

Unlike competitors embracing AI, Airbnb is intentionally avoiding integration with generative AI trip planners like ChatGPT. The company is making a high-risk bet that its brand is strong enough to retain direct bookings, rather than becoming a background "data layer" in a user journey that starts on another platform.

The traditional SaaS method of asking customers what they want doesn't work for AI because customers can't imagine what's possible with the technology's "jagged" capabilities. Instead, teams must start with a deep, technology-first understanding of the models and then map that back to customer problems.

CEO Brian Chesky sees advertising as a multi-billion dollar opportunity but is intentionally holding off. Instead of replicating Google's legacy search ad model, he wants to first perfect an AI-driven search experience and then design a new advertising unit tailored for that conversational interface, ensuring it doesn't degrade user trust.

In the rush to adopt AI, teams are tempted to start with the technology and search for a problem. However, the most successful AI products still adhere to the fundamental principle of starting with user pain points, not the capabilities of the technology.

Prioritize using AI to support human agents internally. A co-pilot model equips agents with instant, accurate information, enabling them to resolve complex issues faster and provide a more natural, less-scripted customer experience.